We list the FS_Score metric in many of the quantitative screeners on the website. Not only we present the FS_Scores for stocks, but we also use it as part of the underlying quant algorithms which generate the screeners’ results.
Not surprisingly we often get the question – What the heck is this FS_Score? We hope that this article will help in clarifying.
To understand what is FS_Score, we first have to become familiar with Joseph Piotroski and his F_Score. Joseph Piotroski is an accounting professor at Stanford University. In 2000, while he was still a professor at Chicago University, he developed a metric which he named (how original 🙂 ), The Piotroski Fundamental Score, or F_Score, a quantitative method of conducting an accounting-based fundamental analysis.
The F_Score can be of help to both discretionary investors who pick and choose their stock holdings after careful fundamental research, as well as to quantitative investors who invest mechanically based on a research-driven algorithm.
The F_Score utilizes various accounting metrics to assess a company’s financial strength. The F_Score is an integer between 0 and 9. A higher score correlates to higher financial stability and lower scores, to questionable stability. When calculated automatically, the F_Score can be is a huge time saver for the fundamental analyst. Like any quantitative metric, it cannot reflect the accurate status of every single stock selection, but on average, it is a quick way to determine if a stock is strong or weak financially.
Piotroski’s key insight was that quantitatively-analyzed financial statements could improve performance. The F_Score was designed to eliminate underperforming stocks. Piotroski observed that although value investing portfolios beat the market as a whole (we already know that 🙂 ), most of the outperformance of those portfolios result from just a handful of stocks, while the majority of the stocks (approximately 57 percent) underperform the market. The F_score helps detect in advance under-performers and eliminate them from portfolios. More than a tool for selecting STRONG stock candidates, The F_Score is used to eliminate obvious weak candidates.
The research that Piotroski conducted shows that his assumptions were correct. In his paper, Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers, he shows that over a period of 21 years between 1976 and 1996, using the F_score could improve the average yearly returns of a value portfolio by up to 7.5% per year. He also found that expected winners gained 23% per year more than expected losers during that period. That’s a remarkable result.
Piotroski never claimed that he had optimized his F_score to achieve the best predictions. It’s a useful metric, but it can be made even better. Tobias Carlisle and Wesley Grey, in their book Quantitative Value, took on the challenge to further optimize the F-Score to improve its prediction power. They named their improved metric the FS-Score.
The FS_Score is constructed from 10 conditions based on fundamental data gathered from a company’s financial statements. If a condition is met, it gets a score of 1. If the condition is not met, it receives a score of zero. The sum of the individual scores in the FS_Score. A higher score indicates a financially stronger company.
Here are its components.
FS_ROA = ‘1’ if the ROA is positive for past year, ‘0’ if otherwise
FS_FCFTA = ‘1’ if the Free Cash Flow divided by total assets for the past year is positive, ‘0’ if otherwise
FS_ACCRUAL = ‘1’ if the Free Cash Flow minus Net Earnings in the past year is positive, ‘0’ if otherwise
FS_LEVER = ‘1’ if Debt/Assets ratio has decreased during the past year, ‘0’ if otherwise
FS_LIQUID = ‘1’ if the current ratio has improved in the past year, ‘0’ if otherwise
FS_NEQISS = ‘1’ if the equity repurchases exceeded equity issuance in the past year, ‘0’ if otherwise
F_dROA = ‘1’ if the ROA in the last year is greater than in the past year, ‘0’ if otherwise
F_dFCFTA = ‘1’ if the Free Cash Flow divided by Total Asset has increased in the past year, ‘0’ if otherwise
FS_dMargin = ‘1’ if the Gross Margin rate increased during the past year, ‘0’ if otherwise
FS_dTURN = ‘1’ if Asset Turnover has increased during the past year, ‘0’ if otherwise.
The Piotroski score is not intended to be used in isolation. It should be regarded as a complement to a Value or Momentum strategy.
Piotroski himself lists in his paper a few limitations of the F_Score. He indicates that the “benefits to financial statement analysis are concentrated in small and medium-sized firms, companies with low share turnover, and firms with no analyst following.” His findings imply that small caps and less-followed stocks are more prone to mispricing. Lucky us individual investors who are not restricted in buying small caps.
We use the FS_Score in our strategy screeners in several ways. First, it is one of the criteria we use in the “Quality Rank” ranking system of the Quantitative Value Screener. Secondly, In our Quantitative Momentum strategy screener, we eliminate the stocks with the absolute worst scores (0,1 and 2).
Lastly, we list the FS_Score in the screener results tables to assist investors who perform their own discretionary research on candidate stocks. We know of no other free or commercial screener that calculates and lists the FS_Score.
Equipped with this knowledge on the FS_Score, go and check the score of your current stock holdings.
- Gray, W. R., & Carlisle, T. E. (2012, December 26). Quantitative Value,+ Web Site: A Practitioner’s Guide to Automating Intelligent Investment and Eliminating Behavioral Errors (Vol. 836). John Wiley & Sons.
- The Piotroski F-Score: Reviewing Joseph Piotroski’s accounting-based value investing screen, Stockpedia
- Piotroski, J.(2002). Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers.