Events

Professor Alejandro Lopez-Lira
Assistant Professor of Finance
Warrington College of Business
University of Florida

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 ?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.