The Z-Score for predicting bankruptcy was published in 1968 by Edward I. Altman, who was an assistant professor of finance at New York University at that time. It measures the financial health of a company based on a set of income and balance sheet values. The Altman Z-Score predicts the probability that a firm will go bankrupt within 2 years. In its initial test, the Altman Z-Score was found to be 72% accurate in predicting bankruptcy two years before the event. In a series of subsequent tests, the model was found to be approximately 80%–90% accurate in predicting bankruptcy one year before the event

Atman built the model by applying the statistical method of discriminant analysis to a dataset of publicly held manufacturers. Since then he has published new versions based on other datasets for private manufacturing (Z'-Score), non-manufacturing, service companies, and companies in emerging markets. (Z''-Score)

Please also note that the original dataset used was quite small and consisted of only 66 firms of which half filed for bankruptcy. All companies were manufacturers and small firms (total assets < $1m) were removed.

We currently support the original model so please take care to only use it to assess manufacturers.

How is it calculated?

The Z-score is calculated as follows:

The 5 components used in the calculation are:

X1, Working Capital/Total Assets (WC/TA)

"The working capital/total assets ratio...is a measure of the net liquid assets of the firm relative to the total capitalization...A firm experiencing consistent operating losses will have shrinking current assets in relation to total assets. "

X2, Retained Earnings/Total Assets (RE/TA)

"Retained earnings is the account which reports the total amount of reinvested earnings and/or losses of a firm over its entire life... This ratio discriminates young companies on purpose as the incidence of failure is much higher in a firm’s earlier years... It also measures the leverage of a firm. Those firms with high RE, relative to TA, have financed their assets through retention of profits and have not utilized as much debt."

X3, Earnings Before Interest and Taxes/Total Assets (EBIT/TA)

"This ratio is a measure of the true productivity of the firm’s assets, independent of any tax or leverage factors... insolvency in a bankrupt sense occurs when the total liabilities exceed a fair valuation of the firm’s assets with value determined by the earning power of the assets."

X4, Market Value of Equity/Book Value of Total Liabilities (MVE/TL)

"The measure shows how much the firm's assets can decline in value (measured by market value of equity plus debt) before the liabilities exceed the assets and the firm becomes insolvent. "

X5, Sales/Total Assets (S/TA)

"The capital-turnover ratio is a standard financial ratio illustrating the sales generating ability of the firm’s assets. It is one measure of management’s capacity in dealing with competitive conditions. "

Source of the quotes above: Edward I. Altman - Predicting Financial distress of companies: revisiting the Z-Score and Zeta models.

Companies that are caught manipulating their earnings tend to see their stocks plummet in value. Is there a way to detect earnings manipulation only by looking at the financial statements?

The M-score

Professor Messoud D. Beneish studied the characteristics of earnings manipulators and used this to create a model that is pretty good at detecting this type of companies. In his most recent paper, he demonstrates that the model correctly identified a large majority (71%) of the most famous accounting fraud cases that surfaced after the model's estimation period in advance of public disclosure. The model attained widespread recognition after a group of MBA students posted the earliest warning about Enron's accounting manipulation using the Beneish model a full year before the first analyst reports.

While very few companies get indicted for accounting fraud, the M-score helps predict a firm's prospects.

To the extent that the pricing implications of these accounting-based indicators are not fully transparent to investors, firms that “look like” past earnings manipulators will also earn lower future returns.

Beneish initially described his M-score as a detector for companies that manipulate earnings. (click here to read his original paper.). In his more recent work, he reveals that the M-score is also an excellent predictor of future stock returns.

How do you calculate the M-score?

The M-score is based on eight variables, of which some are designed to capture the effects of manipulation while others show preconditions that may prompt firms to engage in such activity. While Beneish takes data from the fiscal years, we use the last trailing twelve-month (TTM) numbers as the current year (year t). For year t-1, we take the TTM results for the 12 months before year t.

1. Days Sales in Receivables Index (DSRI): The ratio of days sales in receivables during the last year (t) compared to the year before (t-1). A disproportionate increase in receivables relative to sales may be suggestive of revenue inflation.
   $$\mathrm{Days\; Sales\; In\; Receivables\; Index}=\frac{\frac{\mathrm{Net\; Receivables}}{\mathrm{Net\; Sales\; or\; Revenues}}}{\frac{\mathrm{Net\; Receivables\; y-1}}{\mathrm{Net\; Sales\; Or\; Revenues\; y-1}}}$$
2. Gross Margin Index (GMI): A value greater than 1 indicates that margins have deteriorated. This signals poor prospects and might lead to earnings manipulation.
   $$\mathrm{Gross\; Margin\; Index}=\frac{\mathrm{Gross\; Margin\; y-1}}{\mathrm{Gross\; Margin}}$$
3. Asset Quality Index (AQI): Asset Quality is the ratio of non-current assets other than plan, property, and equipment as a proportion of total assets. An AQI greater than 1 indicates that a firm has potentially increased its involvement in cost deferral.
   $$\mathrm{Asset\; Quality\; Index}=\frac{1-\left(\frac{\mathrm{Current\; Assets}+\mathrm{Net\; Property,\; Plant\; \&\; Equipment}}{\mathrm{Total\; Assets}}\right)}{1-\left(\frac{\mathrm{Current\; Assets\; y-1}+\mathrm{Net\; Property,\; Plant\; \&\; Equipment\; y-1}}{\mathrm{Total\; Assets\; y-1}}\right)}$$
4. Sales Growth Index (SGI): Growth does not imply manipulation, but growth firms are more likely to commit fraud because their financial position and capital needs put pressure on managers to achieve earnings targets. In addition, controls and reporting tend to lag behind operations in periods of high growth. Any perception of decelerating growth can significantly impact the value of the stock and be very costly to manage. A value greater than one increases the probability of earnings manipulation.
   $$\mathrm{Sales\; Growth\; Index}=\frac{\mathrm{Net\; Sales\; or\; Revenues}}{\mathrm{Net\; Sales\; Or\; Revenues\; y-1}}$$
5. Depreciation Index (DEPI): The rate of depreciation in year t-1 / year t. The rate of depreciation is equal to depreciation / (depreciation + net property, plant & equipment). If this value is greater than 1, this means that the rate at which assets are depreciated has slowed down. Either management revised the estimates of assets useful lives upwards or adopted a new income method.
   $$\mathrm{Depreciation\; Index}=\frac{\frac{\mathrm{Depreciation\; Depletion\; Amortization\; y-1}}{\mathrm{Depreciation\; Depletion\; Amortization\; y-1}+\mathrm{Net\; Property,\; Plant\; \&\; Equipment\; y-1}}}{\frac{\mathrm{Depreciation\; Depletion\; Amortization}}{\mathrm{Depreciation\; Depletion\; Amortization}+\mathrm{Net\; Property,\; Plant\; \&\; Equipment}}}$$
6. Sales General and Administrative Expenses Index (SGAI): The ratio of SGA to sales in year t / year t-1. Analysts would interpret a disproportionate increase in sales as a negative signal about the firm's prospects. Beneish expects a positive relation between SGAI and the probability of manipulation.
   $$\mathrm{SGA\; Index}=\frac{\frac{\mathrm{SGA\; Expenses}}{\mathrm{Net\; Sales\; or\; Revenues}}}{\frac{\mathrm{SGA\; Expenses\; y-1}}{\mathrm{Net\; Sales\; Or\; Revenues\; y-1}}}$$
7. Leverage Index (LVGI): The ratio of total debt to total assets in year t relative to year t-1. A value greater than 1 indicates an increase in leverage.
   $$\mathrm{Leverage\; Index}=\frac{\frac{(\mathrm{Long\; Term\; Debt}+\mathrm{Current\; Liabilities})}{\mathrm{Total\; Assets}}}{\frac{(\mathrm{Long\; Term\; Debt\; y-1}+\mathrm{Current\; Liabilities\; y-1})}{\mathrm{Total\; Assets\; y-1}}}$$
8. Total Accruals to Total Assets (TATA): Total accruals is calculated as the change in working capital accounts other than cash less depreciation. This ratio proxies the extent to which cash underlies reported earnings. Higher positive accruals (less cash) indicates a higher likelihood of earnings manipulation.
   $$\mathrm{Total\; Accruals\; to\; Total\; Assets}=\frac{(\mathrm{Net\; Income}-\mathrm{Cash\; Flow\; from\; Operations}-\mathrm{Cash\; Flow\; from\; Investments})}{\mathrm{Total\; Assets}}$$

Let's take an example we found in the stock screener: Microstrategy

This company has been selling analytics software for more than 20 years and has 2,000 employees. Recently the company decided to dramatically increase its debt level to place a significant bet on bitcoin. At present, the company holds $92,000 bitcoins, with a current market value of 3.7bn. Its market cap is 4,6bn.

After six years of declining revenue, the company seems to be making a turnaround. It reported that its Q1 revenue was up 10.3%. EBIT increased fro $-0.1 to $+10.9m.

The Beneish M-scorecard

As you can see, the total score is 0.19, which is above the -1.78 threshold. According to the formula, this makes it a suspect of earnings manipulation. Other services like gurufocus give it an even worse score of 8.35, but this is incorrect and we will explain why.

We can also see that it's suspect on four signals: asset quality, depreciation rate, leverage, and accruals. Let's dive into a bit more detail.

Asset quality index

As you can see in the screenshot below, the company significantly increased its non-current assets. This is easy to explain by the investment in bitcoins. As a result of this transaction, this ratio went up from 0.03 to 0.85. But here's where companies like gurufocus make a mistake. The ratio last year was so low that any increase would have a significant impact. And because the M-score is just the weighted sum of these eight factors, it can have an important overall impact.

To solve this issue, Beneish winsorizes percentile 1 and 99. This means that he replaces this score with the median score of percentile 2 and 98. As a result of this, we use 3.28 instead of 27.92. If we had used 27.92, the M-score would have been close to what gurufocus calculated.

The following suspect ratio is the rate of depreciation, which seems to have slowed down by 13%.

As the company financed its bitcoin purchases almost exclusively with debt, it increased its leverage significantly. So far, this bet has paid off nicely, but the bitcoin has plunged 40% since mid-April. Investors fear that regulators worldwide will crack down on these virtual assets, and Elon Musk voiced concerns about the adverse effects on the environment.

Finally, the accrual rate compared to assets was 78%. This means that a significant share of the company's income is not supported by cash flow.

M-score as secondary ratio

Our members typically use the Beneish score to filter the results of other screens. They typically use it when scanning new markets for value stocks since they're unfamiliar with the companies. Since a share of the companies discovered manipulating earnings would eventually see their stocks plummet in value, it provides extra security to filter these potential manipulators out of the screener.

Valuesignals offers the unique capability to combine any number of factors into a combined factor. Joel Greenblatt combined Earnings Yield and ROC into the Magic Formula. Our team added ROC 5Y and Book-to-Market to create the ERP5 ranking. But what if you wanted, for instance to momentum into the mix?

Custom factors allows you to combine your favorite factors and make this factor available in the grid.

The ERP5 score was designed by the MFIE Capital team. It combines the Greenblatt Magic formula with ideas developed by Graham & Dodd, who advocated the use of 5 to 10 year smoothed earnings to cover full economic business cycles and dampen the effect of expansions and recessions. Finally it adds the book-to-market ratio into the mix.

Each company gets ranked according to 4 ratios:

1. Earnings Yield
2. ROC
3. 5 year ROC
4. Book-to-Market

Similar to the Greenblatt Magic formula, companies are ranked on each individual factor and the sum of this becomes the ERP5 score. The ERP5 template screen will sort companies according to this factor. If one of the ratios is missing, the company will get an ERP5 score of 99999 which will put it at the back of the list.

More details about the ERP5 score and screener can be found here.

New! The ERP5 score is now fully dynamic and is calculated on a filtered stock universe using the filters specified in the Filter Menu. The filters taken into account are: countries, markets, industries, market cap, trading value, results age and currency.

Magic Formula (MF) score. This factor was introduced by Joel Greenblatt in his bestseller: 'The little book that beats the market'. In this book, he explained that in order to get above-average returns, you should buy companies with above-average return on capital at below-average prices. To identify these companies, we rank the stock universe based on 2 factors:

1. Earnings Yield: how much a business earns compared to its purchase price.
2. ROC: how much a business earns compared to the capital needed to conduct the business.

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We first rank these companies based on each of these ratios individually. Then we sum up the individual scores and rank the combined score.

$$\mathrm{Magic\; Formula}=\mathrm{Rank}\left(\mathrm{Rank}\right(\mathrm{Earnings\; Yield})+(\mathrm{Rank}\left(\mathrm{ROIC}\right))$$

The result of this calculation is the Magic Formula score. If the Earnings Yield or ROC cannot be calculated due to missing data, the company gets a score of 99999. The companies with the lowest MF score are the ones you should invest in according to Greenblatt's theory.

Greenblatt adds a few other conditions such as removing certain industries such as utilities and financials. We made it easy for you to reproduce this screen by adding it to our template screens. You can find more details about this screen here.

New! The Magic Formula is now fully dynamic and is calculated on a filtered stock universe using the filters specified in the Filter Menu. The filters taken into account are: countries, markets, industries, market cap, trading value, results age and currency.

Value Composite One (VC1) is a composite factor introduced by James O'Shaughnessy in the 4th edition of his book 'What Works on Wall Street'. This factor combines balance sheet and cash flow factors into what he calls a "pure-play" combined value factor. It's made up of the following 5 factors.

1. Price-to-Book
2. Price-to-Earnings
3. Price-to-Sales
4. EBITDA/EV
5. Price-to-Cash flow

Instead of ranking stocks on each ratio, stocks are grouped into 100 groups equal groups (percentiles), from 1 to 100. This is done on each ratio. If a company is in group 1, that means it's in the best 1%. If the ratio is missing, a neutral score of 50 is assigned. The scores for each ratio are summed up to get a combined score. Based on this value, companies are again grouped from 1 to 100.A company with a VC1 of 1 is in the 1% cheapest companies according to the combination of those 5 factors. Companies with a VC1 of 100 are the most expensive.

The scorecard also displays variants of the VC1 where the score is calculated for the selected company compared to peer companies in the same industry, industry group, or sector.

Please note that we use Book-to-Market instead of P/B since it allows a more accurate sorting compared to P/B. Stocks with a high B/M show up at the top of the list, stocks with negative B/M are at the bottom of the list. For the same reason, we use Earnings-to-Price instead of Price-to-Earnings and Cash flow-to-price instead of Price-to-Cash flow.

Also important is that we always make sure that companies with the same score get added to the same percentile. For stock universes where the number of stocks is less than 100, we make sure that the stocks are still allocated to percentiles from 0 to 100 instead of 0 to the total number of stocks. This is particularly relevant for the industry, industry group, or sector variants where if additional filters are used, the number of stocks often drops below 100.

New! The VC1 score is now fully dynamic and is calculated on a filtered stock universe using the filters specified in the Filter Menu. The filters taken into account are: countries, markets, industries, market cap, trading value, results age and currency.

Value Composite Two (VC2) is an adaptation of the VC1 factor described above. O'Shaughnessy found that the addition of shareholder yield can improve the results of the pure play Value Factor One. This composite is the combination of the following factors:

1. Price-to-Book
2. Price-to-Earnings
3. Price-to-Sales
4. EBITDA/EV
5. Price-to-Cash flow
6. Shareholder Yield

As with the VC1, companies are put into groups from 1 to 100 for each ratio and the individual scores are summed up. This total score is then put into groups again from 1 to 100. 1 is cheap, 100 is expensive.

O'Shaughnessy uses the VC2 factor in his trended value screen, which is described in more detail here.

The scorecard also displays variants of the VC2 where the score is calculated for the selected company compared to peer companies in the same industry, industry group, or sector.

Please note that we use Book-to-Market instead of P/B since it allows a more accurate sorting compared to P/B. Stocks with a high B/M show up at the top of the list, stocks with negative B/M are at the bottom of the list. For the same reason, we use Earnings-to-Price instead of Price-to-Earnings and Cash flow-to-price instead of Price-to-Cash flow.

Also important is that we always make sure that companies with the same score get added to the same percentile. For stock universes where the number of stocks is less than 100, we make sure that the stocks are still allocated to percentiles from 0 to 100 instead of 0 to the total number of stocks. This is particularly relevant for the industry, industry group, or sector variants where if additional filters are used, the number of stocks often drops below 100.

New! The VC2 score is now fully dynamic and is calculated on a filtered stock universe using the filters specified in the Filter Menu. The filters taken into account are: countries, markets, industries, market cap, trading value, results age and currency.

Value Composite Three (VC3) is another adaptation of O'Shaughnessy's value composite but here he combines the factors used in VC1 with buyback yield. This factor is interesting for investors who're looking for stocks with the best value characteristics, but are indifferent to whether these companies pay a dividend.

VC3 is the combination of the following factors:

1. Price-to-Book
2. Price-to-Earnings
3. Price-to-Sales
4. EBITDA/EV
5. Price-to-Cash flow
6. Buyback Yield

As with the VC1 and VC2, companies are put into groups from 1 to 100 for each ratio and the individual scores are summed up. This total score is then put into groups again from 1 to 100. 1 is cheap, 100 is expensive.

The scorecard also displays variants of the VC3 where the score is calculated for the selected company compared to peer companies in the same industry, industry group, or sector.

Please note that we use Book-to-Market instead of P/B since it allows a more accurate sorting compared to P/B. Stocks with a high B/M show up at the top of the list, stocks with negative B/M are at the bottom of the list. For the same reason, we use Earnings-to-Price instead of Price-to-Earnings and Cash flow-to-price instead of Price-to-cash-flow.

Also important is that we always make sure that companies with the same score get added to the same percentile. For stock universes where the number of stocks is less than 100, we make sure that the stocks are still allocated to percentiles from 0 to 100 instead of 0 to the total number of stocks. This is particularly relevant for the industry, industry group or sector variants where if additional filters are used, the number of stocks often drops below 100.

New! The VC3 score is now fully dynamic and is calculated on a filtered stock universe using the filters specified in the Filter Menu. The filters taken into account are: countries, markets, industries, market cap, trading value, results age and currency.

