Enhancing stock market Forecasting: A hybrid model for accurate prediction of S&P 500 and CSI 300 future prices - Ge, Qing (Expert systems with applications, 2025)
This paper investigates the challenging domain of stock market prediction, a significant aspect of financial markets. It focuses on developing predictive models to forecast stock prices accurately, vital for mitigating losses and maximizing gains amidst the inherent unpredictability and volatility of the market. The study comprehensively analyzes various predictive models, including time series analysis and advanced machine learning techniques. It highlights the superiority of ensemble or hybrid models in enhancing prediction reliability. Central to this research is the development of a model incorporating detailed data collection, thorough analysis, and state-of-the-art machine learning methods, achieving notable predictive accuracy. This approach underscores the benefits of data-centric strategies in today’s rapidly evolving business environment and the widespread applicability of predictive analytics. The model outperforms conventional methods by decomposing time series into simpler components and optimizing hyperparameters, thereby enhancing prediction accuracy, as demonstrated by performance testing on the S&P 500 and CSI 300 indices. The RMSE, MAE, and R2 values of the MEME-AO-LSTM model are 27.12, 19.43, and 0.992, respectively, which serve as evidence of this. The model’s generalizability and high performance are demonstrated by its efficacy in a variety of major markets, including the NASDAQ 100, Nikkei 225, FTSE, DAX, SSE, and KOSPI. Additionally, the model’s adaptability under diverse market conditions is demonstrated through its evaluation of its robustness in response to significant events, such as the economic stimulus responses to the COVID-19 pandemic and the geopolitical tensions resulting from the tension and conflict between Russia and Ukraine. Consequently, the proposed methodology has the potential to help investors achieve substantial and advantageous returns.