The Z-Score for predicting bankruptcy was published in 1968 by Edward I. Altman, who was 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.

The Z-score is calculated as follows:

$$\mathrm{Altman\; Z-Score}=1.2*\mathrm{X1}+1.4*\mathrm{X2}+3.3*\mathrm{X3}+0.6*\mathrm{X4}+0.99*\mathrm{X5}$$

The 5 components used in the calculation are:

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

$$\mathrm{X1}=\frac{(\mathrm{Current\; Assets}-\mathrm{Current\; Liabilities})}{\mathrm{Total\; Assets}}$$

"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)**

$$\mathrm{X2}=\frac{(\mathrm{Common\; Shareholders\; Equity}-\mathrm{Common\; Stock})}{\mathrm{Total\; Assets}}$$

"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)**

$$\mathrm{X3}=\frac{\mathrm{EBIT}}{\mathrm{Total\; Assets}}$$

"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)**

$$\mathrm{X4}=\frac{\mathrm{Market\; Cap}}{\mathrm{Total\; Liabilities}}$$

"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)**

$$\mathrm{X5}=\frac{\mathrm{Net\; Sales\; or\; Revenues}}{\mathrm{Total\; Assets}}$$

"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.

The M-Score was created in June 1999 by Messod D. Beneish, professor at the Indiana University. It provides a quick and easy way to detect companies that are likely to have manipulated their reported earnings. In his most recent paper, he demonstrates that his model correctly identified, in advance of public disclosure, a large majority (71%) of the most famous accounting fraud cases that surfaced subsequent to the model's estimation period. 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 analist reports.

While very few companies get indicted for accounting fraud, the M-Score helps predict a firms future prospects. A typical earnings manipulator as defined by Beneish is a firm that (1) is growing extremely fast (extremely high year-over-year sales), (2) is experiencing deteriorating fundamentals (as evidenced by a decline in asset quality, eroding profit margins, and increasing leverage) and (3) is adopting aggressive accounting practices (receivables growing much faster than sales; large income-inflating accruals; decreasing depreciation expense). These companies are particulary risky to invest in as they're very likely to be overpriced (because of their high recent growth trajectory) and they exhibit a number of problematic characteristics (either lower earnings quality or more challenging economic conditions). These companies are more likely to disappoint investors in the future.

"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 companies that manipulate earnings. (click here for more info). In his more recent work, he reveals that the M-Score is also an excellent predictor of future stock returns. He summarized his main findings as follows:

- The firms with a higher probability of manipulation (M-Score) earn lower returns in every decile portfolio sorted by Size, Book-to-Market, Momentum, Accruals, and Short-Interest.
- The predictive power of M-Score is related to its ability to forecast the persistence of current-year accruals. High M-Score firms have income-increasing accruals that are much more likely to disappear next year and income-decreasing accruals that are more likely to persist.
- The predictive power of the M-Score is most pronounced for low-accrual (ostensibly high quality-earnings) companies.
- The variables that relate to the predisposition to commit fraud (higher sales growth, change in assets quality and increase in leverage) , rather than the variables associated with the level of aggressive accounting, are the primary drivers of the incremental power of the model.
- Abnormal returns are witnessed in the three-day windows centered on the next 4 earnings announcements.

The M-Score is based on 8 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 available as current year, year t. For year t-1 we take the TTM results for the 12-month period before year t.

**Days Sales in Receivables Index (DSRI)**: The ratio in 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.**Gross Margin Index (GMI)**: GM in year t-1 / year t. If A value greater than 1 indicates that margins have deteriorated and this signals a negative signal about firms' prospects.**Asset Quality Index (AQI)**: Asset Quality in year t / year t-1. 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.**Sales Growth Index (SGI)**: Sales in year t / year t-1. 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 have a significant impact on the value of the stock and be very costly to management. A value greater than 1 increases the probability of earnings manipulation.**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 upwards the estimates of assets usefull lives or adopted a new method that is income increasing.**Sales General and Administrative Expenses Index (SGAI)**: The ratio of SGA to sales in year t / year t-1. Analists would interpret a disproportionate increase in sales as a negative signal about firms future prospects. Beneish expects a positive relation between SGAI and the probability of manipulation.**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.**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.

