Stock-Index Tracking Optimization Using Auto-Encoders

Abstract

Deep learning algorithms’ powerful capabilities for extracting useful latent information give them the potential to outperform traditional financial models in solving problems of the stock market which is a complex system. In this paper, we explore the use of advanced deep learning algorithms for stock-index tracking. We partially replicate the CSI 300 Index by optimizing with respect to the difference between the returns of the tracking portfolio and the target index. We extract the complex nonlinear relationship between index constituents and select a subset of constituents to construct a dynamic tracking portfolio by six well-known auto-encoders (single-hidden-layer undercomplete, sparse, contractive, stacked, denoising, and variational auto-encoders) that have been widely used in contexts other than stock-index tracking. Empirical results show that the auto-encoder-based strategies perform better than conventional ones when the tracking portfolio is constructed with a small number of stocks. Furthermore, strategies based on auto-encoders capable of learning high-capacity encodings of the input, such as sparse and denoising auto-encoders, have even better tracking performance. Our findings offer evidence that deep learning algorithms with explicitly designed hierarchical architectures are suitable for index tracking problems.