Forecasting movements of stock time series based on hidden state guided deep learning approach

Stock movement forecasting is usually formalized as a sequence prediction task based on time series data. Recently, more and more deep learning models are used to fit the dynamic stock time series with good nonlinear mapping ability, but not much of them attempt to unveil a market system's internal dynamics. For instance, the driving force (state) behind the stock rise may be the company's good profitability or concept marketing, and it is helpful to judge the future trend of the stock. To address this issue, we regard the explored pattern as an organic component of the hidden mechanism. Considering the effective hidden state discovery ability of the Hidden Markov Model (HMM), we aim to integrate it into the training process of the deep learning model. Specifically, we propose a deep learning framework called Hidden Markov Model-Attentive LSTM (HMM-ALSTM) to model stock time series data, which guides the hidden state learning of deep learning methods via the market's pattern (learned by HMM) that generates time series data. What is more, a large number of experiments on 6 real-world data sets and 13 stock prediction baselines for predicting stock movement and return rate are implemented. Our proposed HMM-ALSTM achieves an average 10% improvement on all data sets compared to the best baseline.

Automated summary

Stock movement forecasting is often treated as a sequence prediction task using time series data. While deep learning models have been increasingly employed for fitting dynamic stock time series, few of them have focused on understanding the internal dynamics of the market system. To address this, the proposed HMM-ALSTM framework integrates the Hidden Markov Model (HMM) into the deep learning process, allowing for the discovery of hidden states and patterns that contribute to the stock time series data.