With the growing reliance on machine-learning (ML) methods in finance, an understanding of their long-term efficacy and underlying mechanism is needed. We document the time-varying importance of different stock characteristics over an 18-year (1998–2016) out-of-sample period to determine whether ML models, when trained on a large set of firm and trading characteristics, can consistently outperform factor models. Utilizing a combination of linear and nonlinear models, we form a ML portfolio that consistently generates a significant alpha against factor models, ranging from 2.14 to 2.74% per month. We uncover patterns in characteristic dominance that alternates between arbitrage and financial constraint features. The variation correlates with the US credit cycle, and highlights a fundamental economic mechanism underlying the ML portfolio’s performance. The study’s impact extends to both academics and practitioners, providing insights into the economic drivers of stock returns and the practical implementation of ML methods in portfolio construction.

中文翻译：

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使用机器学习产品组合探索因子动物园


随着金融领域对机器学习 （ML） 方法的日益依赖，需要了解其长期疗效和潜在机制。我们记录了在 18 年（1998-2016 年）的样本外期间不同股票特征的时变重要性，以确定 ML 模型在根据大量公司和交易特征进行训练时，是否能够始终优于因子模型。利用线性和非线性模型的组合，我们形成了一个 ML 投资组合，该投资组合始终如一地产生显着的 alpha against factor 模型，每月从 2.14% 到 2.74% 不等。我们揭示了在套利和财务约束特征之间交替的特征优势模式。这种变化与美国信贷周期相关，并凸显了 ML 投资组合表现背后的基本经济机制。该研究的影响延伸到学术界和从业者，提供了对股票回报的经济驱动因素以及 ML 方法在投资组合构建中的实际实施的见解。