DYCOR: Capturing Hidden Stock Relationships for Stock Trend Prediction

Abstract

Stock trend prediction, the task of forecasting future trends of stocks from their historical feature sequences, remains highly challenging due to the complex and dynamic nature of financial markets. In reality, stocks form diverse relationships that transcend traditional sector boundaries as market conditions evolve, i.e., stocks within the same sector may display different trends, while those in different sectors often exhibit similar movements. However, most existing stock prediction methods rely on predefined static relationships, lacking flexibility to adapt to changing market dynamics. Furthermore, objectives widely adopted in prior work have limitations in capturing complex patterns and relationships in stock market data. To address these limitations, we propose DYCOR, a novel stock trend prediction method that integrates two key innovations: (i) dynamic stock clustering, which captures market characteristics without relying on predefined relationship data by adaptively discovering hidden stock relationships; and (ii) correlation-aware training, which aligns predicted and ground-truth stock trends by reflecting their correlations in a fine-grained manner. We evaluate DYCOR on three datasets NASDAQ, NYSE, and S&P 500 widely used in existing research, and this method demonstrates superior performance across correlation-based and retrieval-based metrics compared to state-of-the-art baseline methods, while maintaining competitive runtime efficiency.