This paper considers the problem of modeling a firm’s expected return as a nonlinear function of its observable characteristics. We investigate whether theoretically-motivated monotonicity constraints on characteristics and nonstationarity of the conditional expectation function provide statistical and economic benefit. We present an interpretable model that has similar out-of-sample performance to black-box machine learning methods. With this model, the data provide support for monotonicity and time variability of the conditional expectation function. Additionally, we develop an approach for characteristic selection using loss functions to summarize the posterior distribution. Standard unexplained volume, short-term reversal, size, and variants of momentum are found to be significant characteristics, and there is evidence this set changes over time.

中文翻译：

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特征对收益的单调影响

本文考虑将公司的预期收益建模为其可观察特征的非线性函数的问题。我们调查了理论上对条件期望函数的特征和非平稳性的单调性约束是否提供统计和经济利益。我们提出了一个可解释的模型，其样本外性能与黑盒机器学习方法相似。使用此模型，数据为条件期望函数的单调性和时间可变性提供了支持。此外，我们开发了一种使用损失函数来总结后验分布的特征选择方法。发现标准的无法解释的交易量，短期反转，规模和动量变化是重要特征，