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 non-linear 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.

深度学习算法具有强大的功能，可以提取有用的潜在信息，因此在解决复杂的股票市场问题方面具有超越传统财务模型的潜力。在本文中，我们探索了如何使用高级深度学习算法进行股指追踪。我们通过优化跟踪投资组合的收益与目标指数之间的差异来部分复制CSI 300指数。我们提取索引成分之间的复杂非线性关系，并选择一组成分的子集，以通过六个著名的自动编码器（单隐藏层不完全，稀疏，收缩，叠加，去噪和变分自动）来构建动态跟踪组合-encoders）已广泛用于除股指跟踪之外的其他环境中。实证结果表明，当跟踪投资组合由少量股票构成时，基于自动编码器的策略的性能要优于传统策略。此外，基于能够学习输入的大容量编码的自动编码器（例如稀疏和去噪自动编码器）的策略甚至具有更好的跟踪性能。我们的发现提供了证据，即具有明确设计的层次结构的深度学习算法适用于索引跟踪问题。