Hybrid Deep Generative and Sequential Learning Approach for Stock Market Prediction

Stock market forecasting remains a significant challenge within the financial sector. The employment of nonlinear predictive techniques has become increasingly prevalent in this domain. This paper introduces a novel approach to stock prediction that leverages a latent space to address the complexities of high-dimensional and intricate stock data. Our methodology integrates Variational Auto-Encoders (VAE) with Long Short-Term Memory networks (LSTM) to first embed the original data into a latent space via VAE, followed by training an LSTM model within this space. This approach not only reduces the model's complexity but also enhances the efficiency of the training process. The proposed model's effectiveness is empirically validated through its application to the S&P 500 dataset, which represents the United States stock market.