We provide the first systematic evidence on the link between long-short anomaly portfolio returns-a cornerstone of the cross-sectional literature-and the timeseries predictability of the aggregate market excess return. Using 100 representative anomalies from the literature, we employ a variety of shrinkage techniques (including machine learning, forecast combination, and dimension reduction) to efficiently extract predictive signals in a high-dimensional setting. We find that longshort anomaly portfolio returns evince statistically and economically significant outof-sample predictive ability for the market excess return. The predictive ability of anomaly portfolio returns appears to stem from asymmetric limits of arbitrage and overpricing correction persistence. STOCK RETURN PREDICTABILITY IS A fundamental topic in finance. Two major-and voluminous-lines of research focus on this subject. The first examines whether firm characteristics can predict the cross-sectional dispersion in stock returns. These studies identify numerous equity market anomalies (e.g., Fama and French (2015), Harvey, Liu, and Zhu (2016), Pontiff (2016), Hou, Xue, andZhang (2020)). The second line of research investigates the time-series predictability of the aggregate market excess return based on a variety of economic and financial variables, such as valuation ratios, interest rates, and inflation (e.g., Nelson (1976), Campbell (1987, Fama and French