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Title: What matters when: Time-varying sparsity in expected returns Authors:  Andrea Tamoni - Rutgers Business School (United States) [presenting]
Daniele Bianchi - Queen Mary University of London (United Kingdom)
Matthias Buechner - University of Warwick (United Kingdom)
Abstract: A measure of sparsity for expected returns within the context of classical factor models is provided. This measure is inversely related to the percentage of active predictors. Empirically, sparsity varies over time and displays an apparent countercyclical behavior. Proxies for financial conditions and for liquidity supply are key determinants of the variability in sparsity. Deteriorating financial conditions and illiquid times are associated with an increase in the number of characteristics that are useful to predict anomaly returns (i.e., the forecasting model becomes more dense). Looking at specific categories of characteristics, we find that variables classified as trading frictions are robustly present throughout the sample. A substantial amount of the time-variation in sparsity is attributable to the value, profitability, and investment categories. A strategy that exploits the dynamics of sparsity to time factors delivers substantial economic gain out-of-sample relative to both a random walk and a model based on preselected, well-know characeristics like size, momentum and book-to-market.