英文标题：
《Deep Learning in Asset Pricing》
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作者：
Luyang Chen, Markus Pelger and Jason Zhu
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最新提交年份：
2021
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英文摘要：
  We use deep neural networks to estimate an asset pricing model for individual stock returns that takes advantage of the vast amount of conditioning information, while keeping a fully flexible form and accounting for time-variation. The key innovations are to use the fundamental no-arbitrage condition as criterion function, to construct the most informative test assets with an adversarial approach and to extract the states of the economy from many macroeconomic time series. Our asset pricing model outperforms out-of-sample all benchmark approaches in terms of Sharpe ratio, explained variation and pricing errors and identifies the key factors that drive asset prices.
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中文摘要：
我们使用深度神经网络来估计单个股票收益的资产定价模型，该模型利用了大量的条件信息，同时保持了完全灵活的形式并考虑了时间变化。关键的创新是使用基本无套利条件作为标准函数，以对抗性方法构建信息量最大的测试资产，并从许多宏观经济时间序列中提取经济状态。我们的资产定价模型在夏普比率方面优于样本外的所有基准方法，解释了变化和定价错误，并确定了驱动资产价格的关键因素。
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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：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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