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 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 only to use it to assess manufacturers.

How is it calculated?

The Z-score is calculated as follows:

The five 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 against young companies on purpose, as 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 the 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 the 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 the ability of the firm’s assets. It is one measure of management’s capacity to deal with competitive conditions. "

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

Companies caught manipulating their earnings tend to see their stocks plummet. 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 company. 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 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 manipulating earnings. (click here to read his original paper.). In his 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 suggest 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 depreciation rate equals depreciation / (depreciation + net property, plant & equipment). If this value exceeds 1, the rate at which assets are depreciated has slowed. 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) indicate 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 turning around. It reported that its Q1 revenue was up 10.3%. EBIT increased from $-0.1 to $+10.9m.

The Beneish M-scorecard

As you can see, the total score is 0.19, 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 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. Last year's ratio was so low that any increase would significantly 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 guru focus 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 bitcoin has plunged 40% since mid-April. Investors fear 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, 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 allow you to combine your favourite factors and make these factors 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 using 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 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. Joel Greenblatt introduced this factor in his bestseller: 'The little book that beats the market'. In this book, he explained that to get above-average returns, you should buy companies with above-average returns on capital at below-average prices. To identify these companies, we rank the stock universe based on two 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.

                </ol>
                

We first rank these companies based on each ratio. 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 five 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 equal groups (percentiles) from 1 to 100. This is done on each ratio. If a company is in group 1, 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 five 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 than P/B. Stocks with a high B/M are at the top of the list, and stocks with a negative B/M are at the bottom. 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 grouped 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 than P/B. Stocks with a high B/M are at the top of the list, and stocks with a negative B/M are at the bottom. 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 grouped 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, and 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 than P/B. Stocks with a high B/M are at the top of the list, and stocks with a negative B/M are at the bottom. 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 than 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 was 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 used 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 showing all underlying values and how the Piotroski F-Score and the nine signals evolved during the last ten 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 headquarters 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 and sector it belongs to. We use the North American Industry Classification System (NAICS). 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 represents 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. (six months, nine months, etc...)

The closing price of the selected stock. Stock prices are updated every day, typically 1 hour after the 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 better distribution. Growth companies with an inverted PEG above two 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 ever, uses PEG as a 'number worth noticing'.

The P/E ratio of any fairly priced company will equal its growth rate...Generally, 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 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 by taking the annual earnings from Value Line or an S&P report and calculating the per cent 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 in 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 quickly. Conversely, companies that 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) measure 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 allows 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 claimants' claims: creditors (secured and unsecured) and equity holders (preferred and common). Think of it as the theoretical takeover price if the company gets 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 above 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 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 cash needed to purchase fixed assets necessary to conduct its business, such as real estate, plant and equipment. Greenblatt removes intangible assets, specifically goodwill, which usually arise from an acquisition of a company. These are historical costs, and should this does not need to be constantly replaced.

The money it has 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 the 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 behaviour of security returns, Narasimhan Jegadeesh reported a strong negative correlation between returns in subsequent months. He examined stock returns in 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 reliably reject the hypothesis that stock prices follow random walks. The 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 three 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 versions. 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 statistically significant. To calculate the biggest losers (winners), they formed portfolios with stocks that showed the biggest declines (increases) in returns during the past five years. They found that the loser outperformed the winners by 24.6% over the next three 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, it will continue to do well.

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 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 based on returns realized in the past six 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 six 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 six 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 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 to predict 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 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 the 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 the 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 per cent accruals was composed of different firms, which were three 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 efficiently management uses 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 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) characterizes growth companies. It measures the company's profitability, i.e. how much net income it can generate on the money its shareholders invested.

Formula:

We don't remove extraordinary items from the net income. Transactions rarely meet the requirements to be presented as extraordinary items. For this reason, the concept of 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 five years to smooth out the business and economic cycles, 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.

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 necessary 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 slow down revenue recognition or pay for future expenses to smooth earnings into upcoming quarters.

If management is increasing the number 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 monitoring and comparing accruals levels and created two ratios for this: Balance Sheet Aggregated Accrual Ratio (BS Accrual Ratio) and Cash Flow Aggregated Accrual Ratio. (CF Accrual Ratio) The first calculates the increase of Net Operating Assets compared to the average of the last two 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 using '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 to the Cash Flow Aggregated Accrual Ratio (CF Accrual Ratio), see the BS Accrual Ratio described above.

Another ratio S&P Analyst Richard Tortoriello recommends using 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 can 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 considered 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 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 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 number 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 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 include it in the scorecard as P/B is presented alongside the P/E, P/S and P/CF ratios.

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 aligns with what Joel Greenblatt uses. As numerators, 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 debt levels and 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% annually. It 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 to compare a company to its peers easily. For more information, click here.

This ratio compares stock prices with earnings smoothed over the last five 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 finances 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, which should 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 relatively low volatility.
• Gray & Vogel found the EBITDA/EV to be the best performing metric, outperforming investor favourites such as Price-to-Earnings, Free Cashflow to EV, and Book-to-Market in 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 to compare a company to its peers easily. 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. Similarly, stocks with a negative EBIT and a negative EV will likely 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:

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. Similarly, stocks with a negative EBITDA and 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:

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. Similarly, stocks with a negative FCF and a negative EV will likely 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:

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 can 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 five years to smooth out the business and economic cycles, 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 among the first to consistently screen the market looking for bargain companies based on value factors. He didn't have databases such as ValueSignals 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. Also, in more recent studies, it has proven to provide superior results. A study conducted by the State University of New York to prove the effectiveness of this strategy showed that from the period 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 to compare a company to its peers easily. 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. Still, 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 company's price 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 to compare a company to its peers easily. 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.

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 one is considered more volatile than the market; less than one 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 two 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 three 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 six months, annualized