Abstract This paper applies a linear alpha forecasting framework to enhance commonly used factor investing strategies by taking into account the informational content and interaction effects of selected firm characteristics. To demonstrate conditions under which it is beneficial to deviate from equally weighted characteristics, we evaluate a comprehensive number of factor portfolios. We consider four single-factor portfolios with 14 different firm characteristics in total and a multifactor portfolio where all factors are included. Empirically, the strategies are analyzed with the S&P 500, the Stoxx Europe 600 and the Nikkei 225 index. In addition, we also examine the strategies’ performance in a simulation experiment and investigate the properties of the information coefficient estimates as a measure of the informational content. The empirical results are consistent with the simulation results, which reveal that the overall portfolio performance can be improved in well-defined factor models with a high dispersion among the mean information coefficients of the firm characteristics. In contrast, the naive combination shows a comparable or better performance in factor models with a small dispersion in informational content between firm characteristics.

中文翻译：

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因子投资中的阿尔法预测：区分公司特征的信息内容

摘要 本文通过考虑所选公司特征的信息内容和交互作用，应用线性阿尔法预测框架来增强常用因子投资策略。为了展示偏离等权重特征的有利条件，我们评估了大量因子投资组合。我们考虑总共具有 14 个不同公司特征的四个单因子投资组合和一个包含所有因子的多因子投资组合。根据经验，这些策略使用标准普尔 500 指数、斯托克欧洲 600 指数和日经 225 指数进行分析。此外，我们还在模拟实验中检查了策略的性能，并研究了信息系数估计作为信息内容度量的属性。实证结果与模拟结果一致，表明在企业特征的平均信息系数之间具有高度离散性的定义明确的因子模型中，可以提高整体投资组合的绩效。相比之下，朴素的组合在因子模型中表现出可比或更好的性能，公司特征之间的信息内容差异很小。