Search

Temporal dependency and mutual impact are two major aspects of the qualitative analysis of stock prices. Modeling the dynamic and complex nature of both the timeline and the mutual impact network is a challenging task for stock price prediction. Furthermore, if we want to capture the high-frequency changes of stock price, the time series will become extremely long, leading to a practical challenge in terms of modeling. To address these challenges, this paper proposes a novel memory-based hierarchical recurrent neural encoder (MHieR-encoder) to embed the time series of stock price into a new representation that preserves (i) the sequential dependency of the time series and (ii) the proximity relationships across stocks in the impact network. The hierarchical structure of the proposed model can easily capture the long-range dependence from an extremely long time series, including in terms of high-frequency prices. Moreover, to capture the dynamic mutual impact across stocks, the intermediate results of the impact network will be stored in the memory module to support further exploration at the training stage. The method is validated using an extremely long time series composed of the one-minute prices derived from the all the Chinese stocks. The results show that MHieR-encoder outperforms all the 8 baselines in the bull market, bear market, calm bull market and calm bear market, and significantly improves the accuracy to 52.2% in a three-class prediction: rising, falling and flat. [ABSTRACT FROM AUTHOR]