Accurate Stock Movement Prediction via Multi-Scale and Multi-Domain Modeling

How can we utilize historical stock prices for accurate stock movement prediction? There have been several attempts to predict the movement of a stock using historical stock prices. However, due to the erratic nature of the stock market, it is difficult to accurately predict the future trajectory of stocks. Existing works are limited in capturing complex dependencies that exist within the stock data.In this paper, we propose ZoomStock, an accurate method for stock movement prediction. ZoomStock captures complex patterns in stock price data with multi-scale and multi-domain modeling. First, ZoomStock captures multi-scale patterns by examining short-term, mid-term, and long-term dependencies from data. By feeding multi-scale data into a 1D convolution layer and applying a circular convolution on data, we learn patterns in various scales. Second, ZoomStock leverages multi-domain features to extract diverse patterns from stock data. In addition to finding patterns in time domain, ZoomStock exploits patterns in the frequency domain via Fourier Transform. We conduct extensive experiments on real-world datasets from diverse stock markets to evaluate the effectiveness of ZoomStock. Our results demonstrate that ZoomStock outperforms previous state-of-the-art models by up to 15.7%p in accuracy and 31.7%p in MCC, showcasing its superior predictive capabilities.