统计套利自编码器架构的端到端策略学习
End-to-End Policy Learning of a Statistical Arbitrage Autoencoder Architecture
AI总结：
研究将自编码器架构应用于统计套利，通过策略学习将自编码器嵌入投资组合交易策略的神经网络中，实现端到端训练，简化策略开发并提升总回报。
摘要：
在统计套利（StatArb）中，经典的均值回归交易策略通常依赖于资产定价或基于PCA的模型来识别合成资产的均值。一旦识别出这种（线性）模型，就会设计一个单独的均值回归策略来生成交易信号。为了推广这种方法并使其真正成为数据驱动的，我们研究了自编码器架构在StatArb中的效用。作为第一种方法，我们采用在美国股票回报上训练的标准自编码器，基于Ornstein-Uhlenbeck（OU）过程推导交易策略。为了进一步增强该模型，我们采用策略学习方法，并将自编码器网络嵌入到投资组合交易策略空间的神经网络表示中。这种整合直接输出投资组合分配，并可通过神经网络策略的风险调整回报的反向传播进行端到端训练。我们的研究结果表明，这种创新的端到端策略学习方法不仅简化了策略开发过程，而且产生了优于其竞争对手的总回报，说明了端到端训练相对于经典两阶段方法的潜力。
英文摘要：
In Statistical Arbitrage (StatArb), classical mean reversion trading strategies typically hinge on asset-pricing or PCA based models to identify the mean of a synthetic asset. Once such a (linear) model is identified, a separate mean reversion strategy is then devised to generate a trading signal. With a view of generalising such an approach and turning it truly data-driven, we study the utility of Autoencoder architectures in StatArb. As a first approach, we employ a standard Autoencoder trained on US stock returns to derive trading strategies based on the Ornstein-Uhlenbeck (OU) process. To further enhance this model, we take a policy-learning approach and embed the Autoencoder network into a neural network representation of a space of portfolio trading policies. This integration outputs portfolio allocations directly and is end-to-end trainable by backpropagation of the risk-adjusted returns of the neural policy. Our findings demonstrate that this innovative end-to-end policy learning approach not only simplifies the strategy development process, but also yields superior gross returns over its competitors illustrating the potential of end-to-end training over classical two-stage approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