The calculation is as follows: \mathrm{M-Score}=-4.84+0.92*\mathrm{DSRI}+0.528*\mathrm{GMI}+0.404*\mathrm{AQI}+0.892*\mathrm{SGI}+0.115*\mathrm{DEPI}-0.172*\mathrm{SGAI}+4.679*\mathrm{TATA}+-0.327*\mathrm{lvgi}

A score greater than -1.78 indicates a strong likelyhood of earnings manipulation.

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 not that familiar with the companies. Since a share of the companies discovered of manipulating earnings will eventually see their stocks plummet in value, it provides an extra security to filter these potential manipulators out of the screener.

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:

- Earnings Yield
- ROC
- 5 year ROC
- 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:

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

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.

- Price-to-Book
- Price-to-Earnings
- Price-to-Sales
- EBITDA/EV
- 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 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:

- Price-to-Book
- Price-to-Earnings
- Price-to-Sales
- EBITDA/EV
- Price-to-Cash flow
- 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 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:

- Price-to-Book
- Price-to-Earnings
- Price-to-Sales
- EBITDA/EV
- Price-to-Cash flow
- 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 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**

**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.**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.**ΔROA**- Change in Return on Assets: Compare return on assets to last year. 1 if it's higher, 0 if it's lower.**ACCRUAL**- Quality of earnings (accrual): Compare cash flow return on assets to return on assets. 1 if CFO > ROA, 0 if CFO < ROA.

**Funding**

**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.**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.**EQ_OFFER**- Change in outstanding shares: The number of shares outstanding compared to last year. 0 if the number increased, otherwise 1.

**Efficiency**

**Δ_MARGIN**- Change in Gross Margin: Current gross margin compared to last year. 1 if higher, 0 if lower**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 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...)

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:

$$\mathrm{Average\; Trading\; Value}=\mathrm{Average\; last\; 30\; days}(\mathrm{Share\; Price}*\mathrm{Volume})$$

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:

$$\mathrm{Enterprise\; Value}=\mathrm{Market\; Cap}+\mathrm{Total\; Debt}+\mathrm{Minority\; Interest}+\mathrm{Preferred\; Stock}-\mathrm{Cash\; and\; ST\; Investments}$$

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:

$$\mathrm{Excess\; Cash}=\mathrm{MAX}(\mathrm{Cash\; and\; ST\; Investments}-\mathrm{20\%}*\mathrm{Net\; Sales\; or\; Revenues},0)$$

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.

The 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:

$$\mathrm{Market\; Cap}=\mathrm{Share\; Price}*\mathrm{Common\; Shares\; Outstanding}$$

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.

$$\mathrm{Net\; Debt}=\mathrm{Total\; Debt}-\mathrm{Cash\; and\; ST\; Investments}$$

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.

$$\mathrm{Net\; Fixed\; Assets}=\mathrm{Net\; Property,\; Plant\; \&\; Equipment}$$

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.

$$\mathrm{Net\; Working\; Capital}=\mathrm{MAX(}\mathrm{Current\; Assets}-\mathrm{Excess\; Cash}-(\mathrm{Current\; Liabilities}-(\mathrm{Total\; Debt}-\mathrm{Long\; Term\; Debt}\left)\right)\mathrm{,\; 0})$$

The price index measures a stock's price change over the last months. Our stock screener provides the price index over the last 3, 6 and 12 months.

Formula:

$$\mathrm{Price\; Index\; 6M}=\frac{\mathrm{Adjusted*\; share\; price\; now}}{\mathrm{Adjusted*\; share\; price\; 6\; months\; ago}}$$** The share price is adjusted for stock splits, cash dividends, right offerings and spin-offs. *

The idea behind using momentum factors with valuation is to find the strongest stocks, i.e. the ones that are going up the most in price. O'Shaughnessy calculated relative strength by looking at the stocks' returns over the past year. (What works on Wall Street, 4th Edition, McGraw Hill) Winners seem to continue to win. According to O'Shaughnessy, the longer the period you consider, the more you will see the effect of regression to the mean. A complete regression to the mean happens on average after about five years. Stocks that have exhibited five years of strong relative strength are usually on the brink of a turnaround (positive or negative). This is the reason why we use 6 month PI as a primary factor for our newsletter. In our latest paper, you can see that this significantly increases returns. Click here to read more about this.

