금리 변동을 반영한 LSTM 기반 주가 예측 모델 연구
(Stock Price Prediction Model Based on LSTM Reflecting Interest Rate Fluctuations)
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초록
최근 인플레이션의 영향으로 중앙은행이 금리 인상 기조를 유지하고 있으며, 이러한 경제 상황을 반영한 금융 시장 예측의 중요성이 커지고 있다. 그러나 국내에서는 최근 금리 변동을 고려한 주가 예측 연구가 부족한 실정이다. 본 연구에서는 주가 예측 모델에 금리를 변수로 추가하여 예측 정확도를 향상시키고자 하였다. 이를 위해 LSTM 알고리즘을 활용하였으며, 역사적으로 주가 변동이 컸던 시기의 데이터를 훈련하여 코스피 지수를 예측하였다. 또한, 시장 상황 및 섹터별 분석을 통해 금리의 영향을 다각도로 분석하였다. 다양한 평가지표를 사용하여 주가 예측과 등락 예측을 동시에 수행한 결과, 금리를 포함한 모델이 기존 모델에 비해 RMSE 기준 48.31% 오차가 감소하였으며, 예측 성능 차이는 통계적으로 유의한 것으로 확인되었다. 본 연구는 금리가 주가 예측에 중요한 변수임을 보여주며, 금리 변동을 포함한 모델이 예측 성능을 향상시킬 수 있음을 확인하였다.
Abstract
Due to the recent impact of inflation, central banks have maintained a policy of raising interest rates, highlighting the growing importance of financial market forecasting that reflects these economic conditions. However, there has been a lack of recent studies in Korea that consider interest rate in stock price prediction. In this study, we aimed to improve prediction accuracy by incorporating interest rates as a variable into stock price prediction models. For this purpose, we utilized the LSTM algorithm and trained the model on historical data from periods of high stock market volatility to predict the KOSPI index. Additionally, we conducted a multi-faceted analysis of the impact of interest rates through market conditions and sector-specific analyses. By employing various evaluation metrics, we performed both stock price prediction and directional movement prediction simultaneously. As a result, the model that incorporated interest rates demonstrated a 48.31% reduction in RMSE, compared to the baseline model, with the performance difference being statistically significant. This study confirms that interest rates are a critical variable in stock price prediction and that models reflecting interest rate fluctuations can enhance predictive performance.
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