英文标题：
《Deep Learning for Forecasting Stock Returns in the Cross-Section》
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作者：
Masaya Abe, Hideki Nakayama
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最新提交年份：
2018
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英文摘要：
  Many studies have been undertaken by using machine learning techniques, including neural networks, to predict stock returns. Recently, a method known as deep learning, which achieves high performance mainly in image recognition and speech recognition, has attracted attention in the machine learning field. This paper implements deep learning to predict one-month-ahead stock returns in the cross-section in the Japanese stock market and investigates the performance of the method. Our results show that deep neural networks generally outperform shallow neural networks, and the best networks also outperform representative machine learning models. These results indicate that deep learning shows promise as a skillful machine learning method to predict stock returns in the cross-section.
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中文摘要：
许多研究已经通过使用机器学习技术（包括神经网络）来预测股票收益率。近年来，一种称为深度学习的方法在机器学习领域引起了人们的关注，该方法主要在图像识别和语音识别方面取得了很高的性能。本文在日本股市的横截面上实现了深度学习来预测一个月前的股票收益率，并对该方法的性能进行了研究。我们的结果表明，深度神经网络通常优于浅层神经网络，最好的网络也优于典型的机器学习模型。这些结果表明，深度学习作为一种熟练的机器学习方法，有望预测横截面的股票收益率。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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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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