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Institutional Affiliation: University of Maryland
Information about this author at RePEc
NBER Working Papers and Publications
|January 2020||Factor Timing|
with , : w26708
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 which lead 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 is larger, more variable over time, and exhibits different cyclical behavior than estimates ignoring this fact. These results pose new challenges for theories that aim to match the cross-section of stock returns.
Published: Valentin Haddad & Serhiy Kozak & Shrihari Santosh, 2020. "Factor Timing," The Review of Financial Studies, vol 33(5), pages 1980-2018.
|November 2017||Shrinking the Cross Section|
with , : w24070
We construct a robust stochastic discount factor (SDF) that summarizes the joint explanatory power of a large number of cross-sectional stock return predictors. Our method achieves robust out-of-sample performance in this high-dimensional setting by imposing an economically motivated prior on SDF coefficients that shrinks the contributions of low-variance principal components of the candidate factors. While empirical asset pricing research has focused on SDFs with a small number of characteristics-based factors—e.g., the four- or five-factor models discussed in the recent literature—we find that such a characteristics-sparse SDF cannot adequately summarize the cross-section of expected stock returns. However, a relatively small number of principal components of the universe of potential ch...
Published: Serhiy Kozak & Stefan Nagel & Shrihari Santosh, 2019. "Shrinking the Cross-Section," Journal of Financial Economics, . citation courtesy of
|September 2017||Predicting Relative Returns|
with , : w23886
Across a variety of asset classes, we show that relative returns are highly predictable in the time series in and out of sample, much more so than aggregate returns. For Treasuries, slope is more predictable than level. For equities, dominant principal components of anomaly long-short strategies are more predictable than the market. For foreign exchange, a carry portfolio is more predictable than a basket of all currencies against the dollar. We show the commonly used practice to predict each individual asset is often equivalent to predicting only their first principal component, the index, which obscures the predictability of relative returns. Our findings highlight that focusing on important dimensions of the cross-section allows one to uncover additional economically relevant and statis...