Authors propose a framework for equity factor timing in a high-dimensional setting, using shrinkage to enhance out-of-sample performance. Empirical results show sizable gains, even for factors built solely from large-cap stocks.
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Abstract
We develop a framework for equity factor timing in a high-dimensional setting when the number of factors and factor return predictors can be large. To ensure good out-of-sample performance, the approach is disciplined by shrinkage that effectively expresses a degree of skepticism about outsized gains from timing. In our empirical application, the predictors include macroeconomic variables and factor-specific characteristics spreads between the long and short legs of the factors. We find sizable gains from timing equity factors, including for factors constructed only from large-cap stocks.