30 Years Of Accounting Lessons: Penman, Piotroski, Beneish, And More – Part V by Steven De Klerck, follow him on Twitter here
7. Wahlen and Wieland, 2010
Name of investment strategy: PEI-SCORE
Number of variables used: 6
Use of statistical techniques: NO
Wahlen and Wieland (2010) develop the “Predicted Earnings Increase (PEI) score”. This score summarises the likelihood of an increase in one-year-ahead earnings. In this regard Wahlen and Wieland (2010) follow in the footsteps of, for example, Ou and Penman (1989) and Penman and Zhang (2006).
PEI-SCORE consists of six financial signals; each company is granted a score of -1, 0 or +1 for every signal. The signals are chosen based on findings from previous studies (e.g. Penman and Zhang, 2006; Wieland, 2006). Hence, Wahlen and Wieland (2010) explicitly state that their results should be interpreted with caution. Their study furthermore concerns a relatively limited time period (i.e. 1994-2005). It is unclear why Wahlen and Wieland (2010) do not implement their investment strategy over a longer time period.
The six financial statement ratios are shown in the following table. They concern the return on net operating assets (RNOA), the operating accruals (OpAccr), the growth rate in net operating assets (GNOA), the change in gross profit margin relative to the change in sales (deltaGM), the change in selling, general and administrative expenses (deltaSGA) and the change in asset turnover (deltaATO).
First, I want to highlight a major difference between F-SCORE on the one hand and PEI-SCORE on the other. Under F-SCORE a company receives a score of +1 if the return on assets and the cash flow from operations are positive. Under PEI-SCORE strongly loss-making companies receive a score of +1. Consequently it might be expected that the best companies according to PEI-SCORE are more risky, from a fundamental point of view, compared to companies with the highest F-SCOREs.
Secondly, the scoring model of Wahlen and Wieland (2010) is also more detailed and complex compared to that of Piotroski (2000), as can be seen in the following explanation.
As far as the signals RNOA, deltaGM and deltaATO are concerned, companies are annually sorted from large to small.
Economic theory suggests that the earnings generated by a firm on its net operating assets, RNOA, will converge to a firm-specific mean over time (Stigler, 1963). A firm with extremely high RNOA is likely to encounter new competition and reduced future returns; a firm with an extremely low RNOA is likely to take steps to increase future returns or to cease operations. Companies in the top (bottom) quintile get a score of -1 (+1) for signal RNOA.
Prior work (e.g. Graham, Dodd and Cottle, 1962; Lev and Thiagarajan, 1993; Abarbanell and Bushee, 1997) asserts that when the gross margin ratio increases at a rate faster than the rate of growth in sales, this signals potentially sustainable improvement in the firm’s relative pricing power. When the gross margin decreases at a rate faster than the rate of growth in sales, it signals potentially persistent deterioration in the relationship between a firm’s input and output prices.
The asset turnover ratio measures the efficiency of how the firm utilizes its assets to generate sales. An increase in the asset turnover ratio occurs when sales increase faster than net operating assets. If the increase is the result of increased operating efficiency and continues into the future, one should observe a positive relation between the asset turnover ratio and the future change in RNOA. Companies in the top quintile get a score of +1 for signals deltaGM and deltaATO; companies in the bottom quintile get a score of -1 for deltaGM and deltaATO. The companies in the remaining quintiles get a score of 0.
As far as the other accounting variables are concerned, the score depends on the value that the company assumes for another variable. For the accrual signal (OpAccr) companies first are subdivided in quintiles based on RNOA. Within each RNOA quintile companies are subsequently sorted from small to large based on their operating accruals (OpAccr) and again subdivided in quintiles. Wahlen and Wieland assign a score of -1 (+1) to firms in the top (bottom) accrual quintile within each RNOA quintile. The companies in the remaining quintiles get a score of 0. An identical methodology is applied to the GNOA signal. Wahlen and Wieland (2010) argue that if the current period growth rate in net operating assets is low relative to current RNOA, this trend implies increasing operating efficiency, which leads to further earnings increases. Conversely, if the current growth rate in net operating assets is high relative to RNOA, it implies the inefficient usage of (net) operating assets.
The deltaSGA variable measures the firm’s overhead expenses growth relative to sales growth. Anderson, Banker and Janakiraman (2003) argue that the interpretation of this signal must be conditioned on the direction of sales growth. Consequently, as part of PEI-SCORE, a distinction is made between the companies with an increase or decrease in sales in the previous fiscal year. Anderson, Banker and Janakiraman (2003) predict and find that when sales increase and SGA expenses increase as a percentage of sales, it implies weak overhead cost control, which does not bode well for future earnings growth. Likewise, if SGA expenses decline as a percentage of sales, it implies strong overhead cost control, which portends future earnings increases. Companies having had a sales increase get a score of +1 (-1) in case they are – based on the deltaSGA signal – in the bottom (top) quintile. Conversely, when firms experience sales declines, Anderson, Banker and Janakiraman (2003) predict and find that when SGA expenses grow as a percentage of sales, it signals managers’ optimism about future growth in sales and earnings. Similarly, when firms experience sales declines and managers simultaneously cut SGA expenses as a percentage of sales, it signals their pessimism about future. Companies with a sales decrease get a score of -1 (+1) in case they are – based on the deltaSGA signal – in the bottom (top) quintile. All remaining companies get a score of 0.
In the final step Wahlen and Wieland (2010) compute the sum of the scores on the six financial signals; they obtain the so-called “Predicted Earnings Increase (PEI) score”.
From the above discussion, it becomes clear that PEI-SCORE computations are much more complex compared to F-SCORE.
The study demonstrates that over the 1994-2005 time period a long position in the companies with the 20 percent highest PEI-SCORE results in an average annual size-adjusted return of 6.5 percent. This is shown in the following table. In line with Penman and Zhang (2006), Wahlen and Wieland (2010) provide no information on the performance of PEI-SCORE for smaller stock portfolios. These results would give us more insight into the returns of stocks with the highest (between 4 and 6) and lowest (between -6 and -4) PEI-SCOREs.
Amor?Tapia and Tascón (2012) find that PEI-SCORE is not effective in European stock markets, as shown in the following table. High PEI-SCORE firms underperform low PEI-SCORE firms over the 1981-2011 period, contrary to previous evidence in the US stock market.
In the light of these findings, among many other critical observations, investors are advised to put the results of the studies by Penman and Zhang (2006) and Wahlen and Wieland (2010) through additional (international) empirical analyses.
In the final part on fundamentals-based investment strategies in the accounting literature, I discuss the paper by Dickinson and Sommers (2012): “Which Competitive Efforts Lead to Future Abnormal Economic Rents? Using Accounting Ratios to Assess Competitive Advantage”. In this paper the researchers quantify competitive advantages using accounting-based ratios. Based on my limited knowledge of Warren Buffett’s investment strategies (I am more a follower of Grahamite value investing) and his focus on “moat”, this paper might be interesting to Buffett’s followers.