Before taking a look at the S-SCORE strategy by Penman and Zhang (2006), I would like to attract the reader’s attention to a remark made by Nissim and Penman (2001) in their research paper “Ratio Analysis and Equity Valuation: From Research to Practice”:

“But it is fair to say that the research [since 1962] has been conducted without much structure. Nor has it produced many innovations for practice.” (Nissim and Penman, 2001, p. 110) (emphasis added)

Nissim and Penman (2001) argue that the preceding literature on financial statement analysis (e.g. Ou and Penman, 1989; Lev and Thiagarajan, 1993; Abarbanell and Bushee, 1997) is characterized by a lack of structure in the selection of relevant accounting ratios. By introducing a structured, fundamental analytical approach the authors try to address this shortcoming. In this approach they start from the most aggregated value drivers underlying a valuation model (e.g. the residual earnings model). The aggregated components are then, step by step, split up in the underlying financial ratios (e.g. profit margin and asset turnover). It is assumed that the current values of the ratios are the value drivers underlying the future values of the aggregated components. In addition to this, the structured approach takes into account key interrelations between accounting data; an understanding of these interrelations contributes to building a cohesive earnings forecasting framework.

This renewed view on financial statement analysis is operationalized by Penman and Zhang (2002, 2004, 2006). Their working paper is one of the first attempts to provide structure to the earnings forecasting exercise. Richardson, Tuna and Wysocki (2010) – a recent extensive literature overview on fundamental analysis – refer to the Penman and Zhang (2006) approach as a promising avenue for future research.

6. Penman and Zhang, 2006

Name of investment strategy: S-SCORE

Number of variables used: 6

Use of statistical techniques: YES

The objective of Penman and Zhang (2006) is similar to the one of Ou and Penman (1989). Penman and Zhang (2006) develop a summary measure for the sustainability of earnings as reflected in a forecast of the change in operating profitability one year ahead (deltaRNOAt+1). deltaRNOAt+1 is one of the components underlying the residual operating income valuation model.

“This paper yields a summary number (S-SCORE) that informs about the sustainability of earnings.” (Penman and Zhang, 2006, p. 1)

In Table 4 of their research paper the change in the return on net operating assets one year ahead (deltaRNOAt+1) is considered to be driven and forecasted by six accounting variables: the return on net operating assets (RNOAt), the change in the return on net operating assets (deltaRNOAt), its underlying Du Pont components deltaPMt and deltaATOt, the growth rate in net operating assets (GNOAt) and the operating accruals (OpAccrt). This is shown in the following table.


The parameters in the above multivariate linear regression model are estimated annually. Out-of-sample predictions for deltaRNOAt+1 are made by combining the average regression coefficients estimated over the prior three years (from year t-3 to year t-1) in combination with the accounting variables from year t. The forecasts are truly ex ante because future values of the independent variables are not used. The sample is then recursively rolled forward to forecast deltaRNOAt+1 for each of the twenty-four out-of-sample years over the 1976-1999 period. Based on the forecasts made, the companies are ranked from small to large.

Penman and Zhang (2006) document that a stock portfolio consisting of a long position in the ten percent stocks with the highest predicted change in operating profitability on the one hand and a short position in the ten percent stocks with the lowest predicted change in operating profitability on the other, leads to returns that are markedly positive over 20 of the 21 years analysed. This is shown in the following graph.


Penman and Zhang (2006) document a mean size-adjusted return difference between decile 10 and decile 1 of 13.1 percent with a t-statistic of 5.03. This return difference is realized when portfolios are established three months after fiscal-year end. Delaying portfolio formation by one month results however in a substantial drop in mean size-adjusted return difference between decile 10 and decile 1 of 34.5% and 31.6%, a finding mentioned in footnote 13 (Penman and Zhang,2002, 2004) and footnote 19 (Penman and Zhang, 2006) of their working paper respectively.

The study shows no results about the distribution of returns over the long and short position. For investors it would be interesting to dispose of these results. The question can also be raised whether the above hedge returns can be realized. When shorting stocks you should take into account possible short selling constraints, insufficient liquidity and the costs associated with holding short positions among others. The effectiveness of the model should also be assessed on international data, among many other robustness analyses.

Again, and consistent with many of the already discussed accounting papers, critical and important results remain hidden for the fundamental investor. Based on the reported results I believe further in-depth research on S-SCORE is needed. As a consequence, it still has to be proven whether Richardson, Tuna and Wysocki (2010) are right in their assessment of Penman and Zhang (2006) as a promising avenue for successful future fundamental research.

The empirical results by Penman and Zhang (2006) are used as an input in the development of PEI-SCORE by Wahlen and Wieland (2010), which stands for Predicted Earnings Increase-SCORE or PEI-SCORE. This score will be discussed in Part V and afterwards I will advance some important preliminary conclusions with respect to both S-SCORE and PEI-SCORE.

Last year the Flemish financial newspaper De Tijd stated that the Penman and Zhang (2006) approach was being used by Dutch fund managers.