30 Years Of Accounting Lessons: Penman, Piotroski, Beneish [Part II]

30 Years Of Accounting Lessons: Penman, Piotroski, Beneish [Part II]

30 Years Of Accounting Lessons: Penman, Piotroski, Beneish And More – Part II by Steven De Klerck – Also follow him on Twitter here

Before continuing with the studies by Abarbanell and Bushee (1998) and Piotroski (2000), I would like to draw the reader’s attention to two international applications of the Ou and Penman (1989) Pr approach. Despite the lack of robustness on the US dataset (e.g. Holthausen and Larcker, 1992), the Ou and Penman (1989) Pr measure has been found to be effective in South-Korea by Chung, Kim and Lee (1998) and in New Zealand by Goslin, Chai and Gunasekarage (2012). In light of these international results some readers might be interested to pursue the Ou and Penman (1989) strategy in greater depth. The study by Goslin, Chai and Gunasekarage (2012) provides an accessible and recent paper to become familiar with the Ou and Penman (1989) Pr measure.

As mentioned at the end of Part I, it lasted about ten years – following Ou and Penman (1989) – for a truly new fundamental investment strategy to be introduced in the accounting literature. The next attempt was made by Abarbanell and Bushee in 1998.

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3. Abarbanell and Bushee, 1998

Name of investment strategy: /
Number of accounting variables used: 9
Use of statistical techniques: YES

In 1993 Lev and Thiagarajan argued that the purely statistical approach, the computation of an extensive set of detailed accounting variables (68 accounting variables in Ou and Penman (1989)) and the employment of intricate statistical methodologies are important disadvantages of the Ou and Penman (1989) Pr strategy. As a consequence Lev and Thiagarajan (1993) abandon the statistical search procedure and appeal to the analyses of financial analysts in The Wall Street JournalBarron’sValue Line, … in order to compile a list of relevant accounting signals for stock selection. Their so-called guided search procedure yielded twelve fundamental signals; the signals are shown in the following table. Nine out of these twelve signals are used by Abarbanell and Bushee (1998) in the development of a fundamentals-based investment strategy. The most important signals deal with relative changes in inventories, accounts receivables, capital expenditures, gross margins and selling and administrative expenses. The other variables set forth by Lev and Thiagarajan (1993) are – from my point of view – either too fine-grained to be (internationally) relevant and/or result in a massive reduction of the number of available companies due to the lack of data availability.

In contrast with the other seven papers mentioned in Part I’s introduction, the explanations by Abarbanell and Bushee (1998) make it impossible for me to replicate their study. Consequently, the obscureness of the methodology makes it difficult to pronounce upon the effectiveness of their strategy. Given this observation and the fact that the strategy has taken no prominent place in later literature (witness the lack of replications of the strategy by other researchers (e.g. Goslin, Chai and Gunasekarage (2012)), among other arguments, raises doubts about its effectiveness. I leave it to the reader whether he or she finds some added value in the paper by Abarbanell and Bushee (1998). In case you readers should be familiar with profound international replications of their strategy, please let me know by sending an email to [email protected]

4. Piotroski, 2000

Name of investment strategy: F-SCORE
Number of accounting variables used: 9
Use of statistical techniques: NO

To the best of my knowledge Piotroski (2000) is one of the very few researchers in the accounting literature who appreciates the advantages and the elegancy of simple accounting-based investment strategies (for individual investors). With “simple” I mean the use of a limited number of broad accounting variables and the lack of obscure statistical methods requiring the availability of the data for all public companies over many years.

“A limitation of these two studies [Ou and Penman (1989) and Holthausen and Larcker (1992)] is the use of complex methodologies and a vast amount of historical information to make the necessary predictions.” (Piotroski, 2000, p. 10)

Piotroski (2000) investigates whether a much more simplified financial statement analysis enables to discriminate between value stocks with a low and high future return. Companies are assessed on nine binary accounting criteria that apply to profitability, leverage, liquidity, sources of funds and operating efficiency respectively. This is shown in the following table.


