헤테로지니어스 다이나믹 그래프 뉴럴 네트워크를 활용한 국내 주가 예측

초록

This study designed a stock price prediction model for the Korean stock market using a heterogeneous dynamic graph neural network (HDGNN). To effectively capture the nonlinear characteristics of stock prices and various influencing factors, we utilized a dynamic graph structure that integrates data from stocks, economic indicators, news, and disclosures. Using daily data from January 2022 to December 2023 for KOSPI 200 stocks, we established relationships between nodes based on correlations and performed topic extraction with LDA and text embedding with FinBERT for news and disclosures. Temporal embeddings were generated using LSTM to reflect each node's time-series characteristics, while multi-level attention mechanisms were applied to integrate information at the node, temporal, and graph levels. Experimental results demonstrated that the HDGNN model achieved superior performance in MAE and RMSE metrics compared to existing models such as RNN, LSTM, and GRU, thereby enhancing the accuracy of stock price prediction.

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