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:
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.
The formula is calculated based on 2 ratios:
EBIT / Enterprise Value
EBIT / (Net Working Capital + Net Fixed Assets)
The individual components of this formula are calculated as follows:
The Trailing Twelve Month (TTM) EBIT
Total Current Assets - Excess Cash - Total Current Liabilities, otherwise it is zero.
Total Assets - Total Current Assets - Total Intangible Assets
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:
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.
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
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 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.
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.
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.
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.
First we split the companies into 100 groups (percentiles) based on the following ratios:
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.”
O'Shaugnessy tested 3 different value composite scores
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.
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.”
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.
The f-score is the sum of 9 binary scores in 3 categories:
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.
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.
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.”
We rank companies based on 4 ratios:
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%.