The F-Score was designed by Joseph Piotroski, a professor in accounting at Stanford University, and is used to identify companies for which the prospects are improving. It's the sum of 9 binary scores based on profitability, funding and operational efficiency. It looks at simple things such as: 'has the company made more profit compared to last year?' (+1 point) but also: 'is the company cooking the books by adjusting accruals?' (0 points). By using 9 points he was able to get enough signals to determine whether the company is really improving or not.

The f-score is the sum of 9 binary scores in 3 categories:

Profitability

1. ROA - Return on Assets: Net income before extraordinary items divided by total assets at the beginning of the year. 1 if positive, 0 if negative.
2. CFO - Cash Flow Return on Assets: Net cash flow from operating activities (operating cash flow) divided by total assets at the beginning of the year. 1 if positive, 0 if negative.
3. ΔROA - Change in Return on Assets: Compare return on assets to last year. 1 if it's higher, 0 if it's lower.
4. ACCRUAL - Quality of earnings (accrual): Compare cash flow return on assets to return on assets. 1 if CFO > ROA, 0 if CFO < ROA.

Funding

1. ΔLEVER - Change in gearing or leverage: Compare the gearing (long-term debt divided by average total assets) to the gearing last year. 1 if gearing is lower, 0 if it's higher.
2. ΔLIQUID - Change in working capital: Compare the current ratio (current assets divided by current liabilities) to the current ratio last year. A value higher than 1 indicates an increasing ability to pay off short term debt.
3. EQ_OFFER - Change in outstanding shares: The number of shares outstanding compared to last year. 0 if the number increased, otherwise 1.

Efficiency

1. Δ_MARGIN - Change in Gross Margin: Current gross margin compared to last year. 1 if higher, 0 if lower
2. ΔTURN - Change in asset turnover: Compare asset turnover (total sales divided by total assets at the beginning of the year) to last year's asset turnover ratio. 1 if higher, 0 if lower.

To calculate this year's number we use the last trailing 12-month (TTM) number available. For last year we use the same number 1 year ago.

Piotroski used his F-Score to filter out the 'dogs with poor prospects' from the lowest price-to-book companies. This template screen available in our screener is discussed in more details here.

The Piotroski F-Score is available in the screener and we also provide a bullet graph and a comprehensive report in the scorecard user guide. This includes a detailed report where you can see all underlying values and how the Piotroski F-Score and the 9 signals evolved during the last 10 reporting periods. Read more about this in the scorecard manual.

The following two factors are not really single factors, but really a combination of several parameters. However, we wanted to include them under the single factor tests as we also wanted to combine them with other factors to see if their market outperformance could be improved even further.

The ERP5 rank is a screen designed by MFIE Capital that uses the following ratios to identify good companies that are trading at undervalued prices:

• Return on Invested Capital (ROIC) - EBIT / (Net Working Capital + Net Fixed Assets).
• Earning Yield - EBIT / Enterprise Value.
• Price-to-Book Value - Market Capitalization / Book Value.
• 5Y Trailing ROIC - five year average EBIT / (Net Working Capital + Net Fixed Assets).

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The results show that the ERP5 rank is a factor that works very well when applied to small cap companies, with the second best results of all single factors we tested. Q1 results are substantially better than Q5. However, the results for small cap companies are not completely linear.

What is worth noting is that the Q1 results for the ERP5 for all size companies are higher than that of the MF rank. The ERP5 screen is particularly effective in identifying market beating small companies. It is also a very consistent factor, beating the market 83% of the time for small and medium-size companies, and 67% of the time for large companies.

The Magic Formula was developed by Joel Greenblatt in his book, ‘The Little Book That Still Beats the Market’. The basic idea behind the rank is to identify good businesses that are selling at attractive prices. This is done through the use of two ratios:

• Return on Invested Capital (ROIC) - which is calculated as EBIT / (Net Working Capital + Net Fixed Assets)
• Earning Yield - which is calculated as EBIT / Enterprise Value.

The rank then combines these two ratios to give you a list of companies with good businesses that are trading at an attractive price.

Kindly note that we tested the Magic Formula based on our interpretation of it after reading Joel Greenblatt’s book mentioned above. Neither Mr Greenblatt nor the website (magicformulainvesting.com) have endorsed this study or have had anything to do with it, or recommended any of the companies included in our back tests. We also made use of our own database and did not have access to Mr Greenblatt's

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As you can see, the Magic Formula is a strong factor that leads to substantial market outperformance. Q1 performs better than Q5, and the results are completely linear. It is, however, not that consistent - outperforming the market 50% of the time for small companies and 58% of the time for mid and large companies.

The country in which the stock market is based on which the company has its primary listing. We use this instead of the company hq as companies might be located in certain countries for tax or other reasons. This way we also ensure that Basic and Professional subscribers get access to all stocks that have their primary listing on a stock exchange in the countries covered by their subscription.

Use the Country Filter in the Filter Menu to include/exclude countries at the source.

For each stock we provide the industry, industry group and sector it belongs to. We use the Global Industry Classification Standard taxonomy. For an overview click on this link.

You can easily include or exclude certain categories by using the Industry Filter in the Filter Menu.

An International Securities Identification Number (ISIN) uniquely identifies a security.

The Market Identification Code (MIC) uniquely identifies the stock market on which the stock is listed

All our ratios - except the 5Y versions - are based on the trailing twelve months data. This is a representation of the financial performance for the 12 months before a certain end date. The Period end date displays the end date of this period.

This column shows how 'fresh' the data is and can be used to filter out stale data. A company could, for instance, become delisted and as a result, it will no longer report financial performance. By setting a column filter to exclude companies with a period end date before a certain date, you ensure that these companies are not included in your list. Another way to do this is to use the 'Results Age' in the filter menu, which comes with preset periods. (6 months, 9 months, etc...)

The closing price of the selected stock. Stock prices are updated every day, typically 1 hour after market close.

As an alternative to the PEG ratio, this ratio compares growth to earnings, while also taking the dividends into account. Peter Lynch mentioned this in his book One up on Wall Street.

The earnings per share (EPS) growth is the % change in EPS over the last 12 months. It gives a picture of the rate at which a company has grown in profitability.

We calculate EPS growth using the following formula:

When the EPS of last year is negative, the growth is not calculated as growth from a negative basis cannot be reliably calculated.

This is the inverted version of the dividend adjusted PEG ratio. Peter Lynch uses this version and considers a value of 1 to be poor, but what you're really looking for is a 2 or better.

This ratio is the opposite of the PEG ratio and allows for a better distribution. Growth companies with an inverted PEG above 2 are considered a bargain while companies with a ratio below 0.5 are considered expensive. By sorting stocks in descending order, bargains show at the top while expensive companies or companies with negative earnings growth will show up at the bottom of the list.

The formula is as follows:

Peter Lynch, one of the greatest fund managers of all time, uses PEG as a 'number worth noticing'.

The P/E ratio of any company that's fairly priced will equal its growth rate...In general, a P/E that's half the growth rate is very positive, and one that's twice the growth rate is very negative. We use this measure all the time when analyzing stocks for the mutual funds.

PEG ratio can be calculated based on past earnings growth or future expected growth rate, but Peter Lynch has the following advice:

If your broker can't give the company's growth rate, you can figure it out for yourself by taking the annual earnings from Value Line or an S&P report and calculating the percent increase from one year to the next. That way you'll end up with another measure of whether a stock is or is not too pricey. As to the all-important future growth rate, your guess is as good as mine

We calculate PEG as follows:

A PEG ratio of less than 0.5 is considered attractive, while ratios above 2 are unattractive.

It should be noted that PEG cannot be used on all companies. Cyclical companies for instance will have a low PEG ratio but buying these stocks at a low point is a proven method for losing half your money in a short period of time. Conversely, companies who had a few tough years will show a high PEG ratio but business could soon pick up.

This measure shows which amount has been traded on average during the last month. You can use the column filter to remove(include) companies below(above) a certain liquidity level. Illiquid shares cannot easily be sold due to a lack of investors willing to purchase them. This will typically lead to large discrepancies between the asking price and the bidding price.

Formula:

$$\mathrm{Average\; Trading\; Value}=\mathrm{Average\; last\; 30\; days}$$

( Share Price * Volume )

The Average Trading Value is converted to the currency selected in the Currency Filter in the Filter Menu. The prior day exchange rate is used to make the conversion. By converting to a common currency, we ensure that you can compare companies reporting in different countries.

If you wish to filter out illiquid stocks from the outset, use the Minimum Trading Value filter available in the Filter Menu.

Earnings before interest and taxes (EBIT) is a measure of a firm's profit that includes all expenses except interest payments and income tax. EBIT is used in different ratios such as the Earnings Yield, the Greenblatt Magic Formula and the Altman Z-Score. Greenblatt uses EBIT instead of reported earnings because this allowed him to compare different companies without the distortions arising from differences in tax rates and debt levels. EBIT is based on the latest 12-month period, as described in Greenblatt's little book.

The EBIT is converted to the currency selected in the Currency Filter in the Filter Menu. The prior day exchange rate is used to make the conversion. By converting to a common currency, we ensure that you can compare companies reporting in different countries.

Enterprise Value (EV) is an economic measure reflecting the market value of a company. It is the sum of claims of all claimants: creditors (secured and unsecured) and equity holders (preferred and common) Think of it as the theoretical takeover price if the company would get bought.

It's calculated using the following formula:

Please note that cash & short-term investments are deducted. The reason for this is that (1) cash is considered a non-operating asset and (2) cash is already implicitly accounted for within equity value.

The Enterprise Value is converted to the currency selected in the Currency Filter in the Filter Menu. The prior day exchange rate is used to make the conversion. By converting to a common currency, we ensure that you can compare companies reporting in different countries.

The amount of cash in excess of what the company needs to run its day-to-day operations.

Formula:

The Excess Cash is converted to the currency selected in the Currency Filter in the Filter Menu. The prior day exchange rate is used to make the conversion. By converting to a common currency, we ensure that you can compare companies reporting in different countries.

Market capitalization is the value of all of a company's outstanding shares. It is often used to determine the size of a company.

Formula:

The Market Cap is converted to the currency selected in the Currency Filter in the Filter Menu. The prior day exchange rate is used to make the conversion. By converting to a common currency, we ensure that you can compare companies reporting in different countries.

If you wish to filter out companies smaller or bigger than a certain market capitalization, use the Market Cap filter available in the Filter Menu.

This measure shows a company's overall debt situation by taking the total debt and deducting cash and equivalent assets. Net debt is used in the Net Debt to Market Cap ratio.

The Net Debt is converted to the currency selected in the Currency Filter in the Filter Menu. The prior day exchange rate is used to make the conversion. By converting to a common currency, we ensure that you can compare companies reporting in different countries.

Net Fixed Assets is used in the ROIC calculation used in the Greenblatt Magic Formula. It calculates the amount of cash needed to purchase fixed assets necessary to conduct its business, such as real estate, plant and equipment. Greenblatt removes intangible assets, and specifically goodwill, which usually arises from an acquisition of a company. These are historical costs and should this does not need to be constantly replaced.

The amount of money it has available to spend on its day-to-day business operations, such as paying short-term bills and buying inventory. We use the definition of Joel Greenblatt and exclude excess cash as this is not needed to conduct the business. We also exclude short-term interest-bearing debt from the current liabilities, as Greenblatt only looks at payables for which the company does not need to pay interest.

Last month's winners are next month's losers. In his paper Evidence of predictable behavior of security returns, Narasimhan Jegadeesh reported a strong negative correlation between returns in subsequent months. He examined stock returns in the period 1934-1987 and found that prior month winners have an average next month return of -1.38%, while the prior month's losers have an average (next month) return of 1.11%. The gap is 2.49%. The author concludes:

The results documented here reliably reject the hypothesis that stock prices follow random walks. Predictability of stock returns can be attributed either to market inefficiency or to systematic changes in expected stock returns.

            <p>Formula:</p>
                

* The share price is adjusted for stock splits, cash dividends, right offerings and spin-offs.

This is an intermediate-term momentum measure similar to the 3-month and 6-month versions. You can find a reference to an academic paper in Price Index 6M. According to this study, the most successful strategy selects stocks based on their 12-month return (= Price Index 1Y) and holds the portfolio for 3 months.

* The share price is adjusted for stock splits, cash dividends, right offerings and spin-offs.

Stocks that showed the highest increase during the last 12 months continue to move upwards in the following months. At the same time, stocks that showed the highest increase during a month tend to be part of the losers during the next month. This factor improves the predictive power of the Price Index 1Y by ignoring the final month.

This is an intermediate-term momentum measure similar to the 6 month and 1 year version. You can find a reference to an academic paper in Price Index 6M. According to this study, the 6 month and 1 year versions have higher returns.

* The share price is adjusted for stock splits, cash dividends, right offerings and spin-offs.

The biggest losers will eventually become winners and vice versa. Werner De Bondt and Richard Thaler tested this hypothesis and found it to be statistically significant. To calculate the biggest losers (winners), they formed portfolios with stocks that showed the biggest declines (increases) in returns during the past 5 years. They found that the loser outperformed the winners by 24.6% over the next 3 years.

We calculate the 5 year price index using the following formula:

* The share price is adjusted for stock splits, cash dividends, right offerings and spin-offs.

We use the 5Y price index in the Return reversal with Piotroski screen.

As opposed to the short term momentum (Price Index 1M) and long term momentum (Price Index 5Y), the intermediate-term momentum (3M, 6M and 1Y) show a continuation in the following months. If a stock has done relatively well in the past, it will continue to do well in the future.

In a paper published in 1993, Returns to buying winners and selling losers: implications for stock market efficiency, authors Narasimhan Jegadeesh and Sheridan Titman found that strategies which buy stocks that have performed well in the past and sell stocks that have performed poorly in the past generate significant positive returns over 3- to 12-month holding periods. They also found that the abnormal returns were not long-lasting.

The stocks included in the relative strength portfolios experience negative abnormal returns starting around 12 months after the formation date and continuing up to the thirty-first month. For example, the portfolio formed on the basis of returns realized in the past 6 months generates an average cumulative return of 9.5% over the next 12 months but loses more than half of this return in the following 24 months.

In their tests, running from 1965 to 1989, selecting stocks based on their 6 month PI and holding them for 6 months realized a return of 12.01% per year on average.

We use the following formula to calculate the 6-month price index:

* The share price is adjusted for stock splits, cash dividends, right offerings and spin-offs.

Stocks that showed the highest increase during the last 6 months continue to move upwards in the following months. At the same time, stocks that showed the highest increase during a month tend to be part of the losers during the next month. This factor improves the predictive power of the Price Index 6 months by ignoring the final month.

This factor measures the proximity of a stock to its 52-week high or 52-week low. The 52 weeks price range is calculated as:

$$\mathrm{Price\; Range}=\frac{(\mathrm{current\; price}-\mathrm{52\; week\; low})}{(\mathrm{52\; week\; high}-\mathrm{52\; week\; low})}$$

Share buybacks have become very popular since the 1990s. When a company pays out a dividend to its shareholders the final amount that the shareholder receives is significantly less compared to the money paid out, due to taxes. If you invest abroad there are taxes due in the country where the company is listed, ad in the country of residence. Double tax agreements exist but it's not always easy to claim these taxes back.

A much more tax-efficient way of paying out to shareholders is for the company to repurchase and destroy shares. If a company has 100 shares and buys back 10%, the shareholder with 10 shares effectively owns 11,1% of the company after this operation. This means that he's entitled to a larger share of future earnings and distributions to the shareholders.

A company buying back shares is a signal that the company's management believes the shares are trading at a discount compared to fair value.

Buyback Yield is calculated as follows:

Cash flow on total assets is an efficiency ratio that rates cash flows to the company assets without being affected by income recognition or income measurements. CFO is one of the four variables that J. Piotroski uses to measure performance.

This ratio is calculated by dividing cash flows from operations by the average total assets.

Investors looking for an income stream from their portfolio look for stocks that distribute a relatively high dividend compared to the value of the shares. Dividend yield also provides one of the most reliable pictures of a company's performance and is tangible proof of excess free cash flow.

Dividend Yield is calculated as follows:

This factor was introduced by Richard Tortoriello, a senior quantitative analyst for S&P Capital IQ. He authored a book on quantitative analysis: Quantitative Strategies for Achieving Alpha (2009, McGraw Hill). In this book, he identified the External Financing Ratio as a factor that is very good at predicting investment underperformance.

Formula:

Gross margin is the difference between revenue and cost of goods sold divided by revenue, expressed as a percentage. The gross margin represents the percentage of total sales that the company retains after deducting the direct costs associated with producing the goods.

More than 50 years ago, Charlie told me it was far better to buy a wonderful business at a fair price than to buy a fair business at a wonderful price. Despite the compelling logic of his position, I have sometimes reverted to my old habit of bargain-hunting, with results ranging from tolerable to terrible.

Robert Novy-Marx, a professor at the University of Rochester, discovered that gross profitability - a quality factor - has as much power predicting stock returns as traditional value metrics. He found that while other quality measures had some predictive power, especially on small caps and in conjunction with value measures, gross profitability generates significant excess returns as a stand-alone strategy, especially on large-cap stocks.

Gross Profitability is calculated as follows:

Novy-Marx's key insight was that you don't need to go further down the income statement as these numbers may get manipulated with accounting tricks. To identify really profitable firms, one should look at the top line, not the bottom line.

In one of his papers, Novy-Marx compares gross profitability to the other most famous strategies such as Greenblatt magic formula, Piortoski F-Score, etc. You can read more about it here. You can also read an interview with Mr Novy-Marx here.

This ratio gives a sense of how much debt a company has relative to its market value. Companies with high debt levels compared to their peers can be volatile. We calculate it as follows:

Percent Accruals was presented by University of Michigan academics Hafzalla, Lundholm and Van Winkle. In their paper, they showed that by building a model based on accruals scaled by earnings instead of by total assets (Sloan ratio), they got much higher returns.

Another interesting finding was that the lowest decile of percent accruals was composed of different firms which were 3 times larger and much better performing than the lowest decile of traditional accruals, based on either operating or total accruals.

Formula:

Return on Assets (ROA) shows how efficient management is at using its assets to generate profit. ROA can vary substantially between industries and should only be used to compare similar companies. It's calculated as follows:

Note: Net Income includes extraordinary items. (see ROE)

Return On Capital (ROC) measures a company's efficiency at allocating the capital under its control to profitable investments. The ROC measure gives a sense of how well a company is using its money to generate returns. Comparing a company's ROC with its cost of capital (WACC) reveals whether invested capital was used effectively.

