Three main points emerged from the analysis of ROIC patterns. First, analysts need to consider the lessons of history when modeling rather than approaching each model as unique. Analysts should view the experience of a large sample of companies as a rich reference class. Second, the empirical evidence shows ROICs tend to revert to the mean, a level similar to the cost of capital. Randomness plays an important role in the mean-reversion process. Finally, some companies do deliver persistently high or low results beyond what chance would dictate. Unfortunately, pinpointing the causes of persistence is a challenge.
In an efficient market, stock prices are an unbiased estimate of value. Market efficiency does not say that stock prices are always right; it only asserts that prices are not wrong in a systematic way. For this analysis, we combined our data on ROIC patterns with total shareholder returns to see whether there is a consistent way to generate excess returns.
Buy the Best, Sell the Rest
Investment pros often recommend buying good businesses. So we started our total shareholder return investigation by analyzing the returns from equal-weighted portfolios based on 1997 ROIC quintiles (our data are from 1997 through 2006). The first quintile represents the 20 percent of the companies with the highest ROICs, while the fifth quintile comprises the worst-ROIC companies. Exhibit 1 shows the annual total shareholder returns (TSR) and the combination of returns and standard deviations for each portfolio from 1997 through 2006. Appendix A provides the full distributions. To provide some context, the 1,000-plus companies in this sample came from the Russell 3000, which provided an 8.6 percent return during this period. Appendix B reconciles the index’s returns with those from our sample
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