Conference Agenda

We offer a novel resolution to several asset pricing puzzles by investigating how complexity affects pricing errors when rational, risk-averse agents have imperfect knowledge of the data-generating process. Our theoretical framework yields three key implications as complexity increases: (1) equilibrium pricing errors grow systematically larger, (2) the optimal portfolio increasingly exploits estimation error components rather than fundamental risk, and (3) multiple strategies achieve higher Sharpe ratios while maintaining low cross-correlations. Our model explains the limited pricing power of parsimonious factor models, the weak relationship between betas and average returns, and the proliferation of anomalies. Empirically, we document substantial complexity in return predictability and covariance structures. Analyzing sophisticated quantitative strategies, we find remarkably low correlations, with an average $R^2$ below 1\% among systematic hedge funds' active positions, consistent with our model's prediction that different strategies exploit distinct dimensions of estimation error in complex markets.

We construct managed portfolios based on textual analysis of firms' earnings call transcripts and investigate their asset pricing implications. Loadings on the text-managed portfolios can explain a comparable amount of stock-level return variation as those on the conventional characteristics-based factors. Combining the earnings call text and firm characteristics enhances the conditional mean-variance efficiency of factor portfolios but not the predictive power for stock returns. Drawing on the insights of Kozak and Nagel (2024), our evidence suggests that the earnings call text contains information about the return covariances that are missing from characteristics. Text-managed portfolio returns correlate with investor sentiments and forecast macroeconomic outcomes.