We calculate the ROC as defined in Joel Greenblatt's little book that beats the market. Instead of comparing EBIT to total assets, we compare it to the cost of the assets used to produce those earnings (tangible capital employed).

Formula:

Click on the links below to navigate to the components of this formula:

• Operating Income, i.e. EBIT.
• Net Working Capital: the capital a business needs to fund its receivables and inventory.
• Net Fixed Assets: the capital needed to fund the purchase of fixed assets necessary to conduct its business.

High Return on Equity (ROE) characterize growth companies. It measures the company's profitable, i.e. how much net income it is able to generate on the money its shareholders invested.

Formula:

Please note that we don't remove the extraordinary items from the net income. It's very rare that transactions meet the requirements to be presented as an extraordinary item. For this reason, the concept extraordinary items has been removed from GAAP (starting from Dec 15, 2015). For more information, click here.

For similar reasons as for the Earnings Yield 5Y, this ratio smooths the ROICs of the last 5 years to smooth out business and economic cycle, as well as price fluctuations. It's calculated as follows:

Shareholder Yield shows how much money a company is paying out to its shareholders through a combination of dividends and share repurchases to reduce the number of shares. Dividends are money in the shareholders pocket and when earnings remain constant, share reduction results in increased earnings per share and potentially a higher future dividend yield.

Shareholder Yield is calculated as follows:

In their 2008 paper, professors Cooper, Gulen and Schill provided evidence that a firm's assets growth rates are strong predictors of future abnormal returns.

The findings suggest that corporate events associated with asset expansion (i.e., acquisitions, public equity offerings, public debt offerings, and bank loan initiations) tend to be followed by periods of abnormally low returns, whereas events associated with asset contraction (i.e., spin-offs, share repurchases, debt prepayments, and dividend initiations) tend to be followed by periods of abnormally high returns.

In a study on US data during the period 1967-2007, they find that:

• A hedge portfolio rebalanced annually that is long (short) the stocks of companies with the lowest (highest) percentage growth in total assets over the previous 12 months generates an average annual return of 22%.
• This asset growth effect is stronger for small capitalization stocks, but is still substantial for large capitalization stocks.
• The effect is strongest in the month of January.
• Asset growth rate retains large explanatory power for future stock returns after accounting for firm size, book-to-market ratio and momentum. In fact the asset growth effect is at least as powerful in explaining returns as these other widely used factors.

We calculate asset growth as follows:

Read the full paper here.

With this factor we wanted to test if the amount of debt a company had on its balance sheet had any impact on its stock price over the following 12-months. To do this we used the net debt (long term debt minus excess cash) to market value ratio.

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The results above show that the market rewards companies that take risks and punishes those that are too conservative. Companies with high cash balances and thus low debt to market value ratios (Q1) underperform those with less cash and a high amount of debt (on average).

This was most extreme with mid-sized companies where returns are linear, and highly leveraged companies outperformed companies with low amounts of leverage by over 140%. But overall the results were mixed, showing the net debt-to-market value ratio as a weak factor for achieving market outperformance.

Joseph Piotroski is an associate professor of accounting at the Stanford University Graduate School of Business. He developed the F-score in 2000 while at the University of Chicago. Piotroski recognized that, although it has long been shown that value stocks (or high book-to-market firms as he calls them) have strong returns as a group, there is nevertheless a very wide variability in terms of the returns of these stocks, with most of them performing worse than the market.

In his research paper called ‘Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers’, he noted:

Embedded in that mix of companies, you have some that are just stellar. Their performance turns around. People become optimistic about the stock and it really takes off [but] half of the firms languish; they continue to perform poorly and eventually delist or enter bankruptcy.

The F-score he developed essentially looks for companies that are profit-making, have improving margins, don't employ any (obvious) accounting tricks, and are strengthening their balance sheets. The score consists of nine variables are split into three groups:

• Profitability
• Balance sheet health, and
• Operating efficiency.

More information on exactly how the Piotroski F-Score is calculated can be found in Appendix 2. In our back tests we ordered our universe according to their F-score without taking into account the valuations of the stocks. We first wanted to determine if the F-score on its own is a strong predictor of market outperformance, because if so, it may be an even better predictor in combination with other parameters valuation factors, for example.

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In the above table you can see that the F-score is a strong factor as we defined it. It led to market outperformance for all three company sizes and worked particularly well for mid cap companies. Also, the strategy outperformed the market 75% of the time for small and mid-sized companies, and 83% for large companies. The results were also completely linear.

We not only wanted to test return on equity but also return on assets as a factor that can generate market outperformance. We defined return on assets (ROA) as net profit after tax divided by total assets. But I'm sure you can immediately see the shortcomings of using return on assets when selecting investments. Some companies, like auto manufacturers, need a lot of assets whereas others like software companies have hardly any assets that all. In the first example, return on assets is likely to be low, whereas is the second example it is likely to be extremely high.

However, it does not say how cheap or expensive the shares of the companies are priced, and that, as you saw with the valuation factors we tested, is more important.

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As you can see, ROA is not a very effective factor to use when selecting companies to invest in. Even though Q1 had higher returns than Q5, the results are not linear and the number of years this factor outperformed the market was only 58% for all three market size companies. Of all the single factors we tested, buying companies with the highest ROA was the second worst performing strategy you could have followed.

As a value investor we are sure you also believe that buying bad companies at very low prices is a perfectly viable strategy, provided of course, that the companies don’t go bankrupt. But what about buying good companies that generate a high return on invested capital without looking to see if the companies are over- or undervalued? A lot of investors believe that this is a way to identify market beating investments as it measures how effectively a company invests shareholder's money.

Previous research shows this is not the case. In his book, ‘What works on Wall Street’, in chapter 14, James O'Shaughnessy tested return on equity using a decile analysis and found that stocks in the top decile (highest return on equity) were on average only mediocre investments underperforming the market. Surprisingly, decile two and three did considerably better than their market.

We defined ROIC as the past 12-months operating income divided by the sum of net working capital (current assets minus excess cash minus current liabilities) and net fixed assets (total assets minus current assets minus intangible assets). We tested ROIC over one year, as well as the 5-year average, and this is what we found.

12 Month ROIC

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5 Year Average ROIC

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Similar to the above mentioned study, we also found ROIC to have a mixed influence on the returns during the test period. Companies with the highest ROIC (Q1) did not always perform the best, and there was no linearity in returns from Q1 to Q5. Thus you can safely say that a great company does not automatically make for a great investment.

Accruals are accounting adjustments for revenues that have been earned but are not yet recorded in the accounts, and expenses that have been incurred but are not yet recorded in the accounts. While accruals are necessarily to get an accurate reflection of the company's performance, they lend themselves to management discretion and possibly manipulation of earnings.

Management is under constant pressure to achieve targets and will try to speed up revenue recognition or delay expenses if it looks like results will come in below expectations. Conversely, management may actually slow down revenue recognition or pay for future expenses in order to smooth earnings into upcoming quarters.

If management is increasing the amount of overall earnings, not by actual cash earnings, but by accrual accounting manipulation then the possibility of a reduction in earnings or earnings growth is high. Conversely, a company with low or declining aggregate accruals should have more persistent earnings and higher quality.

Accrual manipulation leads to significant security mispricing which is very likely to lead to a correction in the future. You can read more about this in the paper Accrual Reliability, Earnings Persistence and Stock Prices by Richardson, Sloan, Soliman and Tuna.

The authors recommend to monitor and compare accruals levels and created 2 ratios for this: Balance Sheet Aggregated Accrual Ratio (BS Accrual Ratio) and Cash Flow Aggregated Accrual Ratio. (CF Accrual Ratio) The first one calculates the increase of Net Operating Assets compared to the average of the last 2 years.

Formula:

An increase in earnings accompanied by an increase in the accruals ratios should raise a red flag. The same is true when the company posts above industry-average growth combined with above-average growth of the BS Accrual Ratio.

S&P Analyst Richard Tortoriello recommends to use 'Capital Expenditures to Property, Plant and Equipment' as a red flag in his book 'Quantitative Strategies for Achieving Alpha'. This ratio shows the capital intensity of a company. In his studies, Tortoriello found that investing in companies with lower Capex to PPE generates higher returns.

Formula:

For an introduction of the Cash Flow Aggregated Accrual Ratio (CF Accrual Ratio), see the BS Accrual Ratio described above.

Another ratio S&P Analyst Richard Tortoriello recommends to use is 'Operating Cash Flow to capital expenditure'. ('Quantitative Strategies for Achieving Alpha') This ratio is used by analysts to determine a company's ability to fund operations. It helps to get a better understanding of whether a company is able to buy more assets without having to issue debt or equity.

A rising cash flow to capital expenditures ratio might indicate that the company is in a position to grow.

Please note that some industries are more capital intensive than others, which should be taken into account when evaluating companies.

Formula:

Current ratio is a liquidity and efficiency ratio that measures a firm’s ability to pay off its short-term liabilities with its current assets. A higher current ratio is always more favorable than a lower current ratio because it shows the company can more easily make current debt payments.

The current ratio is calculated by dividing current assets by current liabilities.

Another ratio S&P Analyst Richard Tortoriello recommends to use is 'Free Cash Flow to debt'. ('Quantitative Strategies for Achieving Alpha') This ratio shows how long it would take a company to pay back its debt using its current level of free cash flow. In his study, Tortoriello found that investing in the top 20% of companies with the highest FCF/debt ratio generated substantially higher returns compared to the market.

Formula:

The debt to asset ratio is a leverage ratio that measures the amount of total assets that are financed by creditors instead of investors. It shows what percentage of assets is funded by borrowing compared with the percentage of resources that are funded by the investors.

In this ratio we look at the long-term debt compared with the average assets, the average amount of assets held during a period.

A ratio used to find the value of a company by comparing the book value of a firm to its market value. Book value is calculated by looking at the firm's historical cost, or accounting value. Market value is determined in the stock market through its market capitalization.

Formula:

Most investors are more familiar with P/B or Price-to-book. This is just the inverted value.

We use Book-To-Market in our stock screener as it makes sure that companies with a negative value don't show up at the top of the list. We do include it in the scorecard as P/B is presented alongside the P/E, P/S and P/CF ratio.

The standard definition of earnings yield is the earnings per share divided by the price of a share. It's the inverse of P/E and shows the amount of money earned compared to the price you pay for a share.

Our earnings yield is slightly different and is in line with what Joel Greenblatt uses. As numerator, we use Operating income aka EBIT. As Joel explains:

By using EBIT (which looks at actual operating earnings before interest expense and taxes) and comparing it to enterprise value, we can calculate the pre-tax earnings yield on the full purchase price of a business (i.e., pre-tax operating earnings relative to the price of equity plus any debt assumed). This allows us to put companies with different levels of debt and different tax rates on an equal footing when comparing earnings yields.

As denominator, we use Enterprise Value (EV) as it takes into account both the price paid for an equity stake in the business as well as the debt financing used to help generate operating earnings.

We calculate the Earnings Yield as follows:

While Greenblatt's magic formula combines earnings yield with the quality ratio ROIC, a more recent study concluded that the formula derives all its magic from Earnings Yield and none from ROIC. According to Gray and Carlisle, a portfolio of stocks sorted only on the cheapness metric achieves an astounding return of 15.95% a year and outperforms the two-metric magic formula by more than 2% per year.

In the scorecard, we show the Earnings Yield for the selected stock. We also calculate the median Earnings Yield for all stocks, the company's sector, industry group, and industry. Finally, we include the percentile so you can easily compare a company to its peers. For more information, click here.

This ratio compares stock prices with earnings smoothed over the last 5 years. It was Benjamin Graham and David Dodd who came up with the recommendation in their 1934 book Security Analysis to not only take into account the last year but to look at the last 5 or 10 years. This allows the investor to smooth out the business and economic cycle, as well as price fluctuations. This long-term perspective dampens the effect of expansions as well as recessions.

We calculate this factor as follows:

$$\mathrm{Earnings\; Yield\; 5Y}=\frac{\sum \left(\mathrm{Operating\; Income\; last\; 5\; years}\right)}{(\mathrm{Enterprise\; Value}*5)}$$

This multiple is similar to Earnings Yield, but here we use Earnings Before Interest, Taxes, Depreciation and Amortization (EBITDA) as Nominator). By doing this, we can compare companies with different capital structures and capital expenditures. This way it gives a much better idea of the value of a company compared to the popular P/E ratio. As O'Shaughnessy explains:

Stocks that have very high debt levels often have low PE ratios, but this does not necessarily mean that they are cheap in relation to other securities. Stocks that are highly leveraged tend to have far more volatile PE ratios than those that are not. A stock's PE ratio is greatly affected by debt levels and tax rates, whereas EBITDA/EV is not. To compare valuations on a level playing field, you need to account for how a company is financing itself and then compare how relatively cheap or expensive it is after accounting for all balance sheet items.

You can think of it as taking all the revenue and subtracting the costs that solely go into running the business. The downside of EBITDA is that it can be abused by companies declaring it as “one-off” costs things that should really be considered normal costs. We use the EBITDA of the last 12 months.

As denominator it uses Enterprise Value. The formula is as follows:

EBITDA/EV has been identified in many academic studies as one of the most predictive valuation factors.

• In the 4th edition of 'What works on Wall Street', O'Shaughnessy reported that in his backtests, EBITDA/EV earned the best absolute return over the testing period (1963-2009), unseating all other ratios examined, and doing this with a relatively low volatility.
• Gray & Vogel found the EBITDA/EV to be the best performing metric, outperforming investor favorites such as Price-to-Earnings, Free Cashflow to EV, and Book-to-Market in the period 1971-2010. They also found in contrast to prior empirical work, that long-term ratios add little investment value over standard one-year valuation metrics. Click here to download their study.

In the scorecard, we show the EBITDA Yield for the selected stock. We also calculate the median EBITDA Yield for all stocks, the company's sector, industry group, and industry. Finally, we include the percentile so you can easily compare a company to its peers. For more information, click here.

This ratio is the opposite of Earnings Yield and was added to the screener to solve an important flaw. When sorting companies based on earnings yield, companies with a small enterprise value and positive EBIT will show up at the top of the list but as soon as the EV becomes negative, the stock will drop to the bottom of the list. Similarly, stocks with a negative EBIT and a negative EV are likely to feature at the top of the list.

To prevent this behavior, we created the EV/EBIT ratio. Stocks with a negative EBIT get a blank score and by sorting stocks ascending, stocks where the EV becomes negative don't get sent to the bottom of the list.

We calculate the EV/EBIT as follows:

Stocks with an EBIT <= 0 automatically get a blank score

This ratio is the opposite of EBITDA/EV and was added to the screener to solve an important flaw. When sorting companies based on EBITDA/EV, companies with a small enterprise value and positive EBITDA will show up at the top of the list but as soon as the EV becomes negative, the stock will drop to the bottom of the list. Similarly, stocks with a negative EBITDA and a negative EV are likely to feature at the top of the list.

To prevent this behavior, we created the EV/EBITDA ratio. Stocks with a negative EBITDA get a blank score and by sorting stocks ascending, stocks where the EV becomes negative don't get sent to the bottom of the list.

We calculate the EV/EBITDA as follows:

Stocks with an EBITDA <= 0 automatically get a blank score.

EV/EBITDA interpretation: What number are we looking for? A low value is good, a high value is bad.

This ratio is the opposite of FCF Yield and was added to the screener to solve an important flaw. When sorting companies based on FCF yield, companies with a small enterprise value and positive FCF will show up at the top of the list but as soon as the EV becomes negative, the stock will drop to the bottom of the list. Similarly, stocks with a negative FCF and a negative EV are likely to feature at the top of the list.

To prevent this behavior, we created the EV/FCF ratio. Stocks with a negative FCF get a blank score and by sorting stocks ascending, stocks where the EV becomes negative don't get sent to the bottom of the list.

We calculate the EV/FCF as follows:

Stocks with an FCF <= 0 automatically get a blank score

Some investors regard free cash flow as a more accurate representation of the returns shareholders receive from owning a business. It's essentially the money left over from operations after accounting for all the firm's obligations. The company has the possibility to distribute this money to its shareholders in the form of an increase in cash dividends or for buying back shares in the open market. It can also be used to pay down debt or it can be left in the bank account.

Some value investors prefer using cash flow ratios to find bargain-priced stocks because cash flow is traditionally more difficult to manipulate than earnings.

FCF Yield is calculated as follows:

For similar reasons as for the Earnings Yield 5Y, this ratio calculates the FCF Yield of the last 5 years to smooth out business and economic cycle, as well as price fluctuations. It's calculated as follows:

$$\mathrm{FCF\; Yield\; 5Y}=\frac{\sum \left(\mathrm{Cash\; Flow\; from\; Operations\; last\; 5\; years}\right)-\sum \left(\mathrm{Capital\; Expenditure\; last\; 5\; years}\right)}{(\mathrm{Enterprise\; Value}*5)}$$

Benjamin Graham, professor and founder of value investing principles, was one of the first to consistently screen the market looking for bargain companies based on value factors. He didn't have databases such as ValueSignals at his disposal, but used people like his apprentice Warren Buffet to fill out stock sheets with the most important data.

Graham was always on the lookout for companies that were so cheap, that if the company went into liquidation, the proceeds of the assets would still return a gain.

The ratio he used to identify these companies was Net Current Asset Value or NCAV. This ratio is much more stringent compared to book value (total assets - total liabilities) and is calculated as follows:

$$\mathrm{Current\; Assets}=\mathrm{Cash\; \&\; ST\; Investments}+\mathrm{Inventories}+\mathrm{Accounts\; Receivable}$$

Graham was only happy if he could buy the company at 2/3 of the NCAV. That's the sort of margin of safety he was looking for.

This strategy was very successful during the years after Graham published it in his book 'Security analysis' in 1934 and also in more recent studies it has proven to provide superior results. A study was done by the State University of New York to prove the effectiveness of this strategy showed that from the period of 1970 to 1983 an investor could have earned an average return of 29.4%, by purchasing stocks that fulfilled Graham's requirement and holding them for one year. Nowadays it's very difficult to find companies that meet Graham's criteria.

We calculate NCAV to Market as follows:

Our NCAV screen only selects companies with an NCAV-to-Market > 1.5.

P/B or Price-to-Book ratio is used to find the value of a company by comparing the book value of a firm to its market value. Book value is calculated by looking at the firm's historical cost, or accounting value. Market value is determined in the stock market through its market capitalization.

