Guru Screens


Greenblatt Magic Formula

Joel Greenblatt is a successful hedge fund manager and adjunct professor at the Columbia University Graduate school. In 2006 he published the bestseller 'The little book that beats the market', a book he supposedly wrote to teach his children how to make money. In this book he encourages people to take control of their own money and invest it themselves. Most people entrust their money to investment professionals but Greenblatt observes that most of them don't beat the market. They make investing sound quite complicated but as Greenblatt explains, it's actually quite simple. He devised a very straightforward model that can be implemented easily by everyone and has proven to beat the market significantly in the past.

According to Greenblatt, you should be interested in 2 things when investing money into a business:

  1. Paying a bargain price when you purchase a share in a company. One way to do this is to purchase a business that earns more relative to the price you are paying. In other words, you should buy companies with a relatively high earnings yield.
  2. Buying good business rather than bad ones. One way to do this is to purchase a business that can invest its own money at higher rates of return. You should buy companies with a relatively higher ROIC.

Combining these 2 points is the secret to make lots of money.

By eliminating companies that earn ordinary or poor returns on capital, the magic formula starts with a group of companies that have a high return on capital. It then tries to buy these above-average companies at below-average prices.

Joel Greenblatt

How do we calculate the magic formula?

The formula is calculated based on 2 ratios:

  1. Earnings Yield: EBIT / Enterprise Value
  2. ROIC: EBIT / (Net Working Capital + Net Fixed Assets)

The individual components of this formula are calculated as follows:

  1. EBIT: The Trailing Twelve Month (TTM) EBIT
  2. Net Working Capital: if Total Current Assets exceeds Total Current Liabilities, Total Current Assets - Excess Cash - Total Current Liabilities, otherwise it is zero.
  3. Excess Cash: If Total Current Assets are greater than 2 * Total Current Liabilities, then Excess Cash is determined to be the lesser of Cash And Short Term Investments or Total Current Assets - 2 * Total Current Liabilities, otherwise it is zero.
  4. Net Fixed Assets: Total Assets - Total Current Assets - Total Intangible Assets
  5. Enterprise Value: Market Cap + Long-Term Debt + Minority Interest + Preferred Stock - Excess Cash

Rank companies based on each of these ratios individually. Make the sum of the results and rank this again. Magic Formula = Rank(Rank(Earnings Yield) + Rank(ROIC))

This formula doesn't work on all companies, so Greenblatt advises to set the following filters:

  1. Set Market Capitalization to a value greater than 50 million dollars.
  2. Exclude utility and financial stocks

Invest in the top 20-30 companies, accumulating 2-3 positions per month over a 12-month period. Re-balance the portfolio once a year. The formula won't beat the market every year, but should do if correctly applied over a period of 3 to 5 year.

Combining the Magic Formula with other value indicators

Studies have shown that combining the Magic Formula with for instance the piotroski F-Score increases return. If for instance you take the top 20% results of the magic formula and then take the 20% of stocks with the highest 6-month price index, the total return increases from 235% to 784% during the period 1999-2011. You can find more details about this in our latest paper.

For More info on our Greenblatt Magic Formula stock screener please visit the following link:

Greenblatt Magic Formula


Beneish M-Score

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, Lee & Nichols

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:

  1. 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.
  2. 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.
  3. The predictive power of the M-Score is most pronounced for low-accrual (ostensibly high quality-earnings) companies.
  4. 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.
  5. Abnormal returns are witnessed in the three-day windows centered on the next 4 earnings announcements.

How is it calculated?

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.

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. Depreciation Index (DEPI): The rate of depreciation in year t-1 / year t. The rate of depreciation is equal to depreciation / (depreciation + net property, plant & equipment). If this value is greater than 1 this means that the rate at which assets are depreciated has slowed down. Either management revised upwards the estimates of assets usefull lives or adopted a new method that is income increasing.
  6. 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 probablity of manipulation.
  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.
  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 undelies reported earnings. Higher positive accruals (less cash) indicates a higher likelyhood of earnings manipulation.

The calculation is as follows: M = -4.84 + 0.92*DSRI + 0.528*GMI + 0.404*AQI + 0.892*SGI + 0.115*DEPI – 0.172*SGAI + 4.679*TATA – 0.327*LVGI

A score greater than -1,78 indicates a strong likelyhood of earnings manipulation.

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 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.


O'Shaughnessy Trending Value

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

How does it work?

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

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

If a company's price-to-book ratio is in the lowest 1% of the dataset, it gets a score of 1. For some ratios it's the other way around, for instance EBITDA/EV. If a company belongs to the highest 10%, it gets a score of 1. If a value is missing, it gets a score of 50. We repeat the same calculation for each of the ratios and then sum up these values. Companies are again divided into 100 groups based on this score. This final result is called value composite. A value composite of 1 means that the company belongs to the 1% cheapest companies according to these factors.

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

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

James O'Shaughnessy

Alternatives

O'Shaugnessy tested 3 different value composite scores

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

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

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


Piotroski F-Score

Many scientific studies confirm that buying a portfolio of low price-to-book companies will beat the market over time. It makes sense: you buy companies for less than what they're worth on paper. More experienced investors would argue that book value doesn't always provide an accurate picture of the company value, and a full review of the assets will help get a better understanding of the real value. While this criticism is correct, one can't deny the conclusions of these studies. Furthermore, studying these companies in great detail takes a considerable amount of time and the information necessary to perform an accurate estimate of all assets is not always available to all investors.

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

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

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

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

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

Joseph Piotroski

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

How do we calculate the f-score?

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

Profitability

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

Funding

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

Efficiency

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

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

F-Score as secondary ratio

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

Other Screens


ERP5

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

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

How does it work?

We rank companies based on 4 ratios:

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

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

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