End-to-End Policy Learning of a Statistical Arbitrage Autoencoder Architecture
<|reference_start|>End-to-End Policy Learning of a Statistical Arbitrage Autoencoder Architecture: 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.<|reference_end|>
@article{krause2024end-to-end,
    title={End-to-End Policy Learning of a Statistical Arbitrage Autoencoder
  Architecture},
    author={Fabian Krause, Jan-Peter Calliess},
    journal={arXiv preprint arXiv:2402.08233},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.08233},
    primaryClass={q-fin.TR cs.LG}
}