Formula:

In the scorecard, we show the P/B for the selected stock. We also calculate the median P/B for all stocks, the company's sector, industry group, and industry. Finally, we include the percentile so you can easily compare a company to its peers. For more information, click here.

This is undoubtedly the most popular value factor and for many investors the one true faith. It compares the price you pay per share compared to the earnings during the last 12 months. It's calculated as follows:

In the scorecard, we show the P/E for the selected stock. We also calculate the median P/E for all stocks, the company's sector, industry group and industry. Finally, we include the percentile so you can easily compare a company to its peers. For more information, click here.

In the original edition of 'What works on Wall Street', O'Shaughnessy wrote that the single-best value factor was a company's price-to-sales ratio (P/S). In his latest edition the P/S continues to perform well, but it was unseated by the value composites and EBITDA/EV due to 2 reasons: (1) A broader scope of analysis by using deciles and (2) two very bad years for P/S, e.g. 2007 and 2008.

A stock's P/S is similar to its P/E ratio, but it measures the price of the company against its annual sales instead of earnings.

It's calculated as follows:

In the scorecard, we show the P/S for the selected stock. We also calculate the median P/S for all stocks, the company's sector, industry group, and industry. Finally, we include the percentile so you can easily compare a company to its peers. For more information, click here.

This ratio compares the share price of the company to how much cash it's generating per share.

Formula:

This ratio is used as one of the components in O'Shaugnessy's VC1, VC2 and VC3 factors.

We defined the earnings yield ratio (EY) as operating income / enterprise value. We also tested the ratio in two ways: trailing 12-month operating income divided by enterprise value, and 5-year average operating income divided by enterprise value. Thus the lower the EY, the more investors are paying for operating income and the larger their expectations of future growth of the company.

Earnings Yield 12 months

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Earnings Yield 5 year average

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As you can see, trailing 12-months EY is a strong factor (as we defined it) over the test period. The returns in Q1 were higher than Q5 for all company sizes. It is interesting to note that the factor led to substantially better performance with mid and large companies. Also, for large companies, Q1 outperformed the market more than 80%, but only 67% of the time with small companies.

Market outperformance was substantial, with Q1 for the mid and large companies outperforming the market by more than 8% per year (pa). Small companies did not perform as well, but still outperformed the market portfolio by more than 4,6% pa.

Market outperformance was substantial, with Q1 for the mid and large companies outperforming the market by more than 8% per year (pa). Small companies did not perform as well, but still outperformed the market portfolio by more than 4,6% pa.

The 12-months EY was the second most successful single factor strategy to select large cap companies. The 5-year average EY is not as strong a factor as the one year. For all company sizes Q1 performed better than Q5, but the results were not linear with Q5 performing better than Q4 for all company sizes.

Free cash flow (FCF) can best be defined as the cash available from operations minus capital expenditure, and is the cash available to the company to pay dividends, make investments and buy back shares. We defined the free cash flow yield as cash from operations minus capital expenditure, divided by enterprise value. And we analysed the trailing 12-month FCF yield and the 5-year average FCF yield.

If you think about it, a high FCF yield should have strong predictive power over future returns. This may be because the market is less efficient when it comes to pricing free cash flow and its growth in the stock price. Another reason may be because FCF is more difficult to manipulate compared with earnings.

12 Month FCF Yield

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As you can see, the 12-month trailing FCF yield is a strong factor and it is very consistent. High FCF companies (Q1) outperform low FCF yield companies (Q5) consistently for all three market size companies, with the outperformance also completely linear over the five quintiles. Thus FCF valuation really matters in separating the winners from the losers. This valuation factor has a strong predictive power for the mid cap stocks, but less so for small companies.

5 Year Average FCF Yield

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Even though using the 5-year average FCF yield on mid cap companies (third best single factor we tested) over the test period would have given you a higher return than the 12-month FCF yield, the results for the other market size companies would have been a lot lower. As a factor it is also not strong, with the results not being linear over the five quintiles. Q1 did, however, outperform Q5 by a substantial margin.

A stock with a low price-to-book (PB) ratio is cheap, based on the price of acquiring its book equity. This factor does not take the earnings power of the company into consideration and relies on the assets and liabilities of the company being fairly valued. The price-to-book value was a favorite tool of Benjamin Graham and other earlier value investors. In spite of its shortcomings, PB is a strong factor in generating market outperformance, and also works well with other factors as you will see later.

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Investors who believe PB is an important factor when looking for bargains would be correct. It certainly is for the mid cap companies, with Q1 generating market outperformance of 12,1% pa, and Q5 underperforming the market by 8,5 % pa. For the other company sizes, the factor is less strong. However, for all three company sizes it led to market outperformance between 66% and 75% of the time over the 12-year test period.

Of all the single factors we tested, a low PB strategy applied to mid cap companies led to the highest return of 400,3% over 12 years. That was nearly 370% better than the market portfolio. It did not work as well for large cap companies, returning only 203,6%, and was even less successful when applied to small companies, leading to a 172,5% return. So over the 12 years tested you would have been well rewarded if you used only a low price-to-book strategy.

The price-to-sales measures the market value of the company against its annual sales. Investors buy low PSR stocks because they believe companies are undervalued when they are not paying much for the sales the company generates. Also, PSR is a more stable ratio than EY, for example as sales fluctuate less than earnings, and it can be used to value companies that temporarily have no earnings.

James O'Shaughnessy in his book, ‘What works on Wall Street’, called the PSR the ‘king of the valuation factors’ as it beat the returns of all the valuation ratios he tested.

James Montier, in his 2008 paper, ‘Joining the dark side: Pirates, Spies and Short Sellers’, on the other hand, used the price-to-sales ratio to find overpriced companies that may be good candidates to sell short. A high PSR allows you to hone in on companies whose valuation has lost all touch with reality.

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As you can see, this is a strong factor with linear returns for all three company sizes. However, it is not as effective with small companies as it only beat the market 58% of the time. Returns of Q1 were also not as high as some of the other single factors we tested. This may be because sales do not automatically lead to profits, and thus this ratio may work better in combination with another factor; something we tested in the two-factor strategies.

The beta of a share is a number describing the relation of its returns with that of the financial market as a whole.

This volatility measure gives you an idea of how far the stock will fall if the market decreases and how high the stock will rise if the market increases.

A stock with a beta greater than 1 is considered more volatile than the market, less than 1 means less volatile. If the stock is perfectly correlated with the market, the beta equals 1.

If a stock gets a beta of 1.15, it has a history of fluctuating 15% more than the market. If the market goes up, the stock should outperform by 15%. If the market heads lower, the stock should fall by 15% more.

Volatility is the degree of variation of a trading price series over time as measured by the standard deviation of returns.

Formula:

The standard deviation of daily log normal price returns over the past year, annualized

Volatility is the degree of variation of a trading price series over time as measured by the standard deviation of returns.

Formula:

The standard deviation of daily log normal price returns over the past 2 years, annualized

Pim Van Vliet uses this factor in his Conservative Formula screen. For more information , click here.

Volatility is the degree of variation of a trading price series over time as measured by the standard deviation of returns.

Formula:

The standard deviation of daily log normal price returns over the past 3 months, annualized

Volatility is the degree of variation of a trading price series over time as measured by the standard deviation of returns.

Formula:

The standard deviation of daily log normal price returns over the past 6 months, annualized

In comparison with the USA there have been relatively few studies conducted on what works in investing in the European stock markets. With this paper we would like to make a contribution and examine what factors led to excess returns in the European markets over the 12-year period from 13 June 1999 to 13 June 2011. The factors we tested were:

• Earnings yield
• Free cash flow yield
• Price-to-book
• Price-to-sales
• Piotroski F-score
• Return on invested capital (ROIC)
• Return on assets (ROA)
• Net debt on Market Cap
• Relative strength / price index

We didn't only test the historical value of the factors, but where it made sense, we also tested the 5-year average to see if it is a better indicator to use to generate market outperformance. When we found a factor that showed strong out-performance we tested it together with other factors to see if two factors generate even more market outperformance. In addition, we also tested two investment strategies, the Magic Formula and the ERP5 strategy, for their ability to outperform the market. What we found mostly confirmed what other research studies found, but a few results were really astounding.

If we averaged the return over large, medium and small companies, the best factor was the price-to-book ratio, generating an average compound annual return of 10.92% compared with 2.25% for the market over the period. The second best average factor was the 12 month free cash flow yield that generated a compound annual return of 10,87%.

For small companies the best single factor was the 6-month price index which generated a compound annual return of 11.91%. The second best factor was the 12-month price index, generating a return of 10.32%.

For medium sized companies the best factor was price-to-book value, which generated an astounding compound return of 14.36% over the period. Second was the five year average free cash flow, generating a compound 12,83%.

For large companies the best factor was free cash flow yield, leading to compound growth of 10.81%, with earnings yield a close second with a compound return of 10.64%.

Interesting to note is that with small companies, unlike with medium and large companies, valuation factors did not lead to the best returns.

Of the two investment strategies we tested, the ERP5 strategy beat the Magic Formula for small (compound 12.95% compared with 7.33%), medium (compound 11.76% compared with 9.05%) and large companies (compound 8.60% compared with 8.39%).

What if we told you we found a simple two factor method you can use to select investments that led to a 23.5% per year compound return (market was 2.25%) over the 12 years we tested? That is a total return of 1157.5% compared with the 30.54% the market returned! That is what a combination of the 6-month price index with the lowest price-to-book value companies returned. Very interesting was that the 10 best performing two-factor strategies all had one momentum factor as one of the factors.

This was hard for us to fully grasp as classical value investors. We always thought buying a cheap, good company would give you market beating results. And the cheaper the company gets, the higher your returns would be. This strategy will give you market beating results, but not nearly as good as buying companies where the share prices are already increasing.

For example, the 11th best performing two-factor strategy, 12 month free cash flow yield combined with price-to-book ratio, led to a total return of 713.7%. Not bad. But if you used the best performing strategy, your return would have been 11.57 times your initial capital compared with only 7.13 times if you used the 11th best strategy!

In this second part of the paper we build portfolios by combining two of the factors we have already tested. Through the combination of the second factor we want to find out, using the strong factors we have already identified, if it leads to higher market outperformance more consistently.

To do this we first sorted all the companies in our investment universe by the first factor. We then selected only the companies in the first quintile, and then used only this group of companies and sorted them into five quintiles using the second factor. So the two-factors were not weighed equally. The first factor in each case had more weight as we only selected the best quintile from this factor to use with the second factor.

We also tested the same factor twice; for example, using price-to-book as the first and second factor. We did this to determine if this combination leads to higher market outperformance compared with the original one-factor tests. As explained, for the two-factor tests we did not split the universe into different market capitalization as in doing so we would not have been able to form second factor quintiles with at least 30 to 40 companies in each quintile.

Overall, what we found was that all the two-factors we tested, even the worst performing quintiles, substantially outperformed the market portfolio.

For this backtest we first sorted our universe of stocks by earnings yield (EY) which we defined as operating income divided by enterprise value. We then took the 300 or so companies with the highest earnings yield and sorted them by the 14 second factors we tested.

For each of the second factors, we divided the 300 companies into five quintiles and calculated the performance of each quintile.

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As you can see, using EY (valuation factor) is very effective to identify market beating stocks. On average, across all second factors tested, the strategy led to an average performance of just under 405% (median was 368%); substantially higher than the market portfolio return of 30.54%.

The best return of 814% was achieved by combining the earnings yield with the 6-month price index. This means a combination of price momentum, as well as undervaluation based on earnings yield. Interesting was that the second best combination was earnings yield combined with a 12-month price index, also a momentum factor.

The worst performing strategy was earnings yield combined with return on invested capital, which returned 143% over 12 years. Even though this strategy also beat the market portfolio, it was not nearly as effective as using price momentum as a second factor. Even though the results of this two-factor strategy were good, based on the average Q1 returns, this was the sixth best two factor strategy we tested.

With this combination we combined the 20% of companies with the highest ERP5-rank with all the second factors we tested.

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Your average return of combining the ERP5 score with all the second factors would have been 458.5% (median was 479.9%) over 12 years. The average this was the fourth best two-factor strategies we tested.

Similar to what we found with the MF-rank, the best performing strategy was combining the ERP5 score with companies that had the highest 6-month price index. If you did this your returns would have been 732.1%.

The worst return was generated by combining the ERP5 score with companies that had the highest return on investment capital on average over the past five years. This would have only given you a return of 114.4%.

With this two-factor backtest we combined the cheapest 20% of companies based on price-to-free cash flow (over the past 12-months) in our investment universe with all the second factors we tested for.

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As you can see the results were also very good, with an average return of just under 470% (median was 488.8%). On average this was the third best two factor strategy we tested.

The best performing strategy was combining a high price-to-free cash flow ratio with the 12-months price index. This led to a total return of 755%. With this strategy the second best performance was not the 6-month price index but buying the lowest price-to-book ratio companies. If you did this, your return over the 12 years would have been just under 714%.

The two-factor strategy with the lowest return was the combination of high free cash flow companies with companies that generated high returns on invested capital. In this case the 12 year return was 199.1%

With this combination we wanted to determine if the results of the Magic Formula could be improved by adding an additional factor to select companies to invest in. Out of our universe of companies we thus took the 20% of companies with the best MF-ranking and combined them with the second factors we tested.

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Across all the second factors we tested the average return was 401.8% (median was 359.3%) over 12 years. On average, this was the eighth best (out of nine) two-factor strategy we tested. The best performing combination would have been to combine the best Magic Formula companies with the companies that had the highest 6-months price index. This would have given you a return of 783.3% over 12 years.

The worst performing strategy would have been to combine the MF-rank with return on invested capital. In this case your returns would have been 121.6%.

With this combination we first selected the 20% of companies with the highest Piotroski F-score and then divided these companies into quintiles based on the second factors we tested. It's worth mentioning that even though you may think that combining the F-score with low price-to-book companies would be what Joseph Piotroski did in the paper mentioned previously, but that would not be correct.

In his paper Mr Piotroski first selected low price-to-book companies and then sorted these by the F-score. So for you to see the results that the strategy based on Mr. Piotroski’s paper, you would have to look under price-to-book as the first factor and the F-score as the second factor.

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Based on average returns of the best quintile of all the second factors, this strategy returned 422% (median was 421%). Out of the nine two-factor strategies we tested this one on average was the fifth best strategy. The best combination that would have given you a 680.4% return over 12 years would have been to combine a high F-score with companies that had the highest 12-month free cash flow yield.

Here we first took the 20% of companies in our investment universe with the highest 12-month price index and then combined these companies with the 14 second factors we tested.

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On average, across all the second factors we tested, this strategy would have given you a return of 404.9% (median was 420.7%). This was the seventh best (out of nine) two-factor strategy we tested. The best combination was combining the 12-month price index with the companies with the highest earnings yield, using the past 12 months earnings. The strategy would have given you a return of 802.4%.

The worst strategy would have been to combine the highest 12-months price index with the same factor again. This means from the 20% of companies with the highest share price increase over the past 12 months, you would have chosen the 20% that went up the most price over the past 12 months. In this case your return would have been 114.9%.

In some of the previous combination strategies the 6-months price index was one of the best second factors to use. In this combination would like to determine if it is also a good first factor to use. We thus selected the 20% of companies with a higher 6-months price index and used only these companies when we made up the portfolios for the second factor tests.

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And it turns out that using the 6-months price index as a first factor gives you a very satisfactory return. On average, across all 14 second factors we tested, the best quintile would have given you an average return of 566% (median was 610%). The best performing strategy was combining the 6-months price index with the lowest price-to-book companies. If you did this to select investments, your return over the past 12 years would have been 1157.5%.

The worst performing strategy combination would have been combining the best 6-months price index companies by the same factor again. This would have given you a return of only 122.1%.

With this two-factor back test we took the cheapest 20% of companies in our universe with the lowest price-to-book value and then sorted these companies into five quintiles based on the second factor we tested.

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Of the nine two-factor strategies we tested, using the price-to-book as the first factor led to by far the highest average return of 620% (median 617.5%). The best two-factor strategy was combining cheap price-to-book companies with the companies that had the highest 6-month price index value. This led to a total return of nearly 1030% over the 12 year period we tested. The second best combination was also momentum, and was the combination of price-to-book value with the highest 12-months price index companies. This led to a total return of 987%.

The worst strategy was the combination of low price-to-book companies with companies that had the highest 5-year average earnings yield. This would have led to a total return of 354.3%. Not bad at all, but not close to the 1030% of the best performing two-factors.

It is of course very hard to make predictions about what investment strategy will work best in future, but looking at the dreadful market over the last 12 years the returns of buying low price-to-book companies with a high 6-months price index is truly astounding.

With this combination we took the lowest 20% of price-to-sales ratio companies and combined them with the second factors we tested for.

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Even though price-to-sales is also a valuation factor, on average, using this combination gave the lowest returns of all the two-factor strategies we tested, generating an average return of 345.3% (median 333.4%). The best performing strategy was selecting companies with a cheap price-to-sales ratio as well as companies with the highest 6-months price index values. This would have given you returns of 563% over 12 years.

The worst combination would have been combining the low price-to-sales ratio companies with those that generated the highest ROIC over the past five years. Using this strategy your return would have been 184.8%.

Here are the main points of the two-factors tests:

• All two-factor strategies we tested substantially outperformed the market with even the worst performing strategy returning 114.4% over 12 years compared with the 30.54% of the market portfolio.
• Price momentum, both 6- and 12-months played a substantial part in all 10 of the best performing two-factor strategies.
• The three best performing strategies that all generated returns of more than 1000%, all either as first or second factors, contained the highest 6-month price index as a factor.
• A low price-to-book value was also a very important factor as it formed part, either as first or second factor, in three out of four of the best performing two-factor strategies.

If you only looked at the first quintile of all two-factor strategies we tested, these were the five best and worse strategies:

Best Strategies:

Even though we tested some single factors that did lead to strong market outperformance, the two-factor strategies we tested were substantially better. For example, if we combine the first quintile performance of all the one and two-factor strategies we tested, and sort them from best to worst, the best single factor performance (achieved by applying a low price-to-book ratio to mid-cap companies) was at position 69 (the next was at position 91). All the strategies that performed better were two-factor strategies.