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's 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 to 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:

$$\mathrm{Buyback\; Yield}=\frac{(\mathrm{Common\; Shares\; Outstanding\; y-1}-\mathrm{Common\; Shares\; Outstanding})}{\mathrm{Common\; Shares\; Outstanding\; y-1}}$$

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 the performance.

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

$$\mathrm{CFO\; to\; Assets}=\frac{\mathrm{Cash\; Flow\; from\; Operations}}{\mathrm{Total\; Assets\; y-1}}$$

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

Dividend Yield is calculated as follows:

$$\mathrm{Dividend\; Yield}=\frac{(\mathrm{Dividend\; per\; Share}*\mathrm{Common\; Shares\; Outstanding})}{\mathrm{Market\; Cap}}$$

This factor was introduced by Richard Tortoriello, a senior quantitative analyist 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:

$$\mathrm{External\; finance\; ratio}=\frac{(\mathrm{Total\; Assets}-\mathrm{Total\; Assets\; y-1}-\mathrm{Cash\; Flow\; from\; Operations})}{\mathrm{Total\; Assets}}$$

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.

$$\mathrm{Gross\; Margin}=1-\frac{\mathrm{Cost\; of\; Goods\; Sold}}{\mathrm{Net\; Sales\; or\; Revenues}}$$

"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:

$$\mathrm{Gross\; Profitability}=\frac{(\mathrm{Net\; Sales\; or\; Revenues}-\mathrm{Cost\; of\; Goods\; Sold})}{\mathrm{Total\; Assets}}$$

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:

$$\mathrm{Net\; Debt\; to\; Market\; Cap}=\frac{(\mathrm{Total\; Debt}-\mathrm{Cash\; and\; ST\; Investments})}{\mathrm{Market\; Cap}}$$

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:

$$\mathrm{Percent\; Operating\; Accruals}=\frac{(\mathrm{Net\; Income}-\mathrm{Cash\; Flow\; from\; Operations})}{\mathrm{Net\; Income}}$$

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:

$$\mathrm{ROA}=\frac{\mathrm{Net\; Income}}{\mathrm{Total\; Assets\; y-1}}$$

*Note: Net Income includes extrordinary 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:

$$\mathrm{ROC}=\frac{\mathrm{Operating\; Income}}{(\mathrm{Net\; Fixed\; Assets}+\mathrm{Net\; Working\; Capital})}$$

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:

$$\mathrm{ROE}=\frac{\mathrm{Net\; Income}}{\mathrm{Common\; Shareholders\; Equity}}$$

*Please note that we don't remove the extrordinary items from the net income. It's very rare that transactions meet the requirements to be presented as an extrordinary item. For this reason, the concept extrordinary 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:

$$\mathrm{ROIC\; 5Y}=\frac{\sum \left(\mathrm{Operating\; Income\; last\; 5\; years}\right)}{\sum \left(\mathrm{Net\; Assets\; last\; 5\; years}\right)+\sum \left(\mathrm{Net\; Working\; Capital\; last\; 5\; years}\right)}$$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:

$$\mathrm{Total\; Asset\; Growth}=\left(\frac{\mathrm{Total\; Assets}}{\mathrm{Total\; Assets\; y-1}}\right)-1$$

Read the full paper here.

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:

$$\mathrm{BS\; Aggregate\; Accrual\; Ratio}=\frac{\mathrm{Balance\; Sheet\; Aggregate\; Accruals}}{\mathrm{Average}(\mathrm{Net\; Operating\; Assets},\mathrm{Net\; Operating\; Assets\; y-1})}$$

$$\mathrm{Balance\; Sheet\; Aggregate\; Accruals}=\mathrm{Net\; Operating\; Assets}-\mathrm{Net\; Operating\; Assets\; y-1}$$

$$\mathrm{Net\; Operating\; Assets}=\mathrm{Total\; Assets}-\mathrm{Cash\; and\; ST\; Investments}-\mathrm{Total\; Liabilities}-\mathrm{Total\; Debt}$$

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:

$$\mathrm{Capex\; on\; PPE}=\frac{\mathrm{Capital\; Expenditure}}{\mathrm{Net\; Property,\; Plant\; \&\; Equipment}}$$

