MATCC: A Novel Approach for Robust Stock Price Prediction Incorporating Market Trends and Cross-time Correlations

Stock price prediction has been a challenging problem due to non-stationary dynamics and complex market dependencies. Existing work has two limitations: 1. Previous studies have underestimated the importance of market trends, relying solely on stock data to learn patterns and capture market regularities implicitly. However, due to random stock fluctuations and trading noise caused by market sentiment, it is difficult to learn underlying market trends, resulting in poor model performance. 2. Prior research has predominantly concentrated on time-aligned feature correlations, with limited exploration of cross-time stock correlations. To address these issues, we propose a novel framework, MATCC (Market Trend and Cross-time Correlation model). It explicitly extracts market trends as guiding information, decomposes stock data into trend and fluctuation components, and employs a carefully designed structure for mining cross-time correlation. Extensive experiments demonstrate that MATCC significantly outperforms previous works in both ranking and portfolio-based metrics. Additionally, we illustrate the influence of trends and correlations on stock prediction through visualization. We publish our code at https://github.com/caozhiy/MATCC.