The most surprising result we found, especially for value investors, is that price movements over previous 6- and 12-months (6- and 12-months price index) were factors in each of the 10 best performing two factor strategies we tested. This is not what we learned as classical value investors. We learned that the more a company share price declined, as long as it became cheaper in terms of valuation, the more attractive it was as an investment.

With our back testing we found that valuation still matters, but it has to be applied in a different way. You first have to look for the 20% of companies that increased the most in price over the previous 6-months and then sort these companies by price-to-book value and buy the 30 companies with the lowest price-to-book value.

At this point you may be asking yourself the same question we have - the results we have shown are all based on historical financial information, but what does this mean for my future investment returns? The simple answer is we cannot say for certain, but we have a good idea. We now know what strategies were very successful in arguably one of the worst 12 years in terms of stock market performance in at least half a century.

For the next 10 years the top performing strategy we tested of buying the lowest 20% of companies by book value of the 20% of companies that have increased the most in price over the past six months will most likely not be the best strategy. But it will still give you outstanding market beating returns. In the past 12 years the strategy returned just under 1160%, compared with the market portfolio 30.54%. Does it really matter if the strategy falls to position 20 of the strategies we tested and generated a total return of 670%? Most likely not, because you would probably have outperformed 99% of all investment funds worldwide. This means that the strategies that performed the best over the past 12 years may not do so over the next 10 years, but they will still be amongst the top strategies in terms of overall returns.

But what will happen if everybody starts using the best performing strategies; surely they will stop working, you may be thinking. If everybody does they will definitely stop working as investors pile in and push up prices to where these companies would not be undervalued anymore. But as Joel Greenblatt in his book, ‘The Little Book That Still Beats The Market’ mentioned, the reason everybody will not follow strategy is because it doesn't work all the time. And as soon as it stops working investors will abandon it like they abandoned the top performing investment fund we mentioned above. Most likely at exactly the wrong time; just before the strategy would substantially start outperforming the market once again. Remember the best performing strategy we mentioned outperformed the market only 83% of the time and had negative returns in three of the 12 years. In one of the last years, or one of the other years that the strategy didn't outperform the market, it would most likely have been exactly the time when investors abandoned the strategy.

One last point we would like to mention. Do not for a minute think that it is easy to follow these strategies. If you see what companies they come up with you will immediately start analysing them and for example say, ‘There's no way I am investing in that industry at the current time’, or ‘Look at this company's financial statements, it’s completely hopeless’. That may be so with one or two of the companies that the strategy comes up with. That is the reason why we suggest that whatever strategy you follow you invest in a minimum of 30 companies. This means that even if one of two companies go bankrupt, the others will do extremely well and your overall performance will still be outstanding.

We sincerely hope that you found the study of value and it substantially improves your investment returns. If it has, or if you have any comments or suggestions please let us know.

Copyright © 2012 MFIE Capital. All rights reserved.

This research was possible using the database and models created by the MFIE Capital team.

Unless otherwise indicated, all materials on these pages are copyrighted by MFIE Capital. All rights reserved. No part of these pages, either text or image may be used for any purpose other than personal use. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission.

Research, Modelling & Development

• Philip Vanstraceele
• Olivier Dambrine
• Luc Allaeys

Editing

• Philip Vanstraceele
• Olivier Dambrine
• Tim du Toit

In his book, 'The Big Secret for the Small Investor’, Joel Greenblatt wrote that the best performing stock mutual fund of the last decade earned more than 18% annually. This is impressive since the market, as measured by the S&P 500, was actually down close to 1% per year between 2000 and 2009. Yet the average investor, in the same fund, managed to lose 11% per year over those 10 years. How is that possible?

After every period in which the fund did poorly, investors ran for the exits, and after every period in which the fund did well, investors piled in. The average investor managed to lose money in the best performing fund by buying and selling the fund at just the wrong times. Investors seem to forget that even the best-performing fund managers go through long periods of significant underperformance.

Our emotions and our behaviour are under the continuous influences of the media, and of course of other people. Emotions are simply a wrong guide to base investment decisions on. Where money is concerned, emotions regularly overcome rationality. This can also be seen in the market as stocks go up and down for no reason other than fear, greed, hope or despair.

In order to avoid your emotions influencing your investment decisions, you should invest using a strict standardized process; a proven system which you can rely on that removes emotions from the decision making process. Think of this system as the process or procedure that a doctor needs to follow when performing an operation. It does not guarantee success, but the procedure has proven its reliability over time and has a high probability of success.

The need to focus on the investment process with the highest probability of success, rather than the outcome, is critical when investing. This is because investment outcomes are probability based, and even if they have a high probability of success there is still a chance that they will be negative. However, only if you invest using a system with a high probability of market beating returns over the long term do you have a high probability of being a successful investor.

And this is exactly what we would like to do with this paper. Determine exactly what factors you should use when selecting your investments to give you the highest probability of substantially outperforming the market. In order to do this we looked at factors based on historical financial data to see how effective each factor is in generating market outperformance. We did this using a computer database that can quickly and accurately process or screen a large number of companies, but more importantly, a computer has no emotions. Once you have identified what factors have a probability of outperforming the market, you can add them to the computerised stock screener to generate the names of companies that meet these factors. This list is an excellent starting point for selecting market beating investment ideas.

This paper examines which historical value factors or financial ratios have the highest probability of consistently outperforming the market.

Considerable research has documented the use of individual ratios or combinations to create portfolios that outperform the market. One factor that received a lot of attention in the past is the book-to-market investment strategy. Studies by Lakonishok, Shleifer and Vishny (1994) and Fama and French (1992) have demonstrated that buying a portfolio of high book-to-market (low price-to-book ratio) companies results in market outperformance. Joseph Piotroski (2000) extended this research by creating his own Piotroski F-score; an accounting based 9-point scoring system that when used in combination with high book-to-market (low price to book) companies shows a consistent upward shift in distribution of returns.

Other authors focused on different ratios. Joel Greenblatt focused on earnings yield and ROIC, and found that ranking US companies based on these measures and investing on a consistent basis in the top companies resulted in an outperformance of 23% compared with the benchmark. In our previous papers, we concluded that these results can be reproduced when we tested it on European companies. James O’Shaughnessy focused on different factors, such as price-to-sales, and proved in his tests that these value factors help create portfolios that outperform the US market on a consistent basis.

The studies above were performed using different datasets and periods, so it’s not trivial to understand which factors or combination of factors leads to the most market outperformance. The goal of this paper is to provide more clarity in this area and to help investors understand which ratios lead to the biggest market outperformance and which have no effect. Finally, we combine the single factors generating the highest market outperformance with a second factor to determine if this increases market outperformance even more.

Our backtest universe is a subset of companies in the Datastream database containing an average of about 1500 companies in the 17 country Eurozone market during our 12-year test period (13 June 1999 to 13 June 2011). We excluded banks, insurance companies, investment funds, certain holdings companies, and REITS. We included bankrupt companies to avoid any survivorship bias, and excluded companies with an average 30-day trading volume of less than €10 000. For bankrupt companies, or companies that were taken over, returns were calculated using the last stock market price available before the company was delisted.

In order to create a market portfolio to compare our results against - remember we excluded certain types of companies - we constructed a market portfolio based on the 250 most traded companies in our test universe, over the previous 30 days, weighted by trading volume in Euros. Each year on 13 June the market portfolio was reconstructed with the then 250 most liquid companies, weighted by trading volume (average over the previous 30 days before 13 June).

As you can see below, our constructed market portfolio is closely correlated with the EURO STOXX index, a broad but liquid subset of the STOXX Europe 600 Index. Over the 12-year period of the study, the market portfolio generated a return of 30.54 % or 2.25% pa, dividends included.

The test period was most certainly not a good time to be invested in stocks.

The 12-year period we tested included a stock market bubble (1999), two recessions (2001, 2008-2009) and two bear markets (2001-2003, 2007-2009). In spite of all the substantial movements, over the whole period it was essentially a sideways market, as Vitaliy Katsenelson defined in his book, ‘The Little Book of Sideways Markets’. The tables below show the movement of the market portfolio over the 12-year time period we tested:

When we tested single factors the portfolios sizes were quite large. As our back test universe was quite large, with an average of 1500 companies, the average portfolio’s size per quintile was around 300 companies. It is of course not practical to have a portfolio with such a large number of companies. Thus in the two-factor strategies we tested, we formed portfolios with 30 to a maximum of 60 companies for each quintile. We did this by taking the first quintile of the first factor we tested (about 300 companies), sorted it by the second factor, and divided it into five quintile portfolios (300/5=60). By testing the two-factors this way you have the added advantage of accurately identifying the stronger and weaker factor, as the first factor is emphasized due to the inclusion of only its first quintile companies.

For the two-factor tests, we did not split the universe into different market capitalization as in doing so we would not have been able to form portfolios with at least 30 to 60 companies.

Each year, as with the market portfolio, all the portfolios we tested were formed on 16 June. We chose 16 June as most European companies have a December year-end and by this date all their previous year-end results would be available in the database. The annual returns for our back test portfolios were calculated as the 12-month price change plus dividends received over the period. Returns were compounded on an annual basis. This means each year the return of the portfolio (dividends included) would be reinvested (equally weighted) in the strategy the following year. The portfolios were all constructed on an equal-weighted basis.

In order to test the effectiveness of a strategy, we divided our back test universe into five equal groups (quintiles), according to the factor we were testing. For example, when testing a low price-to-book (PB) value strategy, we ranked our back test universe from the cheapest (lowest PB) to the most expensive (highest PB) stocks.

The cheapest 20% of companies were put in the first quintile (Q1), the next in the second, and so on, with the 20 % of companies with the highest price-to-book value in the fifth quintile (Q5).

We defined a good factor or strategy as one where:

• The top quintile (Q1) outperforms the bottom quintile (Q5) over the 12 years we back tested and
• There must be a linearity of returns among the quintiles (quintile one must outperform quintile 2 which must outperform quintile 3, up to quintile 5) over the 12 years we tested, and
• The strategy must also consistently outperform the market over time. We defined consistent outperformance when the first quintile (Q1) outperformed the market portfolio 60% or more of the time.

So, in summary, we are looking for factors that increase the probability of positive returns, beat the market, and how strong or weak this probability is.

In order to determine if the size of the company has any effect on the effectiveness of a one factor test, we divided the back test universe into three groups based on of market capitalization:

• SMALL CAP - companies with a market capitalization between 15 million Euro and 100 million Euro.
• MID CAP - companies with a market cap between 100 million and 1 billion Euro.
• LARGE CAP - companies with a market capitalization greater than 1 billion Euro.

Compared with US studies, our Small Cap group can also be classified as Nano capitalization companies, and our Mid Cap group equivalent to US small capitalization companies.

In the paper we only use historical accounting data and no forecasts. The reason being is that there is ample evidence that forecasts cannot be relied on. For example, in his excellent book, ‘The New Contrarian Investment Strategy’, David Dreman mentioned a study that used a sample of 67.375 analysts' quarterly estimates for companies listed on US stock exchanges.

The study found that the average analysts’ error was 40%, and that the estimates were misleading two-third of the time! A less important but not insignificant factor is that historical accounting data is also cheaper.

Using only one factor we tested the following:

1. Value factors (earnings yield, free cash flow yield, price-to-book ratio and price-to-sales),
2. Quality factors (Piotroski F-score, ROIC, ROA, net debt ratio), and
3. Momentum factors (price Index/relative strength).

We also tested two investment strategies; the MF strategy developed by Joel Greenblatt and explained in his book, ‘The Little Book that Still Beats the Market’ and the ERP5 strategy developed by MFIE Capital.

Our goal was to look at each factor and determine if it is a strong or a weak contributor for generating market beating returns.

The idea behind relative strength is to find companies with the best performing stock prices; the ones that have gone up in price the most over a specific period of time.

In his book, ‘What works on Wall Street’, James O'Shaughnessy calculated relative strength by looking at the price increase of a stock over the past year. Looking at the change in stock prices over a year, he found that winners seem to continue to win and the losers kept on loosing.

In this study we first set out to also see if relative strength can separate winners from losers. Then with the multiple factor portfolios, we will see if the combination of reasonable priced stocks with momentum can give you even higher excess returns. We have analysed two periods of short term price momentum:

• Companies with the best 6-month price appreciation (stock price on the day the portfolio was compiled minus the stock price six months ago which we called the 6-month Price Index, and
• Companies with the best 12-month price appreciation (stock price on the day the portfolio was compiled minus the stock price 12-months ago, which we called the 12-month Price Index.

6 Month Price Index

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12 Month Price Index

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As you can see the short term price index (6-months) is a strong factor as we defined it. Results are linear with Q1 beating Q5 for all size companies and the factor outperformed the market just over 83% of the time for all three market sized companies.

The 12-months price index is not strong as it is not linear for large cap companies. It also outperformed the market only 58% of the time for mid cap companies.

What is very clear is that companies with a low price index (Q5) for both the 6- and 12-month price index are to be avoided at all costs as for small companies as the 6-months was the worst, and 12-months price index the second worst single factor strategy we tested.

The results also show good or bad news about a company may be quickly incorporated in the stock price, but clearly with some delay, otherwise the top quintiles would not outperform the bottom quintiles as well as the market. The factor is particularly strong for small and mid-cap companies. This may be, for example, if a company's order book is decreasing the company’s employees, or suppliers may notice this and start selling the shares who then tell others who then sell shares before the news is really public.

The increased numbers of sellers that are selling leads to supply exceeding demand, causing the stock price to decline. But there may also be other reasons, such as company insiders that may be buying.

Another reason why short term momentum works is the so called ‘inertia effect’. In his book, ‘The New Finance’, Robert Haugen said stock prices exhibit inertia in the short-term and often have reversals in the long-term. This is driven by the tendency of companies in competitive industries to revert to the mean. Yesterday's winners become losers or average performers, while yesterday's losers improve. The market is slow to recognise these reversals and thus share price trends continue.

• What works on Wall Street – James O’Shaughnessy
• Contrarian Investment Strategies : The next generation – David Dreman
• The little Book of Sideways Markets – Vitaliy Katsenelson
• The Big Secret for the Small Investor – Joel Greenblatt
• Quantitative Strategies for achieving Alpha – Richard Tortoriello
• Value Investing – Tools and Techniques for Intelligent Investment – James Montier
• Predicting the Markets of Tomorrow – James O’Shaughnessy
• Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers - Joseph D. Piotroski
• The Little Book That Still Beats the Market – Joel Greenblatt
• Contrarian Investment, Extrapolation, and Risk, Journal of Finance 49, 1541-1578 - Lakonishok, Josef, Andrei Shleifer, and Robert W. Vishny
• The Cross-Section of Expected Stock Returns, Journal of Finance 47, 427-465 - Fama, Eugene F., Kenneth R. French

Here are the main points for the one factor tests:

• Valuation factors have a strong predictive power to achieve market outperformance.
• The mid cap companies seem to outperform the small cap and large cap companies except for the results of the ERP5 rank.
• The fact that a company generates a high return on invested capital does not make it a market beating investment; valuation is more important.
• Investing in companies with a good F-score, which suggests improving fundamentals, results in market beating returns.
• Winners continue to win and losers continue to lose, as shown in our test using 6- and 12-months price index factors.

In the following table we show how all the single factors we tested met our criteria of being classified as a strong factor.

As a reminder, this is how we defined a strong factor:

• The top quintile (Q1) outperforms the bottom quintile (Q5), and
• There must be a linearity of returns among the quintiles (quintile one must outperform quintile 2 which must outperform quintile 3, up to quintile 5), and
• The strategy must also consistently outperform the market over time. We defined consistent outperformance when the first quintile (Q1) outperformed the market portfolio 60% or more of the time.

If you only looked at the first quintile of each single factor we tested, this detailed the two best and worse strategies for each market size group of companies:

LARGE COMPANIES:

MEDIUM-SIZED COMPANIES:

SMALL COMPANIES:

To create a new portfoliogroup, navigate to the portfoliogroups screen. Click on the button Add Portfoliogroup.

Enter the following fields:

Click the Create button to Create the portfoliogroup.

To delete a portfolio, navigate to the portfoliogroup list and click on the portfolio you wish to delete. Click on the Delete button.

Once a portfoliogroup is created, the list of portfolios will be displayed. Click on the Add button to add a specific portfolio. Click on Delete to remove it.

To create a new portfolio, navigate to the portfolios screen. Click on the 'Add portfolio' button. This will take you to the transactions screen where you can enter the following parameters:

Change the values and hit the 'Save' button.

To edit a portfolio settings, navigate to the portfolios list and click on the portfolio you want to update. Change one of the parameters and hit 'Update'.

To delete a portfolio, navigate to the same screen and hit the 'Delete' button. To avoid accidental deletion we will ask you to enter the name of the portfolio. After submitting this form the portfolio will be deleted and can no longer be recovered.

When you create a new portfolio, you will be able to enter transactions. To enter a transaction to an existing portfolio, navigate to the portfolio and click on the transactions tab.

Transactions can be entered in quantity or value. The first option is ideal for entering your portfolio, the second option can be used to enter model portfolios.

Users who want to enter their exact portfolio will pick the first option, 'Enter quantity'. The following fields can be entered:

Based on the values entered above, the system will calculate the value of the transaction. To save the transaction, click the blue save button. To cancel the creation click on the grey cross button.

The second option is 'Enter amount'. We use this option for our model portfolio, where we invest €10.000 in each position. We can enter the value and the system will calculate the quantity based on the value minus brokerage fee, converted to the stock currency. It will round this number and use this number as quantity. It will save this quantity and recalculate the value of the transaction.

To edit an existing transaction, click on the pencil next to the transaction.

The edit form pops open and here you can update the properties of the transaction. Click on the blue button to save. To delete the record, click on the red button. To cancel the operation, click on the grey button.

To search for a specific transaction, use the textbox on top of the grid. As you start typing, the grid will only show the securities that have this text as part of their name.

Click on the Positions tab for a portfolio or portfoliogroup to navigate to the current positions report. By default, this will show the report for the latest date on which quotes are available. You can change this by picking a different date in the date picker at the top of the grid.

This report has the following columns:

The report offers similar options to the stock screener:

• Export to excel
• Sort columns ascending or ascending
• Lock columns
• Filter columns
• Group by column by dragging it to the top of the grid

Click on the Sold tab for a portfolio or portfoliogroup to navigate to the sold positions report. By default, this will show the report for the latest date on which quotes are available. You can change this by picking a different date in the date picker at the top of the grid.

