MCI-GRU: Stock prediction model based on multi-head cross-attention and improved GRU

Abstract

As financial markets become increasingly complex and the era of big data unfolds, accurate stock prediction has become more critical. Although traditional time series models, such as GRU, have been widely applied to stock prediction, they still exhibit limitations in addressing the intricate nonlinear dynamics of markets, particularly in the flexible selection and effective utilization of key historical information. In recent years, emerging methods like Graph Neural Networks and Reinforcement Learning have shown significant potential in stock prediction. However, these methods often demand high data quality and quantity, and they tend to exhibit instability when dealing with data sparsity and noise. Moreover, the training and inference processes for these models are typically complex and computationally expensive, limiting their broad deployment in practical applications. Existing approaches also generally struggle to capture unobservable latent market states effectively, such as market sentiment and expectations, microstructural factors, and participant behavior patterns, leading to an inadequate understanding of market dynamics and subsequently impact prediction accuracy. To address these challenges, this paper proposes a stock prediction model, MCI-GRU, based on a multi-head cross-attention mechanism and an improved GRU. First, we enhance the GRU model by replacing the reset gate with an attention mechanism, thereby increasing the model’s flexibility in selecting and utilizing historical information. Second, we design a multi-head cross-attention mechanism for learning unobservable latent market state representations, which are further enriched through interactions with both temporal features and cross-sectional features. Finally, extensive experiments conducted on the CSI 300 and CSI 500 datasets from the Chinese stock market, as well as the NASDAQ 100 and S&P 500 datasets from the U.S. stock market, demonstrate that the proposed method outperforms the current state-of-the-art methods across multiple metrics. Furthermore, this approach has been successfully applied in the real-world operations of a fund management company, validating its effectiveness and practicality in actual financial environments.