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

$$\mathrm{CF\; Aggregate\; Accruals\; Ratio}=\frac{\mathrm{Cash\; Flow\; Aggregated\; Accruals}}{\mathrm{Average}(\mathrm{Net\; Operating\; Assets\; y-1},\mathrm{Net\; Operating\; Assets})}$$

$$\mathrm{Cash\; Flow\; Aggregated\; Accruals}=\mathrm{Net\; Income}-(\mathrm{Cash\; Flow\; from\; Operations}+\mathrm{Cash\; Flow\; from\; Investments})$$

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:

$$\mathrm{Cash\; flow\; on\; Capex}=\frac{\mathrm{Cash\; Flow\; from\; Operations}}{\mathrm{Capital\; Expenditure}}$$

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 favourable 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.

.$$\mathrm{Current\; Ratio}=\frac{\mathrm{Current\; Assets}}{\mathrm{Current\; Liabilities}}$$

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% companies with the highest FCF/debt ratio generated substantially higher returns compared to the market.

Formula:

$$\mathrm{FCF\; on\; Debt}=\frac{(\mathrm{Cash\; Flow\; from\; Operations}-\mathrm{Capital\; Expenditure})}{\mathrm{Total\; Debt}}$$

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.

$$\mathrm{LT\; Debt\; to\; Average\; Assets}=\frac{\mathrm{Long\; Term\; Debt}}{\frac{(\mathrm{Total\; Assets}+\mathrm{Total\; Assets\; y-1})}{2}}$$

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:

$$\mathrm{Book-to-Market\; Ratio}=\frac{\mathrm{Common\; Shareholders\; Equity}}{\mathrm{Market\; Cap}}$$

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

$$\mathrm{Price-to-Book\; Ratio}=\frac{\mathrm{Market\; Cap}}{\mathrm{Common\; Shareholders\; Equity}}$$

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:

$$\mathrm{Earnings\; Yield}=\frac{\mathrm{Operating\; Income}}{\mathrm{Enterprise\; Value}}$$

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 a different capital structure 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 explaines:

" 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 the 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 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:

$$\mathrm{EBITDA/EV}=\frac{\mathrm{EBITDA}}{\mathrm{Enterprise\; Value}}$$

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 behaviour, 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:

$$\mathrm{EV/EBIT}=\frac{\mathrm{Enterprise\; Value}}{\mathrm{EBIT}}$$

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 behaviour, 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:

$$\mathrm{EV/EBITDA}=\frac{\mathrm{Enterprise\; Value}}{\mathrm{EBITDA}}$$

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 behaviour, 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:

$$\mathrm{EV/FCF}=\frac{\mathrm{Enterprise\; Value}}{(\mathrm{Cash\; Flow\; from\; Operations}-\mathrm{Capital\; Expenditure})}$$

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 firms 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:

$$\mathrm{FCF\; Yield}=\frac{(\mathrm{Cash\; Flow\; from\; Operations}-\mathrm{Capital\; Expenditure})}{\mathrm{Enterprise\; Value}}$$

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:

$$\mathrm{NCAV}=\mathrm{Current\; Assets}-\mathrm{Total\; Liabilities}$$

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

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 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:

$$\mathrm{NCAV-to-Market\; Ratio}=\frac{\mathrm{NCAV}}{\mathrm{Market\; Cap}}$$

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

P/B or Price-to-Book 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:

$$\mathrm{Price-to-Book\; Ratio}=\frac{\mathrm{Market\; Cap}}{\mathrm{Common\; Shareholders\; Equity}}$$

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:

$$\mathrm{Price-to-Earnings\; Ratio}=\frac{\mathrm{Share\; Price}}{\mathrm{Diluted\; EPS\; excl.\; extra\; items}}$$

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:

$$\mathrm{Price-to-Sales\; Ratio}=\frac{\mathrm{Market\; Cap}}{\mathrm{Net\; Sales\; or\; Revenues}}$$

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:

$$\mathrm{Price-to-Cash\; flow\; Ratio}=\frac{\mathrm{Market\; Cap}}{\mathrm{Cash\; Flow\; from\; Operations}}$$

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