Anomalies and the Expected Market Return

Abstract

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

Meta-Learning Approaches for Recovery Rate Prediction

“…In Section 4.1, we confirm that this is incompatible with the possibility of exploiting the information contained in a large data set of candidate predictors and embeds high model risk. This result is in line with the recent findings of Dong et al (2020) in a similar financial context.…”

The Virtue of Complexity in Return Prediction

“…Ludvigson and Ng (2007) and Kelly and Pruitt (2013) use principal components regression and partial least squares, respectively, to leverage large predictor sets for market return prediction and achieve shrinkage through dimension reduction. Dong et al (2022) use 100 long-short "anomaly" portfolios to forecast the market return using a variety of forecasting strategies to implement shrinkage (more generally, see the recent survey by Rapach and Zhou (2022)). An emerging literature uses machine learning methods to forecast large panels of individual stock returns or portfolios, including Rapach and Zhou (2020), Kozak, Nagel, andSantosh (2020), Freyberger, Neuhierl, andWeber (2020), Gu, Kelly, andXiu (2020), andChen, Pelger, andZhu (2023) (also see the survey by Kelly and Xiu (2022)).…”

Hedging performance of volatility index futures: a partial cointegration approach

“…Since the hedging performance varies with the length of the hedge period, we consider the hedging effectiveness for the 1-, 4-, 8-, and 12-week cases. 8 Following Dong et al (2022), this paper uses a 10-year moving window to estimate the hedge ratios and measures of the hedging effectiveness. 9 , 10 The hedging performance metrics are measured from March 26, 2014 to January 19, 2022.…”