摘要翻译：
深度学习为预测资产收益寻找非线性因素。可预测性是通过多层复合因素实现的，而不是加性因素。从这种角度来看，资产定价研究可以使用多层深度学习者来重新审视，如校正线性单元(ReLU)或针对时间序列效应的长-短-时记忆(LSTM)。最先进的算法包括随机梯度下降(SGD)、TensorFlow和dropout设计，提供了丰富和有效的因素探索。为了说明我们的方法，我们重温了Welch和Goyal(2008)的股票市场风险溢价数据集。我们发现非线性因素的存在性，特别是在特征空间的极值处，这些非线性因素解释了收益的可预测性。最后，对今后的研究方向进行了总结。
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英文标题：
《Deep Learning for Predicting Asset Returns》
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作者：
Guanhao Feng, Jingyu He, Nicholas G. Polson
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最新提交年份：
2018
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分类信息：

一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  Deep learning searches for nonlinear factors for predicting asset returns. Predictability is achieved via multiple layers of composite factors as opposed to additive ones. Viewed in this way, asset pricing studies can be revisited using multi-layer deep learners, such as rectified linear units (ReLU) or long-short-term-memory (LSTM) for time-series effects. State-of-the-art algorithms including stochastic gradient descent (SGD), TensorFlow and dropout design provide imple- mentation and efficient factor exploration. To illustrate our methodology, we revisit the equity market risk premium dataset of Welch and Goyal (2008). We find the existence of nonlinear factors which explain predictability of returns, in particular at the extremes of the characteristic space. Finally, we conclude with directions for future research.
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PDF链接：
https://arxiv.org/pdf/1804.09314