Many scientific studies confirm that buying a portfolio of low price-to-book companies will beat the market over time. This makes sense: you buy companies for less than what they're worth on paper. Other experienced investors, however, would argue that book value doesn't always provide an accurate picture of the company value. If you want a better understanding of the real value of the company, a full review of the assets is needed. This is true, of course. But the conclusions of the above studies can't be denied. Furthermore, studying these companies in great detail takes a considerable amount of time and the information necessary to perform an accurate estimate of all assets is not always available to all investors.
Joseph Piotroski, a professor in accounting at the Stanford University Graduate School of Business, had a closer look at the data used in these studies and found that in a portfolio consisting of the lowest price-to-book companies, the profits were generated by only a few stocks. In fact, 44% of the companies performed worse than the market. So he thought to himself: wouldn't it be great if I could find an easy way to filter out these companies?
Piotroski wondered whether he could remove these bad apples by looking at the company financial data for the last year. He devised a scoring system called the Piotroski F-Score, a 9 points scoring system based on profitability, funding, and operational efficiency. It looks at simple things such as: 'has the company made more profit compared to last year?' (+1 point) but also: 'is the company cooking the books by adjusting accruals?' (0 points). By using 9 points he was able to get enough signals to determine whether the company is really improving or not.
His research showed that his Piotroski F-score helped to predict the performance of low price-to-book stocks. In his backtests, he found that this strategy outperformed the market by 10% a year on average between 1976 and 1996. The tests also displayed that this was even more the case for small and medium sized companies. Piotroski attributes to the fact that these stocks are often outside of the radar of analysts and new information about a company doesn't get reflected in the share price as quickly.
You can read his influential 2000 paper by clicking on the following link.
“by selecting low Price-to-Book companies and by filtering out the best companies using a set of accounting signals, one could have generated a 23% average yearly return from 1976 to 1996.”Joseph Piotroski
Our Piotroski screener selects the 20% lowest price-to-book companies and filters out the ones with an F-score of less than 7.
How do we calculate the F-score?
The F-score is the sum of 9 binary scores in 3 categories:
- ROA - Return on Assets: Net income before extraordinary items divided by total assets at the beginning of the year. 1 if positive, 0 if negative.
- CFO - Cash Flow Return on Assets: Net cash flow from operating activities (operating cash flow) divided by total assets at the beginning of the year. 1 if positive, 0 if negative.
- ΔROA - Change in Return on Assets: Compare return on assets to last year. 1 if it's higher, 0 if it's lower.
- ACCRUAL - Quality of earnings (accrual): Compare cash flow return on assets to return on assets. 1 if CFO > ROA, 0 if CFO < ROA.
- ΔLEVER - Change in gearing or leverage: Compare the gearing (long-term debt divided by average total assets) to the gearing last year. 1 if gearing is lower, 0 if it's higher.
- ΔLIQUID - Change in working capital: Compare the current ratio (current assets divided by current liabilities) to the current ratio last year. A value higher than 1 indicates an increasing ability to pay off short term debt.
- EQ_OFFER - Change in outstanding shares: The number of shares outstanding compared to last year. 0 if the number increased, otherwise 1.
- ΔMARGIN - Change in Gross Margin: Current gross margin compared to last year. 1 if higher, 0 if lower
- ΔTURN - Change in asset turnover: Compare asset turnover (total sales divided by total assets at the beginning of the year) to last year's asset turnover ratio. 1 if higher, 0 if lower.
To calculate this year's number we use the last trailing 12 months (TTM) number available. For last year we use the same number 1 year ago.
F-Score as secondary ratio
The F-Score is often used in combination with other screens. Our tests showed that if you filter the results of the ERP5 screen by only selecting companies with an F-Score of 7 or more, the return increases from 18,66% to 19,5% per annum. (1999-2010). Many of our members also like to use it in combination with the Greenblatt Magic Formula. We added both screens to our templates and called them the ERP5 Best Selection and the Magic Formula Best Selection.
In our glossary:
Book-to-Market RatioA ratio used to find the value of a company by comparing the book value of a firm to its market value.. more...
Piotroski F-ScoreThe 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.. more...
In our blog:
Piotroski backtesting results available, 525pct return between June 1999 and now!We just completed backtests on our Piotroski price-to-book screener using our proprietary backtesting tool.. more...
Utility of Piotroski F-Score for predicting Growth-Stock ReturnsOver the course of the last decades, the analysis of structural reasons for equity out- or underperformance has been a widely discussed academic topic.. more...
Which magic formula is the most popular(Intro from the June 2014 edition of the systematic value investor newsletter)Over the past 5 years we gathered quite a few screens and ratios.. more...
Top 15 Piotroski f-score stocks Western EuropeThe Piotroski screen remains one of our most popular screens.. more...
In our research:
Piotroski F-ScoreJoseph Piotroski is an associate professor of accounting at the Stanford University Graduate School of Business.. more...
Piotroski F-ScoreWith this combination, we first selected the 20% of companies with the highest Piotroski F-score and then divided these companies into quintiles based on the second factor we tested.. more...