We provide a tractable model of firm-level expected holding period returns using two firm fundamentals—book-to-market ratio and return on equity—and study the cross-sectional properties of the model-implied expected returns. We find that firm-level expected returns and expected profitability are time-varying but highly persistent and that forecasts of holding period returns strongly predict the cross section of future returns up to three years ahead. We show a highly significant predictive pooled regression slope for future quarterly returns of 0.86. The popular factor-based expected return models have either an insignificant or a significantly negative association with future returns. In supplemental analyses, we show that these forecasts are also informative of the time series variation in aggregate conditions. For a representative firm, the slope of the conditional expected return curve is more positive in good times, when expected short-run returns are relatively low, and the model-implied forecaster of aggregate returns exhibits modest predictive ability. Collectively, we provide a simple, theoretically motivated, and practically useful approach to estimating multi-period-ahead expected returns.

Applying a “co-search” algorithm to Internet traffic at the SEC׳s EDGAR website, we develop a novel method for identifying economically related peer firms and for measuring their relative importance. Our results show that firms appearing in chronologically adjacent searches by the same individual (Search-Based Peers or SBPs) are fundamentally similar on multiple dimensions. In direct tests, SBPs dominate GICS6 industry peers in explaining cross-sectional variations in base firms׳ out-of-sample: (a) stock returns, (b) valuation multiples, (c) growth rates, (d) R&D expenditures, (e) leverage, and (f) profitability ratios. We show that SBPs are not constrained by standard industry classification, and are more dynamic, pliable, and concentrated. We also show that co-search intensity captures the degree of similarity between firms. Our results highlight the potential of the collective wisdom of investors — extracted from co-search patterns — in addressing long-standing benchmarking problems in finance.