COGRASP: Co-Occurrence Graph Based Stock Price Forecasting

Abstract

Forecasting stock prices is complex and challenging. Uncovering correlations among stocks has proven to enhance stock price forecasting. However, existing correlation discovery methods, such as concept-based methods, are slow, inaccurate, and limited by their reliance on predefined concepts and manual analysis. In this paper, we propose COGRASP, a novel approach for stock price forecasting that constructs stock co-occurrence graphs automatically by analyzing rapidly updated sources such as reports, newspapers, and social media. Besides, we aggregate forecasts across multiple timescales (i.e., long-, medium-, and short-term) to capture multi-timescale trends fluctuations, thereby enhancing price forecasting accuracy. In experiments with real-world open-source stock market data, COGRASP outperforms state-of-the-art methods.