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.

翻译：我们利用深层的神经网络来估算个人股票回报的资产定价模型,该模型利用大量有条件信息,同时保持完全灵活的形式和计时时间变换。 关键创新是使用基本的无仲裁条件作为标准功能,以对抗方式构建信息最丰富的测试资产,并从许多宏观经济时间序列中抽取经济状态。 我们的资产定价模型在Sharpe比率方面优于所有基准方法,解释了差异和定价错误,并确定了驱动资产价格的关键因素。