This report has the following columns:

The report offers similar options to the stock screener:

• Export to excel
• Sort columns ascending or ascending
• Lock columns
• Filter columns
• Group by column by dragging it to the top of the grid .

The time-weighted return (TWR) is a measure of the historical performance of an investment portfolio which compensates for external flows. The amount of money you invest in stocks will vary, so you have to constantly adjust the basis on which you calculate your profit depending on the size of your positions. This is a very laborious calculation, but it's what fund managers use to asses their performance over time.

Find out your TWR over time for a specific portolio or portfoliogroup by clicking on the TWR tab. Here you can see 3 lines:

• TWR (blue): your YTD return (TWR).
• Benchmark (red): the return of the benchmark selected for your portfolio or portfolio group.
• Alpha (yellow): the difference between your TWR and the benchmark return. If the alpha is positive, you're doing a great job! If the alpha is always negative, it might be worth switching to a different stock picking strategy.

This report will show the current year as per default. Switch to a different year by selecting it from the list of values and by clicking on the refresh button.

Are you picking the right stocks and are you investing at the right time?

This next report calculates the TWR of each position within a portfolio or portfolio group and shows the evolution during the months of this period. It can also show the alpha, which shows whether the stock has outperformed the selected benchmark market index.

This report will show the current year as per default. Switch to a different year by selecting it from the list of values and by clicking on the refresh button. Switch on teh alpha toggle and click on refresh to see the alpha report.

The final tab on the scorecard provides a few useful links that are helpful to conduct further analysis on the company. We include the following links:

• The company website
• Yahoo Finance
• Google Finance
• The financial times
• Reuters
• New! Our dynamic glossary which opens the glossary with each formula and values for the selected company.

When evaluating a company, one should always check the latest news. Recent events might explain the reason why a stock is undervalued or has shown significant momentum. A company might be a takeover target, analysts might have up- or downgraded the stock, etc... By clicking on the 'News' tab, you get the latest 10 news items provided by the google finance news service.

The Z-score was built to predict whether a company is likely to go bankrupt in the next 2 years. It was developed by finance professor Edward I. Altman and is based on multiple corporate income and balance sheet values. The Z-score will signal 70% of bankruptcies of publicly listed companies and predicted the demise of Enron, Worldcom, and other disasters.

The Altman Z-Score is displayed on a bullet graph. A value below 1.81 indicates that the company is in significant distress and there's a high probability that the company will go bankrupt in the next 2 years. A value between 1.81 and 2.99 indicates that there's a good chance that the company will go bankrupt in the next 2 years. A value above 2.99 indicates that the company is in the "safe" zone.

Please note that we currently support only the original Altman Z-Score. This is only valid for manufacturing companies.

The M-Score was created by professor Beneish. It uses eight financial ratios to identify whether a company has manipulated earnings. In 1998, students from Cornell University University correctly identified Enron as an earnings manipulator using the M-Score, where experienced financial analysts failed to do so. (Enron filed for bankruptcy in late 2001.

The M-Score is displayed in a bullet chart. A score above -2.22 indicates a strong likelyhood of earnings manipulation.

If you want to understand how the M-Score is calculated, you can click on the details link at the top right corner of the Beneish M-Score panel. This opens the M-Scorecard.

Beneish M-Score Details

This scorecard shows all 8 signals used to calculate the score and an explanation in plain English. You can drill down on a particular signal and see how it's calculated.

This field allows you to quickly navigate to the scorecard of another company. When you start typing the name of a company, a list of options will show up. You can select a company from the list

• by clicking on it, or
• by selecting it using the up & down arrows and pressing the 'tab' key

After selecting the company, the scorecard will be reloaded with the values for this company.

The ERP5 ranking is our home-brewed ratio based on the magic formula and ideas by the father of value investing and stock screening in general, Benjamin Graham. We developed this formula as we didn't want to find companies that just performed well during the last year, but also the 4 years before that. We also wanted to remove companies with a high price-to-book value. This formula worked very well in our backtests and it has become a favorite factor of many of our members.

The visualization is very similar to the magic formula. We display both the ranking and a star rating.

New! The ERP5 score is now fully dynamic and is calculated on a filtered stock universe using the filters specified in the Filter Menu. The filters taken into account are: countries, markets, industries, market cap, trading value, results age and currency.

Joel Greenblatt, one of the most successful hedge fund managers with a spectacular track record, created a very simple and effective formula that can easily be understood by even the most novice investors. He wrote a book about it called 'The little book that beats the market' where he explains the formula in detail. His advice sounds very simple: buy good companies at bargain prices and repeat this process every year.

Not exactly rocket science, but it works. To calculate the magic formula, we rank companies based on ROIC and Earnings Yield and then we rank them again based on the sum of the 2 rankings. A company with a magic formula score of 100 is in 100th position out of the stock universe of your last screen. The score is also displayed as a star rating.

New! The Magic Formula score is now fully dynamic and is calculated on a filtered stock universe using the filters specified in the Filter Menu. The filters taken into account are: countries, markets, industries, market cap, trading value, results age and currency.

If you want to get a better understanding of the components used in the Magic Formula calculation, you can click on the 'details' link on top of the chart. This opens the following screen:

Magic Formula Scorecard

First of all, this screen shows a small introduction to the Magic Formula and a useful link to the little book. It then displays a few visualizations that should help get a better understanding of the score:

• In the top right corner, you can see the same star rating as included in the main report. This star rating is based on a percentile calculation, grouping the (filtered) companies into 100 groups of equal sizes.
• In the middle left section, you can see the formula used to calculate Earnings Yield and ROC. It also shows the values for the selected company.
• On the right, you can see a histogram of each respective ratio. A histogram is a graphical representation of the distribution of data. It is calculated by - after removing the outliers - subtracting the minimum value from the maximum value and then splitting this into 100 buckets with equal intervals. Companies are then assigned to the different buckets, which are represented by bars in the chart. When you hover over a particular bar, a pop-up will display the mean of the bucket and the number of companies it contains. All bars are colored in yellow, except for the bar representing the bucket to which the selected company belongs. This is colored in red.

We think this new scorecard provides a much more intuitive assessment of a company's relative cheapness and quality. And since it uses the screener filters of your last screen, you can play around with these filters in the screener and see the effect on the histograms. This way, you can see the histograms for one or more sectors, countries, or selected company sizes.

Our scorecard provides a quantitative view of a specific company by combining the most potent factors and presenting them in an easy to understand presentation. You can get instant answers to the following key questions:

• How did the stock perform in the recent past?
• Is it undervalued compared to its peers?
• Is the company strong enough to survive, and are its prospects improving?
• Is it likely to manipulate its earnings report?
• What's the latest news on the company?

You will find answers to all these questions in our scorecard. Here's an overview of the main parts:

It's one thing to find undervalued stocks, but what tells you that this stock will return to intrinsic value in the near future? Companies might have relatively low earnings, cash flow or book value multiples, but often this low valuation is justified if for instance the company is getting hammered by competition and doesn't have enough economic moat to create a profitable business model.

Some of the most powerful screens like the O'Shaughnessy trending value screen avoid value traps by using a momentum factor. It looks for relatively undervalued stocks but tries to avoid value traps by only selecting the companies with the highest stock price increase over the last 6 months. If the stock price has been going up during the last months, something must be evolving in the right direction.

Our own backtests have shown that you get even better results by using momentum as a primary factor in your model, i.e. look for stocks that have gone up in the recent past but are still cheap.

We think momentum is important so we positioned it in the top left corner of our scorecard. You can see the price increase during the last 3, 6 & 12 months as well as the price range, i.e. where the stock price is now compared to its 52 week high and low.

Value Composites were introduced by O'Shaughnessy in the 4th edition of 'What works on Wall Street'. O'Shaughnessy found that instead of looking at individual factors on their own, returns were considerably higher when combining them together. He called them 'value composites' and created 3 versions of them. You can find more details on the calculation by clicking here.

O'Shaughnessy calculates his value composites on the full universe of stocks. A VC of 1 means that the company is in the 1% cheapest stocks in the universe. This however does not take into account the different characteristics of industries or sectors. The average P/E, P/B and other ratios can vary considerably as you can see in the following overview.

For this reason we created 3 alternative calculations for each of the VCs. Instead of calculating percentiles for each component over the entire stock universe, we calculated the percentile within the sector, industry group and sector. You can see the results in the grid below:

How should I read this?

This company is very cheap when looking at VC1 and VC2 (which adds shareholder yield as one of the factors). The VC3 is higher as the company buys back relatively little shares compared to all other stocks.

When we look at the industry column however, we see that the company is not that cheap after all and has a VC1 of 51. The VC3 increases to 70, which puts it in the red.

The overall conclusion is that the stock is cheap compared to all stocks, but this is partly because it operates in an industry where the ratios are typically lower than average. By making the VC industry, industry group or sector agnostic, you get a better view on whether it's a real bargain or not. Please note that we have not yet conducted any study on this so we don't know whether these group VCs have similar predictive capabilities as the pure VCs as designed by O'Shaughnessy.

New! All value composites are now fully dynamic and calculated on a filtered stock universe using the filters specified in the Filter Menu. The filters taken into account are: countries, markets, industries, market cap, trading value, results age and currency.

Our scorecard makes peer comparison very easy by calculating percentiles on each factor, relative to the sector, industry group, and industry. It does this by ranking the companies on this factor and then creating 100 groups (percentile) with the same number of companies.

Often it's quite useful to get a list of the other companies in the sector, industry group, or industry and see the factors for each company. To make this very easy, we added links to the sector, industry group, and industry at the top of the screen. Clicking on any of these links will open the screener and include only the companies belonging to this group.

You can use the functionality of the screener to add additional filters, change the sorting etc...

The next section on the scorecard provides a very quick health assessment based on balance sheet data. Joseph Piotroski designed the f-score as the sum of 9 binary health signals and he used this on low price-to-book companies to separate the dogs from the good prospects.

Our scorecard shows the Piotroski F-score on a bullet chart. This has different components:

• The orange bar shows the current Piotroski F-score
• The orange vertical line shows a target value
• Colored bands show the ranges

Below the chart we also show a quick interpretation of the F-score.

If you want to understand how the score was calculated, you can click on the 'details' link on top of the chart. This opens the Piotroski F-Scorecard which consists of 2 parts.

Piotroski F-Score Details

This scorecard shows all 9 signals and an explanation in plain English. You can drill down on any particular signal and see how it's calculated.

Piotroski F-Score History

The Piotroski F-Score is a point-in-time health assessment and a high score indicates that the company's prospects are improving. The score also changes over time and even if there are no studies proving any predictive value, it can be quite interesting to see how the Piotroski F-Score evolved over time. Open the 'History' tab to see the Piotroski F-Score and the 9 signals during the last 11 periods.

The next grid in the scorecard shows the same information for the key quality factors. The first 2 columns show the ratio and the value for the company. The next 4 columns show an easy comparison to all stocks and its peers in its sector, industry group and industry. For each column it displays:

• The percentile to which the company belongs. (Percentile 1 = the best group, percentile 100 = the worst group.) The values are shown on a meter which will also change color depending on its value. (less than 33 = green, greater than 66 = red)
• The median value for the specific ratio in the group.

In the screenshot above you can see that the company has a ROC of 12.06%, which is lower than the median of 14.8% for all stocks. When we compare it to its peers in the industry, it's actually more than 100 basis points higher. Looking at shareholder yield, the company distributes almost double of the median of all stocks. When we compare this to the industry however, we can see that it's not great compared to its competitors. The next ratio seems to indicate that the company has a sustainable competitive advantage. Gross profitability of 68% is significantly above the industry median of 25.8%. If you want to see all other stocks in the industry we provided a quick link at the top of the scorecard. Click here for more information.

New! The quality factors are now fully dynamic and calculated on a filtered stock universe using the filters specified in the Filter Menu. The filters taken into account are: countries, markets, industries, market cap, trading value, results age and currency.

Our stock scorecard is fully dynamic and calculates all scores based on the filters of your last screen. If the selected stock is not included in this screen, dynamic scores such as the Magic Formula, ERP5, and VCs will be blank.

Click on the link provided at the top of the scorecard to get an overview of the filters used in your last screen. The next column in this grid shows the results of a test on each individual filter and whether the company passed or failed the test. The last column displays the value for this specific company.

A company can be cheap overall, but how does it compare to the selected stock universe and its peers? To make this as intuitive as possible, we created the value factors grid. In the rows, you can find the key value ratios. In the second column, we provide these ratios for the selected company.

In the third column, we provide the percentile to which the company belongs when using the complete stock universe. (percentiles divide all stocks in 100 groups with an equal amount of stocks. 1 = best, 100 worst) This data is displayed using a meter, which turns green (red) when the value is below 33 (over 66). We also provide the median value for this ratio calculated over the full stock universe. (We use median instead of average to avoid that extreme values impact the value.)

In the next 3 columns, we provide the same percentiles and medians, but this time calculated only on the stocks belonging to the same sector, industry group, and industry.

In the screenshot above, you can see that while the company's P/S of 0.52 is quite low compared to all stocks, it is relatively high when looking only at the industry, where the average is 0.29. Another interesting piece of information is that its FCF yield is 48 basis points lower than the median of all stocks; however, compared to its peers in the industry, the FCF yield is 92 basis points higher. If you want to see all other stocks in the industry, there's a quick link at the top of the scorecard. Click here for more information.

Finally, if you want to see how a particular measure is calculated, hover over the value to see the formula tooltip:

If you want to read more about the selected measure, click on the 'Go To glossary' link. This takes you to the dynamic glossary where you can find background information about this measure, the formula, and the calculation for the selected company. You can also find links to components of this calculation. Here's an extract of the glossary:

New! All value factors are now fully dynamic and calculated on a filtered stock universe using the filters specified in the Filter Menu. The filters taken into account are: countries, markets, industries, market cap, trading value, results age and currency.

Our more experienced users typically have a few screens they have taken great care to build. They run them on a regular basis to see what stocks come to the surface. It's extremely easy to save your favorites to our cloud, upon which they become available on all your devices.

To save your screen, click on the 'Add to favorites' button in the grid toolbar. A popup window opens with 2 panels:

• The top panel allows you to select an existing favorite and overwrite it with your current screen.
• In the bottom panel you can create a new favorite by specifying a name and an optional description. Click on the 'Create' button to create your favorite.

All your favorites are available in the 'Favorites' item in the top menu. Clicking on 'Favorites' shows a list of all your favorites and a description. It also shows a 'Delete' button which allows you to remove the favorite from your list. Clicking on the name will open the favorite.

The next filter displays a list of countries covered by your subscription. By ticking one or more countries in this list, stocks that have their primary listing in a securities exchange based in those countries will display. The grid is refreshed after closing the list (by clicking outside of the list control).

There are 3 different options to select countries.

• Select countries one by one by clicking on the checkbox next to the specific country.
• Click on a subregion to select all countries belonging to that subregion.
• Click on 'Select All' to select all countries in the list.

For each of these options, clicking a second time will remove these countries from the screener. If you selected a lot of countries, just hovering over the picklist will display a list of the countries selected.

The countries filter will also be used to define the stock universe on which relative scores such as the Magic Formula, the ERP5 and the O'Shaughnessy Value Composites (VC1, VC2, VC3) are calculated.

Please note that the filter will only show the markets covered by your subscription.

The currency picklist allows you to specify in which currency you want the filters to be applied. If you select 'USD' for instance:

• The filters on market cap and value traded will be executed in dollars.
• The amounts displayed in the grid (Enterprise Value, EBIT, Net Debt, Excess Cash,..) will be converted to USD.

By converting the values, it becomes very easy to compare companies even if they report their quarterly results in different currencies. We always take the currency exchange rate of the prior day to calculate the amounts.

The currency will also be used to define the stock universe on which relative scores such as the Magic Formula, the ERP5 and the O'Shaughnessy Value Composites (VC1, VC2, VC3) are calculated.

The filter menu provides a set of filters where you can do a first level filtering on the data. You can:

• Search the entire data universe for a specific stock.
• Select a specific watchlist.
• Specify which markets, countries and industries should be included or excluded.
• Filter out stocks above and/or a below a specific market capitalization.
• Remove stocks with below a certain liquidity level.
• Filter out stocks for which the company didn't publish any results during the last x months.
• Specify in which currency you want the filters to work.

These filters - excluding the stock search and watchlist - will also define the stock universe used to calculate relative scores such as the Magic Formula, the ERP5 and the O'Shaughnessy Value Composites (VC1, VC2, VC3).

Finally, you can set up 4 levels of hierarchical filters that allow you to start from a subset of the data, create a subset from this subset, and so on.

The filter menu is available as a slide-in panel. You can open it by clicking on the 'Filter Menu' button in the top menu bar. Once you have set these filters you can close the menu by clicking on the 'Filter Menu' option again.

The following section describes each filter individually:

Most strategies have specific guidelines on which industries should be excluded. The Greenblatt Magic formula for instance excludes financial companies and utilities. We don't remove any companies from our base set but specific industries can be removed very easily using this filter. There are 3 ways to do this:

• Tick the checkbox next to the specific industry.
• Click on the industry group to select all industries belonging to this group.
• Click on 'Select All' to select all industries.

The screener grid is refreshed after every closing of the list (by clicking outside of the list control).

The industries filter will also be used to define the stock universe on which relative scores such as the Magic Formula, the ERP5 and the O'Shaughnessy Value Composites (VC1, VC2, VC3) are calculated.

We use the global industry classification standard (GICS) developed by MSCI and S&P. The GICS structure consists of 10 sectors, 24 industry groups and 67 industries. For more information, visit this link. You can also use our GICS hierarchy page to drill through the hierarchy and see the companies in a specific industry, industry group or sector.

To see a summary of all selected industries, just hover over the industries filter.

You can very easily limit the size of the companies by using the market cap filter. If for instance you want to select only small cap stocks according to the US definition, set the minimum to $300m and the max to $2,000m. Updates are immediately reflected in the screener grid and you can see companies added or removed as you update these threshold values. (Click outside of the control for the changes to take effect)

The currency used in the market cap filter can be selected in the currency filter covered below.

The market cap filter will also be used to define the stock universe on which relative scores such as the Magic Formula, the ERP5 and the O'Shaughnessy Value Composites (VC1, VC2, VC3) are calculated.

