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Optimal Factor Timing in a High-Dimensional Setting
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DOI：10.1080/0015198X.2025.2474385.png)
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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.
Keyword:
equity factors
factor timing
shrinkage
machine learning
out-of-sample performance
2.0
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2.2
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1.2K
被引数:
3.1K
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