Under F-SCORE profitable firms (in terms of net earnings and cash flow from operations) and firms with a current improvement in business and financial fundamentals (i.e. profitability, solvency, liquidity, profit margin and efficiency) are rewarded with a score of +1. Furthermore, and similar to an increase in financial leverage, the issuance of common equity is considered to be a negative for future firm’s performance. Finally, Piotroski (2000) entertains the accrual anomaly documented by Sloan (1996). Sloan (1996) documents that the accrual component of earnings has lower persistence than the cash flow component of earnings. Sloan (1996) also shows that investors fail to distinguish fully between the different properties of the accrual and cash flow components of earnings.

With respect to the accrual anomaly it should be noted however that Green, Hand and Soliman (2011) find that the cumulative raw annual hedge portfolio return to Sloan’s accrual definition is negative over the 2000-2010 period. Raw annual hedge portfolio returns were negative in seven out of the eleven years over the 2000-2010 period. Similar results are documented for size-adjusted returns. Richardson, Tuna and Wysocki (2010) [a recent literature overview on fundamental analysis] and Green, Hand and Soliman (2011) attribute the attenuated relationship between accruals and future stock returns to the exploitation of the associated anomaly by hedge funds.

Piotroski (2000) computes F-SCORE as the simple sum of the nine binary accounting signals. As a consequence, companies with a high (low) F-SCORE are characterized by a broad improvement (decline) in financial performance during the previous fiscal year. Piotroski (2000) documents that over the 1976-1996 period in a high book-to-market portfolio the average annual portfolio return can be increased significantly by at least 7.4 percent through the selection of high book-to-market stocks with high F-SCOREs. This means that within a value portfolio companies with the strongest overall improvement in business fundamentals – as measured by F-SCORE – generate the highest returns.

The results for the raw returns are shown in the following table. We observe that value stocks with high F-SCOREs realize a one-year raw return of 31.3 percent, significantly higher compared to value stocks with low F-SCOREs (7.8 percent).

When looking at the fundamental signals underlying F-SCORE we also see that the F-SCORE strategy can be considered to be an underreaction strategy. The majority of the accounting signals respond to the improvement or downswing in business and financial fundamentals over the previous fiscal year. Handling this information by market participants will be done quicker when it concerns large cap stocks followed by a considerable number of analysts and enjoying quite some media attention rather than is the case with small companies not followed by analysts nor enjoying any attention of the financial media (e.g. Hong, Lim and Stein, 2000). Piotroski (2000) documents that the effectiveness of F-SCORE is indeed concentrated in “slow information-dissemination environments”, viz. in the group of small and medium-sized companies, companies with low share turnover, and firms with no analyst following. This is shown in the following table. While the difference in returns between high F-SCORE firms and low F-SCORE firms is significant for both small and medium firms, the opposite can be said for large firms.

As opposed to some of the other accounting-based investment strategies (see the eight papers in the introduction ofPart I) F-SCORE has become a frequently used fundamental investment strategy among financial analysts and equity investors. F-SCORE’s popularity definitely originates from the simplicity of the strategy and the use of clear and well-known fundamental signals. F-SCORE has been integrated into many popular stock screeners.

I would like to conclude the first ten years of research in the accounting literature on fundamentals-based investment strategies (from Ou and Penman (1989) to Piotroski (2000)) with two important observations.

First, it has become clear that a massive amount of accounting information is not automatically translated into robust and/or promising investment strategies. Ou and Penman (1989): 68 ad-hoc selected accounting variables. Piotroski (2000): 9 accounting variables with a strong business-economic rationale. Leaving apart the accrual variable the F-SCORE strategy takes into account eight variables.

Secondly, the absence of statistical techniques makes the strategy by Piotroski (2000) much more user-friendly. In case of Ou and Penman (1989) and Abarbanell and Bushee (1998) – but also in the case of Penman and Zhang (2006) and Dickinson and Summers (2012) – as an investor you have to compute ALL accounting information for ALL companies in order to be able to run the annual regressions over the past three and more fiscal years. This is not the case for the Piotroski (2000) strategy. In case of F-SCORE you – as an investor – can look for the first 20-30 companies with high F-SCORE during the publication season, which is a relatively easy task.

In Part III our attention shifts to the Beneish, Lee and Tarpley (2001) two-step strategy to identify stocks with extremely positive and negative returns and the growth version of the F-SCORE strategy (Mohanram, 2005).

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