A common requirement is to be able to filter out companies listed on specific markets. Stocks listed on security exchanges such as the 'Pink sheets' and the 'OTC bulletin board' are often not suited to be included in value investing models. Since these markets are not regulated, the companies don't have to meet the same stringent financial reporting requirements and it's often quite difficult to get reliable financial information.

Excluding stocks that have a primary listing on these markets is very easy and can be done by unticking the specific market(s). You can see the changes reflected in the grid once you close the list (by clicking outside the list control). To see a summary of all selected markets, just hover over the filter field.

The market filter will be used to define the stock universe on which relative scores such as the Magic Formula, the ERP5 and the O'Shaughnessy Value Composites (VC1, VC2, VC3) are calculated.

Please note that the filter will only show the markets covered by your subscription.

Increased demand for illiquid stock will almost immediately drive prices up, making it impossible to build a position at a fair price. It will also be very difficult to sell positions in illiquid stocks as sell orders will bring prices down. To remove these stocks, just set the minimum daily traded value filter. Setting it to 50k for instance will remove all stocks with an average daily trading value (close * volume) of less than 50k during the last month. The currency used for this filter is based on the currency filter explained below.

The minimum trading value filter will also be used to define the stock universe on which relative scores such as the Magic Formula, the ERP5 and the O'Shaughnessy Value Composites (VC1, VC2, VC3) are calculated.

One of the most powerful screening innovations was introduced by James P. O'Shaughnessy in the 4th edition of 'What works on Wall Street'. O'Shaughnessy found that by combining momentum and value factors, performance significantly increased while at the same time reducing risk. He found that a model based on first selecting the stocks with strong momentum and then selecting the 25 stocks with the highest yield was one of the best performing strategies in his study. We found very similar results in our study. By for instance selecting the top 20% book-to-market companies and then taking the shares in the top 20% price increase, the return over the 12 year period of the study was 1029%.

Running multifactor analysis can be very laborious in excel as you need to set filters, select a subset, copy it to another sheet and set another filter... In our screener you can set these screens up in seconds and get results almost instantly. We provide 4 filters and for each of them you can select a base and top level, each between 0 and 100. 0-10 means good, 90-100 means bad. Depending on the ratio, 0-10 brings back the highest (e.g. EBITDA Yield) or the lowest (e.g. Price-to-Sales) values.

In the example model below, we:

1. First select the top 20% stocks with the highest price increase during the last 6 months. This reduces the stocks in the screener from 32,052 stocks down to 6,314 stocks.
2. Then, from this subset, we select the top 20% stocks ranked by shareholder yield. This leaves 1,263 stocks in our selection.
3. We filter the 1,263 and only keep the 20% stocks with the highest EBITDA Yield. This brings back 253 stocks.
4. Finally, we select the top 20% stocks ranked by Book-to-market. This keeps only 51 stocks in our selection.

These 51 stocks can be further filtered or ordered in the screener grid on the right. This might leave even less stocks in the grid. Please note that the above example is for demonstration purposes only. We don't have any studies showing that this particular model is beating the market.

Our powerful screener is able to update the results in real-time as you're changing the factors and the % ranges.

The screener grid always displays the number of records selected based on the filters set in the filter menu or directly on the columns in the grid. The 'number of records displayed' and the 'total number of records are shown at the bottom right of the grid. To make this information available when you have the filter menu open, we added a 'Number of records' field at the top of the menu. This field automatically gets updated whenever the filters are updated.

This filter allows you to remove companies that have not posted quarterly results recently. By setting this to 'Last 6 Months', you limit the stock selection to only contain companies that have posted quarterly results with an end date in the last 6 months. This helps keep the screening results fresh and removes companies that have been delisted or are no longer filing quarterly results.

The results age filter will also be used to define the stock universe on which relative scores such as the Magic Formula, the ERP5 and the O'Shaughnessy Value Composites (VC1, VC2, VC3) are calculated.

You can easily search a specific company using the 'Company Search' field. When you have a value in this field, the screener will ignore all other filters. When you start typing the name of a company, a list of options will show up. You can select a company from the list

• by clicking on it, or
• by selecting it using the up & down arrows and pressing the 'tab' key

After selecting the company it will show up in the screener grid if the company has its primary listing in a country that is covered by your subscription. If this is not the case the screener grid will be empty.

Use this filter to show only the stocks belonging to a specific watchlist. When you select a watchlist here, all other filters are ignored except the 'Company Search' filter. You can remove this filter by selecting the first blank item in the list.

If you want to remove the filters and custom sort in the table grid, just click the 'Reset Table Filter' button.

Column filters can be added as part of your model (e.g. Piotroski F-score > 6) but they're also often used to filter out extreme or negative values (e.g. P/E < 0).

To add a column filter, click on the filter icon in the column header. A filter menu will show where you can specify up to 2 filters for this specific column. The filter menu will show different options for numbers, dates or text. After entering the filters, click on the 'Filter' button.

You can set filters on as many columns as you need. When a column is filtered, the filter icon in the column header will have a white background. Filters can be cleared in 2 ways:

• By clicking on the filter icon in the column header and pressing the 'Clear' button.
• By clicking on the 'Reset Table Filters' button in the table toolbar. This will remove all filters and sorts from the grid.

When using a template, columns that are used to filter or sort the data will be shown on the left. You can very easily customize the order of the columns by using our drag and drop functionality:

• Click on the column header you want to move and keep the mouse button down.
• Move the column on top of the column where you want it to be moved. As you move the column you will see the header and a plus sign on the left.
• Release the mouse button. The column is added to the left of the column on which you dropped it.

When you move columns, the new order will be stored in the cloud. When you open the screen later on the same or another device, the same order will be displayed.

By default, each column has been preconfigured to have an optimal width depending on the header and content. You can also change the column width to for instance show more columns on your screen.

Position the mouse cursor on the line that divides 2 column headers. When the cursor transforms into the resize cursor, click the left mouse button and hold it down. Drag the cursor to the left or right and release the mouse button to set the new width.

Most models rank the data set by a specific column. For the Greenblatt Magic Formula screener for instance, results are sorted ascending on the Magic Formula ranking.

Sorting the values in the screener table is very easy:

• Click on the column header once to sort ascending, i.e. from low to high.
• Click again to sort descending.
• Click again to remove the sort.

Sorting is only allowed on 1 column at a time, so other column sorting will be removed after you apply sorting to a specific column. The column header will show a small triangle if it is used to sort the table data.

Our screener table provides a wide range of ratios calculated by our engine, but we don't provide any raw data or detailed financials. To make it easy to get this data, we provide links to the company pages of some of the leading financial sites. Just click on the triangle next in the first column of the table to open up the 'Stock Information' sheet.

One of the most popular factors in our screen is the Piotroski F-Score which is a combination of 9 balance sheet-based health signals. To see how the company scores on each of the signals, you can open the Piotroski F-Score tab.

The company details slide-down pane provides stock information and quick links to websites that have more financial data. If however you want to dive a bit deeper into the stock and get a more graphic representation of the most important factors, you can click on the scorecard icon.

Our stock screener comes with a set of popular composite factors out of the box such as the Greenblatt Magic Formula, ERP5, and the O'Shaughnessy Value Composite. By ranking on a combined score based on multiple factors, one can easily find stocks that rank relatively well on both factors. The Greenblatt Magic formula for instance ranks companies based on ROC and Earnings yield and then sums up the 2 ranks. It then ranks companies on the combined score. Companies with a bad ROC or Earnings Yield tend to drop to the bottom of the screener, so you're left with companies that score quite well on the 2 different ratios. As opposed to multifactor analysis, all factors get equal weight in the overall result.

But what if you want to use a different set of factors or just build a customized version with for instance an extra factor like price index 6M? This is now possible by using custom factors.

Click on the Custom Factors button to open the custom factors window. Click inside the factor 1 textbox and select a factor from the list. You can repeat this process and add as many factors as required. After selecting the factor, you can pick the calculation method. We support 2 calculation methods:

• Value: each factor will be ranked individually first. In a second step, the sum of the rakings is ranked again. Companies where one of the ratios is missing automatically get a bad score. Examples of factors that use this calculation method are the magic formula and ERP5.
• Percentile: Each factor will be used to create 100 buckets with the same number of stocks. (percentiles) Group 1 contains the best stocks, group 100 contains the worst. A company for which this ratio is missing will get be put in group 50. The sum of the percentiles of the different ratios is used to again create overall percentiles. Examples of factors that use this calculation method are O'Shaugnessy's VC1, VC2, and VC3.

We provide 2 custom factors that act just like any other column in the list. To display these custom factors, you use the display/hide column functionality. Custom factors can be used to sort the results in the grid, to set specific filters, to run multifactor analysis, etc...

Our screener is very versatile and offers a lot of features to slice and dice the data in all possible ways, but some of our more advanced users like to filter and sort the data in excel. Other users like to keep a record of their favorite screens at regular intervals. Our table has a 'Export to Excel' functionality, which creates a formatted spreadsheet in the local settings of the user.

• Click on the 'Export to Excel' button at the top of the table.
• The spreadsheet will show in the download section of your browser. (which is usually at the bottom left of the browser window.)
• Click on the spreadsheet to open it.

Please note that depending on the amount of data selected in the screener and the internet bandwidth, it can take a few seconds to download a workbook. During the download, there's no feedback that the process is running. Please be patient and wait until the download is completed.

Exporting works on all browsers and is not restricted. An export workbook contains 3 sheets:

• Results: this sheet contains the full dataset with only the columns visible in the screener. It also uses the same column order and sorts the data based on the column used to sort in the screener.

• Filters: this sheet provides a list of all filters used in the screener.

• Custom Factors: this sheet shows the type and the different factors used for the configured custom composite factors.

After setting up your base filters in the 'Filter Menu', you can add additional filters and sort on a specific column. As explained in the multifactor analysis section above, our screener implements multifactor analysis as described by James O'Shaughnessy in the 4th edition of 'What works on Wall Street'. Instead of applying all filters on the basic data set as most other screeners do, we provide the ability to apply filters in levels. The results set retrieved by applying the filters defined in the filter menu can be fine-tuned further by adding one or more column filters in the grid. The results can also be ordered on a specific column, allowing you to select the top x stocks.

Our screener table can also be customized to build the exact screener you want. You can show or hide the columns you want to include, reorder the columns and specify the number of records you wish to include. The table is extremely fast as it only loads the data displayed on the screen, but you can also export the full result set to excel.

The table automatically adjusts to the width of the screen and shows as many columns as can be fitted on your screen. You can fine-tune the width of individual columns to show more columns if needed.

Here's an overview of the main features of the grid:

In the next section we will discuss all features in detail:

Most of the models advocate buying the top 20 or 30 stocks in the selection. To make this very easy, we added a 'Page Size' selector at the bottom of the grid that allows you to specify how many records should be shown per page. We also provide an easy navigator that takes you to the other pages.

This setting can also be used to tailor the screener to your screen height. By setting it to a matching height, you can avoid having to scroll up and down the list.

The screener table contains a lot of columns and you can scroll through them using the horizontal scrollbar at the bottom of the table. To be sure that you're looking at the right row you can select the row by clicking on any cell. This will highlight the selected row.

The data table contains quite a number of columns. Most of the columns might not be relevant so we offer an easy way to hide or show them:

• Click on the 'Select Columns' button in the menu at the top of the table.
• Open the relevant categories and check/uncheck the factors you wish to show/hide. The table will refresh as you're making the changes.
• Close the 'Add/remove columns' window.

The Show All button will show all columns.

The Hide All button will hide all columns in one click. This makes it quicker to select the specific columns you want to see.

The companies listed for a specific screen will change over time. As a result of stock price updates or new results, a stock could fall outside the limits of a filter. And since our screener works up to 6 levels of filtering, it might be difficult to find out exactly why the company no longer shows up in the list.

There are different categories of filters:

• Template filters: some templates like the Enhanced Dividend Yield screen sets additional filters in the background. These filters cannot be altered.
• Menu Filters: this - in combination with the template filters - defines the initial stock universe against which all calculations will be performed. Find more info here.
• Multifactor analysis filters: this allows you to create filters in layers. Find more info here
• Table Filters: this allows additional filters to be created for all columns in the screener. Find more info here.

The filters are applied in different levels:

1. Template and menu filters define the first level of filtering. Within the menu filters, when security filter is filled in, all other filters will be ignored. Watchlists is the second most important filter and if this is filled in, all other filters will be ignored. Then the other template and menu filters will be applied.
2. Mulitfactor analysis filters will be applied on the stock universe created by the filters above. First factor 1 is applied. This set is used as the basis for factor 2, etc... This step allows users to add up to 4 levels of filtering based on a ratio and percentile ranges.
3. Table filters are based on the set created by applying the 2 groups of filters explained above.

To see a list of all filters, just click on the 'Where is my company' button. This brings up a window listing all different filters. To find out why a certain company is not listed in the screen, just start typing the company name in the provided textbox. While typing the name, a list of companies will show. Click on the specific company or click on tab to select it. This runs a batch of tests that will reveal why the company dropped out or why it's included.

The testing algorithm is performance optimised and will only perform tests if the previous layer of filtering doesn't already remove the stock. For example if a stock is not included in the countries filter, it's not worth checking the multifactor analysis as the stock would not be included in the base set used by the multifactor analysis. Similarily, it will not test any table filters if the stock was already filtered out based on template filters, menu filters or multifactor filters. The only exception to this rule is filters on name or watchlist. In this case it will still run the tests even if the stock doesn't appear in the watchlist or doesn't have the name set in the name filter.

For each line in the filters list, the following information will be displayed:

• Filter: the name and type of the filter.
• Value: the value of the filter defined in the template or set by the user.
• Test: the results of the test. 'Pass' means that the company meets the filter criteria. 'Fail' means that the company does not meet the filter criteria. 'Not Tested' indicates that the test was not necessary since the company was already excluded by an upstream filter.
• Security value: this shows the value for a specific company. For menu filters or table filters, this is the value for this specific company. For factors, this will show the percentile in the subset to which the company belongs.

Ever since we started offering the stock screener, our objective has been to make it really intuitive, easy and available to all types investors. We want to provide everyone with the capabilities to run these models on the markets they can invest in and get instant results.

The easiest way to start using the stock screener is to start from a template. Open the templates by clicking on the 'Templates' menu item. This will display a list of templates created by the top gurus in the industry. Every screener has a short description and a hyperlink. Clicking on this link will reload the screener using this template.

Each template comes with preset filters in the filter menu and/or the screener grid. For some filter such as 'Countries', it will keep the countries you had selected before running the template.

It's also very easy to create your own watchlists of stocks you want to check on a regular basis. Instead of using the 'Search company' filter to look for each company individually, you can create your own lists. The watchlists then become available in the 'Watchlist' filter in the filter menu.

To create a new watchlist, hover over the watchlists menu item in the top menubar. The watchlist create/edit window will appear.

Enter a name and an optional description. After clicking on the 'Create' button, the watchlist will show up in the top part of the window. Click on the 'Edit' button to add stocks to the list.

All watchlists you create become available in the watchlist filter. Open the 'Filter Menu' and select your watchlist in the watchlist filter. The screener grid will be refreshed and only show the companies on the watchlist. Please note that if you add companies that are not covered by your subscription, these will not be displayed.

It should be noted that relative scores such as the Magic Formula, the ERP5 and the O'Shaughnessy Value Composites (VC1, VC2, VC3) will only be calculated if they're included in the selected stock universe. In the example below, the universe is restricted to the US and Canada, and as a result, these scores will only be calculated for the stocks that have their primary listing in these countries. Adding other countries or changing the market cap filters will recalculate and display the relative scores for all stocks belonging to the updated stock universe.

The enhanced dividend yield strategy was developed by Jim O'Shaughnessy to provide a fixed income strategy based on stocks instead of bonds. O'Shaugnessy argued that while bonds appeal to investors because of their inherent principal protection advantage, they have a number of important disadvantages.

• First of all, bond yield remains fixed, i.e. you will receive the same coupon over the entire period until maturity. This is ok in periods of low inflation, but between 1970 and 2010 inflation has on average been at 4,45%. That means that something that cost 1 dollar in 1970 costs $5,75 in 2010.
• Secondly, with bonds your principal doesn't depreciate, but it also won't appreciate. When the bond matures, you will get back the principal amount, and due to inflation this will be worth a lot less.

To remedy these issues of the traditional fixed income strategies, O'Shaugnessy designed a quantitative investing strategy based on stocks, with as primary objectives a growing yearly income combined with capital appreciation. The results of his study show that by implementing the enhanced dividend yield strategy, yearly income would have increased by 10%per annum and between 1962 and 2009, the principal increased by 5538%. What's more, this strategy never had a five-year period in which it lost money, very enticing for risk-averse investors.

How does it work?

O'Shaughnessy created a dividend yield strategy with a twist. Sometimes high yielding stocks are value traps and this strategy tries to get rid of these stocks in 2 ways.

1. First he limits the stock universe to market leading companies, that accourding to O'Shaughnessy have the following characteristics:
   1. Non-utility stocks
   2. Shares outstanding > dataset average
   3. Cash flow > dataset average
   4. Sales > 1.5 times dataset average
2. Next he excludes the bottom half of these stocks ranked by their EBITDA/EV. This way he only keeps the 50% of market leaders with the best financial conditions.

Finally, he builds a portfolio in which he overweights the stocks with the highest dividend yield, in the following manner:

• 25% of stocks with highest yield get 1.5 times the weight,
• the next 25% by yield get 1.25% the weight,
• the next 25% get 0.75 the weight,
• and the final 25 get 0.5 times the weight

The portfolio should be rebalanced every year.

You can read more about this strategy and the results by clicking on this link. This strategy is quite cumbersome to calculate for small investors, but with our screener it becomes a breeze. Just select the high dividend yield template screen, select your countries, and the screener will show the list of stocks for the dataset of the selected countries.

In their book ‘Quantative Value: a practitioner’s guide to automating intelligent investment and eliminating behavioral errors’, authors Wesley Gray and Tobias Carlisle discuss some of the most popular quantitative investing screens and factors. For each model, they also present an alternative that outperforms the original model. One of the improved models is what they call ”Quality and Price”. This model is based on the same ranking method as the Magic Formula, but it uses a different quality and price factor. Gray and Carlisle got the idea to replace the factors based on 2 academic papers.

