Optimal Factor Timing in a High-Dimensional Setting
研究了在因子和预测变量数量众多的高维环境下，如何通过收缩方法有效进行股票因子择时，发现包括大市值股票因子在内的择时能带来显著收益。
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.