In a factor timing context, academic research has focused on identifying a set of predictors that can explain the dynamics of factor portfolios. We propose an alternative approach for timing factor portfolio returns by exploiting the information from their portfolio characteristics. Different combinations of dimension reduction techniques are employed to independently reduce the number of both predictors and portfolios to predict. Characteristic-based models outperform existing methods in terms of exact predictability, as well as investment performance.

中文翻译：

---

具有投资组合特征的因子时机

在因子时序背景下，学术研究的重点是确定一组可以解释因子投资组合动态的预测因子。我们提出了一种通过利用来自其投资组合特征的信息来获取时间因子投资组合回报的替代方法。采用不同的降维技术组合来独立减少要预测的预测变量和投资组合的数量。基于特征的模型在精确的可预测性和投资绩效方面优于现有方法。