Quality

The Greenblatt Magic Formula uses return on capital (ROC) as a proxy of a stock’s relative quality. The problem with ROC is that it’s not a very clean measure of a firm’s profitability. A firm that quickly grows its sales by spending heavily on marketing or R&D will see its short term profitability impacted. Moreover, management can implement actions to increase short term profitability while putting long-term profit growth in jeopardy. In his paper ‘The Other Side of Value: Good Growth and the Gross Profitability Premium’ Robert Novy-Marx suggested to use gross profitability, which is a much cleaner measure of a firm’s true economic profitability. Find more about this measure in our glossary.

Price

The Magic Formula uses EBIT/EV as its price measure to rank stocks. The problem with this measure is that it can vary significantly from period to period. For this reason, Nobel price winner Eugene Fama and Ken French consider book to market capitalization to be a superior metric as it varies less. This is important to keep turnover down in a value portfolio. Find more about this measure in our glossary.

Backtesting results

In their tests, the quality and price model significantly outperformed the magic formula. Between 1964 and 2011, the quality and price model showed an average yearly compound rate of 15.31% compared to 12.79% for the Magic Formula. This model also had higher volatility and worse drawdowns, but on a risk-adjusted basis it was the clear winner.

Joel Greenblatt introduced the magic formula in 2005, in his bestseller 'The little book that beats the market'.

How does it work?

The strategy looks for companies with the following characteristics:

• You can purchase the shares at a bargain price.
• The business excels at making money.

Can you give an example of a magic formula company?

Let's take Altria, the holding with subsidiaries that include Philip Morris USA. Altria performs particulary well on 2 ratios:

Earnings Yield

To find companies that are trading at bargain prices, we look at the earnings yield.

In the chart above, you can see all US companies excluding utilities and financial companies. Altria's earnings yield is 9.98%, which puts it in the red bar. Out of 3228 companies, only about 100 of them have a higher earnings yield.

Why earnings yield instead of Price/Earnings?

Earnings yield is not widely available and you have to use a paid service like ValueSignals to get reliable numbers. We could have used the more popular and available price-to-earnings ratio, but there are very good reasons why Greenblatt doesn't use rely on it:

1. Companies are taxed at different levels, so we have to look at pre-tax earnings to compare them. Operating earnings or EBIT is a much cleaner way of looking at the company's earnings.
2. Companies have different levels of debt, which is not considered when looking only at market capitalization. Enterprise value is the closest measure of the value of a company's total value.

ROIC

To find companies that excel at making money, we use the Return on Invested Capital (ROIC), more commonly known as ROC. Let's have a look if Altria excels in this area.

As you can see, Altria is very good at making money. It's at the top of the histogram, and only a few companies do better.

Why is ROC this high?

To calculate ROC, we divide the operating income by the sum of net fixed assets and net working capital.

• We already used Operating income in the earnings yield. For the last 12 months, the company generated 11bn.
• Altria only needed 2 billion dollars in cash to purchase fixed assets to conduct its business over the same period.
• The company is in excellent financial health and did not need any additional capital to cover its short-term obligations.

For every dollar Altria spent, it generated 6 dollars. Only very well-run companies can achieve results like these.

How can I find more magic formula companies?

It's pretty simple. You can use Joel's stock screener, which is available at magicformulainvesting.com. Just select whether you want the top 30 or 50 companies, and the list pops up in seconds. There are a few issues.

1. Security. This site has been compromised, and all users have been asked to change their passwords. The site is just a companion to sell more books and has not been updated for quite a long time.
2. The results are very restricted. As you can see in the screenshot above, you get the company name, the ticker, the market cap, the stock quote price, and the date of the most recent quarter. You don't get the magic formula score, the earnings yield, the ROC, or any other information.
3. It only covers the US.
4. You can't add any filters. For instance, looking at the US, I might want to exclude the pink sheets or look at Nyse. Or I might need to increase the minimum market Cap.

Another way is to use the ValueSignals stock screener.

As you can see in the screenshot above, you can run this screen on the US alone, but I also included Canada. I removed the pink sheets and OTC. In the screenshot, you can see the magic formula score, the ROC and the earnings yield. On top of that, you can also see other factors, such as the Piotroski F-score. Finally, I can drill down on the company to see the reports we already discussed in the section above for Altria.

What filters do we use?

For the template screen, we implemented the following filters

• Market Cap > $50m
• All stocks, excluding utility and financial stocks

Why is your list slightly different from MagicFormulaInvesting.com

In general, the same companies show up in the top 50 of both stock screeners. There are differences, however. Our list, for instance, features Aaron's holdings, a company that is not on Greenblatt's list. The explanation is quite simple. Even if Joel Greenblatt published quite some detail about his calculation, he did not disclose everything. To calculate net working capital, he uses excess cash. Unfortunately, he did not disclose the formula he used to calculate it. We tested many different formulae and used the one that created the biggest overlap between the lists.

Combining the Magic Formula with other value indicators

Studies have shown that while the magic formula works on its own, it's even better to combine it with other factors. In our research, we found that it works even better when combining it with a momentum factor. The one that worked best in our test was the 6-month price index. You can read more about this here.

In the fourth edition of his bestselling value quant book 'What works on Wall Street', James O'Shaughnessy devised a new screen which is called "the top stock-market strategy of the past 50 years". Instead of focussing on a particular ratio, he ranks companies according to 5-6 ratios and then combines this with a momentum factor.

How does it work?

First the companies are split into 100 groups (percentiles) based on the following ratios:

1. Price-to-Book
2. Price-to-Sales
3. EBITDA/EV
4. Price-to-Cashflow
5. Price-to-Earnings
6. Shareholder Yield

If a company's price-to-book ratio is in the lowest 1% of the dataset, it gets a score of 1. For some ratios it's the other way around, for instance EBITDA/EV. If a company belongs to the highest 1%, it gets a score of 1. If a value is missing, it gets a score of 50. We repeat the same calculation for each of the ratios and then sum up these values. Companies are again divided into 100 groups based on this score. This final result is called value composite. A value composite of 1 means that the company belongs to the 1% cheapest companies according to these factors.

In a second step, we select the top 10% stocks ranked according to this value composite score. Then he filters these stocks by a momentum factor, i.e. the 6-month price index. The result is an extremely cheap group of stocks that have been on the rise during the last 6 months.

“Trending Value is the top stock-market strategy of the past 50 years.”

James O'Shaughnessy

Alternatives

O'Shaughnessy tested 3 different value composite scores

1. VC1: based on the first 5 ratios only, excluding shareholder yield. By using this ratio his backtests showed a return of 17,18% annually.
2. VC2: based on all 6 ratios. O'Shaughnessy uses this ratio in his trended value screen since his backtests showed an improvement in overall annual compound return of 12 basis points to 17,3%, a reduced standard deviation, and downside risk.
3. VC3: same as VC2 but the last ratio is replaced by buyback yield. Some investors are indifferent whether a company pays out a dividend or want to avoid these since they can be very heavily taxed. This VC generates an even higher return of 17,39% annually but with a slightly higher standard deviation compared to the VC2.

The trended value screener template is based on VC2, but you can change this very easily to use VC1 or VC2 by adjusting the primary factor.

While O'Shaughnessy recommends using the VC as the primary factor and then apply a value ranking, you can also choose to switch it around. Instead of starting with the VC, select the 20% stocks with the highest share price increase during the last 6 months and then sort these by one of the VCs. We have been using this strategy for our European and US newsletter portfolios and this has allowed us to find some real jewels and significantly beat the market.

Many scientific studies confirm that buying a portfolio of low price-to-book companies will beat the market over time. This makes sense: you buy companies for less than what they're worth on paper. Other experienced investors, however, would argue that book value doesn't always provide an accurate picture of the company value. If you want a better understanding of the real value of the company, a full review of the assets is needed. This is true, of course. But the conclusions of the above studies can't be denied. Furthermore, studying these companies in great detail takes a considerable amount of time and the information necessary to perform an accurate estimate of all assets is not always available to all investors.

Joseph Piotroski, a professor in accounting at the Stanford University Graduate School of Business, had a closer look at the data used in these studies and found that in a portfolio consisting of the lowest price-to-book companies, the profits were generated by only a few stocks. In fact, 44% of the companies performed worse than the market. So he thought to himself: wouldn't it be great if I could find an easy way to filter out these companies?

Piotroski wondered whether he could remove these bad apples by looking at the company financial data for the last year. He devised a scoring system called the Piotroski F-Score, a 9 points scoring system based on profitability, funding, and operational efficiency. It looks at simple things such as: 'has the company made more profit compared to last year?' (+1 point) but also: 'is the company cooking the books by adjusting accruals?' (0 points). By using 9 points he was able to get enough signals to determine whether the company is really improving or not.

His research showed that his Piotroski F-score helped to predict the performance of low price-to-book stocks. In his backtests, he found that this strategy outperformed the market by 10% a year on average between 1976 and 1996. The tests also displayed that this was even more the case for small and medium sized companies. Piotroski attributes to the fact that these stocks are often outside of the radar of analysts and new information about a company doesn't get reflected in the share price as quickly.

You can read his influential 2000 paper by clicking on the following link.

“by selecting low Price-to-Book companies and by filtering out the best companies using a set of accounting signals, one could have generated a 23% average yearly return from 1976 to 1996.”

Joseph Piotroski

Our Piotroski screener selects the 20% lowest price-to-book companies and filters out the ones with an F-score of less than 7.

How do we calculate the F-score?

The F-score is the sum of 9 binary scores in 3 categories:

Profitability

1. ROA - Return on Assets: Net income before extraordinary items divided by total assets at the beginning of the year. 1 if positive, 0 if negative.
2. CFO - Cash Flow Return on Assets: Net cash flow from operating activities (operating cash flow) divided by total assets at the beginning of the year. 1 if positive, 0 if negative.
3. ΔROA - Change in Return on Assets: Compare return on assets to last year. 1 if it's higher, 0 if it's lower.
4. ACCRUAL - Quality of earnings (accrual): Compare cash flow return on assets to return on assets. 1 if CFO > ROA, 0 if CFO < ROA.

Funding

1. ΔLEVER - Change in gearing or leverage: Compare the gearing (long-term debt divided by average total assets) to the gearing last year. 1 if gearing is lower, 0 if it's higher.
2. ΔLIQUID - Change in working capital: Compare the current ratio (current assets divided by current liabilities) to the current ratio last year. A value higher than 1 indicates an increasing ability to pay off short term debt.
3. EQ_OFFER - Change in outstanding shares: The number of shares outstanding compared to last year. 0 if the number increased, otherwise 1.

Efficiency

1. ΔMARGIN - Change in Gross Margin: Current gross margin compared to last year. 1 if higher, 0 if lower
2. ΔTURN - Change in asset turnover: Compare asset turnover (total sales divided by total assets at the beginning of the year) to last year's asset turnover ratio. 1 if higher, 0 if lower.

To calculate this year's number we use the last trailing 12 months (TTM) number available. For last year we use the same number 1 year ago.

F-Score as secondary ratio

The F-Score is often used in combination with other screens. Our tests showed that if you filter the results of the ERP5 screen by only selecting companies with an F-Score of 7 or more, the return increases from 18,66% to 19,5% per annum. (1999-2010). Many of our members also like to use it in combination with the Greenblatt Magic Formula. We added both screens to our templates and called them the ERP5 Best Selection and the Magic Formula Best Selection.

In 1670, Isaac Newton concluded that “What goes up must come down”. Centuries later Werner DeBondt and Nobel prize winner Richard Thaler reported in their 1986 article, ’Does the stock market overreact’ in the Journal of Finance, that the same was true for the stock market. Most people tend to overreact to unexpected and dramatic news events and eventually stock prices correct.

They explained the overreaction effect as follows:

“If stock prices systematically overshoot, then their reversal should be predictable from past return data alone, with no use of any accounting data such as earnings. Specifically, two hypotheses are suggested: (1) Extreme movements in stock prices will be followed by subsequent price movements in the opposite direction. (2) The more extreme the initial price movement, the greater will be the subsequent adjustment."

De Bondt and Thaler tested this overreaction hypothesis by focusing on stocks that had experienced either extreme capital gains or losses over the last years. They tested this on data for the period 1926-1982, constructed loser and winner portfolios and evaluated the performance of these portfolios for a period up to 5 years after the portfolio construction. When using a 3-year look-back, the loser portfolio outperformed the market by 19.6% over the next 3 years. The winner portfolio underperformed by 5%. The difference between the loser and winner portfolios was 24.6%. They also examined portfolios formed on a 5-year look-back and found that the loser portfolio outperformed the past winner portfolio by 31.9% over the next 5 years. This phenomenon is also called the winner-loser effect and was the first attempt to apply a test for a behavioral principle to the stock market.

The authors also made the following observations:

• The effect was assymetric and much less pronounced on winners compared to losers.
• Most of the excess returns were realized in January. This is more widely known as the January effect.
• The results confirmed the claim made by Benjamin Graham, that the overreaction phenomenon mostly occurs during the second and third year of the test period.

Glen Arnolds, who published a paper proving the presence of the overreaction effect in the UK, also discovered that returns on the loser portfolio could be further enhanced by applying the Piotroski F-score.

The Return reversal with Piotroski template screen replicates the enhanced loser portfolio by going through the following steps:

1. First, we select the 10% stocks with the lowest price index over the last 5 years.
2. Then we filter out stocks with a Piotroski F-score below 6.

Although this theory was later confirmed by papers proving its effectiveness in markets around the world, it's a difficult one to put in practice. In her 1998 Wall Street Journal column headed 'Your money matters: investors' overreactions may yield opportunities in the stock market', Thaler was quoted saying:

It's scary to invest in these stocks. When a group of us thought of putting money on this strategy last year, people chickened out when they saw the list of losers we picked out. They all looked terrible...

De Bondt added:

The theory says I should buy them, but I don't know if I could personally stand it. But then again, maybe I'm overreacting.

Pim van Vliet is a Dutch portfolio manager for the quantitative equities team at Robeco. He’s the author of different scientific papers and books, primarily about low-volatility investing. In his book ‘High returns from low risk: a remarkable stock market paradox’ he devised a strategy that provides above-market returns by investing in low volatility stocks.

Low-volatility anomaly

The Capital Asset Pricing Model (CAPM) is widely used to predict the risk of investments. The model assumes that returns can be predicted by a linear model that uses volatility of the asset compared to the market. While this model was widely adopted throughout the finance industry, different academics soon challenged the assumptions used as they were not supported by empirical evidence. One of the first to do this was professor Robert Haugen, who questioned the methodology used to produce the supporting empirical evidence. (link: click here) Different studies confirmed Mr Haugen’s findings, however as it was counterintuitive, the investment community still uses the CAPM to this day.

Pim’s conservative stock formula

Pim built his strategy around this low-volatility anomaly or investment paradox, but he added two other factors into the mix.

1. First he added a value component as he wants to detect stocks that were temporarily ‘on sale’. Stocks that generate a higher income for their shareholders compared to the value of the company are preferred. Pim measures income as dividends and share buybacks.
2. Secondly, he added a momentum factor as some stocks might be cheap for a reason. Some stocks are value traps and even if they’re relatively cheap, there might not be any catalyst for recovery. As momentum factor, Pim uses the 12-month price index.

To get the list of stocks, Pim runs through the following steps:

1. Start from a universe of the largest 1.000 stocks available. (Pim uses only US stocks in his book)
2. Take the 500 stocks with the lowest volatility. (Pim uses 3-year volatility. In our template we use 2-year volatility as volatility over a 3 year period is not available)
3. Select the top 20% of these stocks based on the combined raking of shareholder yield and 12-month price index. Buy these stocks.
4. Repeat the process quarterly and rebalance the portfolio.

This formula selects:

low-risk companies that ‘conservatively’ deploy their capital, as they would rather distribute money to their shareholders than spend it on corporate activities themselves. The formula is also ‘conservative’ with regards to the timing. These stocks are only included when their business momentum improves and other investors have started to bid up their prices.

The author backtested this model over the period 1929-2015 and found that this model generated a 15% return per year. Compared to a portfolio of high-volatility stocks, it also proved to be more stable during more difficult periods.

Click here to go to Pim van Vliet's website.

The conservative formula in Valuesignals

Pim was so kind to provide us with guidance and feedback on how to best build his screen. We took the following steps:

• Set the minimum market Cap to $4,000m.
• Select the top 50% stocks with lowest volatility
• Create a custom factor based on shareholder yield and 1Y price index. Sort the results by this factor.

This screen was designed by the MFIE Capital team in 2010 and reveals companies with consistent earnings power for which the shares are trading at a considerable margin of safety. It can be seen as an extension of the Greenblatt Magic Formula as it uses the same calculation method and shares 2 ratios with the latter. The big difference is that it looks for companies that trade at a discount compared to book value and filters out companies that showed exceptional results during the last year. We made this screen available to all our users since it showed superior performance in our backtest.

“Our ERP5 screen generated a return of 18,66% annually in the period 1999-2010. By filtering out companies with an f-score less than 7, the return increased to 19,5% per annum.”

How does it work?

We rank companies based on 4 ratios:

1. Earnings Yield (EY): EBIT/Enterprise Value. This compares the earnings of a company compared to its theoretical purchase price. (market capitalization + debt) A company with a high EY can be purchased at a relatively low price compared to the earnings it generated during the last 12 months.
2. Return On Invested Capital (ROIC): EBIT / (Net Working Capital + Net Fixed Assets). A company with a high ROIC demonstrates that it's lean, i.e. it's able to generate high earnings compared to the money invested.
3. 5 year ROIC: Average ROIC during the last 5 years. Has the company demonstrated that it's been able to generate relatively high returns in a consistent manner in the past.
4. Price-to-Book: Market Cap/Common Shareholders Equity. How big is the margin of safety, i.e. the price you pay for a share compared to the book value of the company. Research shows that buying companies with a low price-to-book value generates superior returns. (e.g., Rosenberg, Reid-, and Lanstein 1984; Fama and French 1992; and Lakonishok, Shleifer, and Vishny 1994)

We rank each company on these 4 ratios and then sum up the rankings. We rank this score to get the ERP5 score.

We created a variation of this screen and named it the ERP5 Best Selection. This screen ranks the companies based on ERP5 score but also filters out ones with an f-score of less than 7. This way we only select companies for which the prospects are improving compared to last year. Adding this extra filter increased the yearly return in our 1999-2010 backtest by almost 1%, from 18,66% to 19,5%.