To price assets with a parsimonious set of factor-mimicking portfolios, one typically identifies and weights well-diversified basis portfolios. Traditional weightings lead to factor-mimicking portfolios that are unlikely to price even the basis portfolios from which they are formed. We offer a method to combine basis portfolios into a single factor-mimicking portfolio that is closely linked to the optimal portfolio. In practice, this method improves the pricing accuracy of parsimonious factor models, even for anomaly portfolios formed from characteristics that are distinct from those underlying the basis portfolios.

Portfolio optimization often struggles in realistic out-of-sample contexts. We deconstruct this stylized fact by comparing historical forecasts of portfolio optimization inputs with subsequent out-of-sample values. We confirm that historical forecasts are imprecise guides of subsequent values, but we discover the resultant forecast errors are not entirely random. They have predictable patterns and can be partially reduced using their own history. Learning from past forecast errors to calibrate inputs (akin to empirical Bayesian learning) generates portfolio performance that reinforces the case for optimization. Furthermore, the portfolios achieve performance that meets expectations, a desirable yet elusive feature of optimization methods. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

We introduce a theoretical model of the active fund management industry (AFMI) in which performance and size depend on the AFMI's competitiveness (concentration). Under plausible assumptions, as AFMI's concentration decreases, so do fund managers’ incentives for exerting effort in search of alpha. Consequently, managers produce lower gross alpha, and rational investors, inferring lower expected AFMI performance, allocate a smaller portion of their wealth to active funds. Empirically, we find that a decrease in the US mutual fund industry concentration over our sample period is associated with a decrease in its net alpha and size (relative to stock market capitalization).

We develop an intertemporal asset pricing model where cash-flow news, discount-rate news, and their second moments are priced by the market. This model generalizes the market-return decomposition framework, showing that intertemporal considerations imply a decomposition of squared market returns (coskewness risk). Our model accounts for 68% of the return variation across portfolios sorted by size, book-to-market ratio, momentum, investment, and profitability for a modern U.S. sample period. Further, our findings highlight the importance of covariation risk, that is, the risk of simultaneous unfavorable shocks to cash flows and discount rates, in understanding equity risk premia.

We introduce political tournament “participation/inclusion” games. Dominant strategies determine whether players choose to compete by enhancing economic performance. Unique Nash equilibria competitors win (only) inclusion as promotion candidates. We find empirical justification for such equilibria in Chinese province heads' periodic political tournaments/elections for promotion to the Communist Party politburo and government positions. We document pervasive tournament-synchronized corporate decision-making cyclicality. Firms enhance economic performance by increasing investments, taxes, and employment before elections. Cyclicality is dominantly driven by privately-owned enterprises, is weaker in economically/politically weak(strong) provinces. Political promotions, however, are not sensitive to corporate investments enhanced before tournaments but to long-run investments.