ABSTRACT This paper provides an accounting-based valuation model that predicts that cross-sectional variation in firm-level returns to investments in both stock and stock return volatility are related to cross-sectional variation in firm-level fundamentals. The model predicts that expected stock returns have a positive quadratic relation with stock return variance and a negative quadratic relation with gains to trading in stock return variance. Consistent with these predictions, firms with high model-implied expected stock returns have high future stock return variance, and the relation is roughly quadratic. In contrast, firms with high expected stock returns have low future returns to trading in stock return variance through option contracts because these firms have high option-implied variance relative to future realized variance, i.e., low variance risk premia (VRP). The study provides a framework for using fundamentals for trading in individual stocks and options. JEL Classifications: G12; G14; G17.

ABSTRACT This study finds that the association between future stock returns and information quality depends on how option-like is the firm's equity. Firms that have more growth options are more option-like. The association between future stock returns and information quality is negative (positive) for those firms with equity that is least (most) option-like. These results are consistent with traditional asset pricing theory and are robust to numerous empirical specifications. Collectively, these findings offer a theoretically based and empirically supported explanation for why prior studies that do not condition on the option-like nature of equity have documented either a positive or no association between information quality and future average stock returns.

ABSTRACT This study offers solutions to help address practical issues researchers generally overlook when assessing gains from a trading signal. Specifically, the methodologies used in previous research generally ignore investor risk aversion when forming portfolios, do not update portfolios as signals arrive, exploit look-ahead biases, do not assess the incremental gains of a new signal, and do not consider market frictions. We examine trading signals based on post-earnings announcement drift (PEAD), the earnings announcement premium (EAP), and earnings announcement rescheduling (RES). Using our proposed approach, we find that portfolios that incorporate the individual signals produce higher Sharpe ratios than equal-weighted portfolios; however, the gains for each signal are concentrated to a few days around the announcement. The EAP and RES signals do not provide incremental portfolio gains over the PEAD signal. After considering market frictions, portfolio performance rapidly attenuates and becomes similar to the SPY ETF as the portfolio size increases. JEL Classifications: G12; G14; G17.

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.

ABSTRACT We integrate fundamental analysis with mean-variance portfolio optimization to form fully optimized fundamental portfolios. We find that fully optimized fundamental portfolios produce large out-of-sample factor alphas with high Sharpe ratios. They substantially outperform equal-weighted and value-weighted portfolios of stocks in the extreme decile of expected returns, an approach commonly used in fundamental analysis research. They also outperform the factor-based and parametric portfolio policy approaches used in the prior portfolio optimization literature. The relative performance gains from mean-variance optimized fundamental portfolios are persistent through time, robust to eliminating small capitalization firms from the investment set, and robust to incorporating estimated transactions costs. Our results suggest that future fundamental analysis research could implement this portfolio optimization approach to provide greater investment insights. JEL Classifications: G12; G14; G17.

ABSTRACT This study provides the first large-scale study of the performance of expected-return proxies (ERPs) internationally. Analyst-forecast-based ICCs are sparsely populated and not robustly associated with future returns. Earnings-model-forecast-based ICCs are well-populated, but are unreliable outside the U.S. We adapt and extend the log-linear and present-value (LPV) framework—combining an accounting valuation anchor, its expected growth, and market prices—for estimating ERPs internationally, and implement a correction for the use of stale accounting data. An LPV ERP anchored on the book value of equity is positively associated with future returns in 26 of 29 equity markets, and largely subsumes the predictive ability of a broad set of firm characteristics previously shown to be associated with expected returns. JEL Classifications: D83; G12; G14; M41.

ABSTRACT This paper examines how changes in risk disclosures affect uncertainty about risk. We measure changes in risk disclosures using the addition and removal of individual risk factors to firms’ 10-K filings, identified via textual analysis of the risk factors section. Our market outcome is the variance risk premium (VRP), which captures the market’s pricing of uncertainty about firm risk. Following recent theoretical predictions, we predict and empirically document that newly disclosed signals of risk factor exposure—reflected in added and removed individual risk factors—decrease the uncertainty surrounding firm risk, as proxied via the VRP. We further confirm that individual risk factors offer incremental insights compared with alternative textual risk measures. Collectively, our findings suggest that textually evaluating individual risk factors reveals information about the uncertainty regarding firm risk. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: G18; G32; M40.