Estimating Security Betas Using Prior Information Based on Firm Fundamentals

Abstract

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Industry Costs of Equity: Incorporating Prior Information

“…Specifically, he tests an approach that shrinks a firm's rolling-window market beta estimate to the market beta of its industry. Cosemans, Frehen, Schotman and Bauer (2016) apply the shrinkage approach to incorporate the cross-sectional, firm-specific information into rolling-window market beta estimates at the firm level. The authors find that their approach of incorporating firm-level priors outperforms incorporating either the market-level prior or the industry-level prior.…”

“…I apply a grid search for the optimal prior variance for two reasons (Doan, Litterman and Sims, 1984). First, there is no theoretically motivated prior variance for this study, and empirical research has not come to an agreement on what the prior variance should be (Cosemans, Frehen, Schotman and Bauer, 2016). Second, the goal is to find the parameter estimates that yield the smallest out-of-sample forecast error for industry costs of equity, and actively searching for the optimal prior variance serves this purpose.…”

“…The paper in the literature that most closely relates to this study is Cosemans, Frehen, Schotman and Bauer (2016). Along with Cosemans, Frehen, Schotman and Bauer (2016), my paper incorporates prior information into rolling-window beta estimates using Bayesian techniques. Yet, we each use different test assets (individual stocks vs industry portfolios) and evaluate different forecast objects (market beta vs costs of equity).…”

“…Second, I construct a Bayesian hierarchical model that incorporates information from industry characteristics into CAPM beta estimates. This approach relates to, but is different from, the recent method of Cosemans, Frehen, Schotman and Bauer (2016). They combine firm-level beta estimates from a rolling time series approach with estimates of beta from a cross-sectional regression with firm characteristics using Bayes' rule.…”

“…Third, I provide a measure of the improvement generated by a given beta forecast relative to an ex post lower bound on the variability of forecast errors in the cost of equity. In contrast, the prior literature (e.g., Cosemans, Frehen, Schotman and Bauer, 2016) measures performance by analyzing differences between beta forecasts and realized betas, such that observed differences may be attributable to estimation error in either beta estimate. My approach allows for an absolute evaluation of forecast performance and provides an economically interpretable measure of the extent of possible additional forecast improvements.…”