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作者：Haddad, Valentin; Kozak, Serhiy; Santosh, Shrihari
作者单位：University of California System; University of California Los Angeles; National Bureau of Economic Research; University System of Maryland; University of Maryland College Park; University of Colorado System; University of Colorado Boulder
摘要：The optimal factor timing portfolio is equivalent to the stochastic discount factor. We propose and implement a method to characterize both empirically. Our approach imposes restrictions on the dynamics of expected returns, leading to an economically plausible SDF. Market-neutral equity factors are strongly and robustly predictable. Exploiting this predictability leads to substantial improvement in portfolio performance relative to static factor investing. The variance of the corresponding SDF...
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作者：Hou, Kewei; Xue, Chen; Zhang, Lu
作者单位：University System of Ohio; Ohio State University; University System of Ohio; University of Cincinnati; National Bureau of Economic Research
摘要：Most anomalies fail to hold up to currently acceptable standards for empirical finance. With microcaps mitigated via NYSE breakpoints and value-weighted returns, 65% of the 452 anomalies in our extensive data library, including 96% of the trading frictions category, cannot clear the single test hurdle of the absolute t-value of 1.96. Imposing the higher multiple test hurdle of 2.78 at the 5% significance level raises the failure rate to 82%. Even for replicated anomalies, their economic magnit...
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作者：Fama, Eugene F.; French, Kenneth R.
作者单位：University of Chicago; Dartmouth College
摘要：We use the cross-section regression approach of Fama and MacBeth (1973) to construct cross-section factors corresponding to the time-series factors of Fama and French (2015). Time-series models that use only cross-section factors provide better descriptions of average returns than time-series models that use time-series factors. This is true when we impose constant factor loadings and when we use time-varying loadings that are natural for time-series factors and time-varying loadings that are ...
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作者：Lettau, Martin; Pelger, Markus
作者单位：National Bureau of Economic Research; University of California System; University of California Berkeley; Center for Economic & Policy Research (CEPR); Stanford University
摘要：We propose a new method for estimating latent asset pricing factors that fit the time series and cross-section of expected returns. Our estimator generalizes principal component analysis (PCA) by including a penalty on the pricing error in expected returns. Our approach finds weak factors with high Sharpe ratios that PCA cannot detect. We discover five factors with economic meaning that explain well the cross-section and time series of characteristic-sorted portfolio returns. The out-of-sample...
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作者：DeMiguel, Victor; Martin-Utrera, Alberto; Nogales, Francisco J.; Uppal, Raman
作者单位：University of London; London Business School; New Jersey Institute of Technology; Universidad Carlos III de Madrid; Universite Catholique de Lille; EDHEC Business School; Center for Economic & Policy Research (CEPR)
摘要：We investigate how transaction costs change the number of characteristics that are jointly significant for an investor's optimal portfolio and, hence, how they change the dimension of the cross-section of stock returns. We find that transaction costs increase the number of significant characteristics from 6 to 15. The explanation is that, as we show theoretically and empirically, combining characteristics reduces transaction costs because the trades in the underlying stocks required to rebalan...
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作者：Gu, Shihao; Kelly, Bryan; Xiu, Dacheng
作者单位：University of Chicago; Yale University; National Bureau of Economic Research
摘要：We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premiums. We demonstrate large economic gains to investors using machine learning forecasts, in some cases doubling the performance of leading regression-based strategies from the literature. We identify the best-performing methods (trees and neural networks) and trace their predictive gains to allowing nonlinear predictor interactions missed by other methods...
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作者：Daniel, Kent; Mota, Lira; Rottke, Simon; Santos, Tano
作者单位：Columbia University; National Bureau of Economic Research; University of Amsterdam
摘要：A common practice in the finance literature is to create characteristic portfolios by sorting on characteristics associated with average returns. We show that the resultant portfolios are likely to capture not only the priced risk associated with the characteristic but also unpriced risk. We develop a procedure to remove this unpriced risk using covariance information estimated from past returns. We apply our methodology to the five Fama-French characteristic portfolios. The squared Sharpe rat...
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作者：Chordia, Tarun; Goyal, Amit; Saretto, Alessio
作者单位：Emory University; University of Lausanne; Swiss Finance Institute (SFI); University of Texas System; University of Texas Dallas
摘要：We use information from over 2 million trading strategies randomly generated using real data and from strategies that survive the publication process to infer the statistical properties of the set of strategies that could have been studied by researchers. Using this set, we compute t-statistic thresholds that control for multiple hypothesis testing, when searching for anomalies, at 3.8 and 3.4 for time-series and cross-sectional regressions, respectively. We estimate the expected proportion of...
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作者：Freyberger, Joachim; Neuhierl, Andreas; Weber, Michael
作者单位：University of Wisconsin System; University of Wisconsin Madison; Washington University (WUSTL); University of Chicago
摘要：We propose a nonparametric method to study which characteristics provide incremental information for the cross-section of expected returns. We use the adaptive group LASSO to select characteristics and to estimate how selected characteristics affect expected returns nonparametrically. Our method can handle a large number of characteristics and allows for a flexible functional form. Our implementation is insensitive to outliers. Many of the previously identified return predictors don't provide ...
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作者：Karolyi, G. Andrew; Van Nieuwerburgh, Stijn
作者单位：Cornell University; Columbia University
摘要：The cross-section and time series of stock returns contains a wealth of information about the stochastic discount factor (SDF), the object that links cash flows to prices. A large empirical literature has uncovered many candidate factors-many more than seem plausible-to summarize the SDF. This special volume of the Review of Financial Studies presents recent advances in extracting information from both the cross-section and the time series, in dealing with issues of replication and false disco...
