Yongchareon, Sira2025-07-092025-07-092025-07-01Machine Learning and Knowledge Extraction, ISSN: 2504-4990 (Print); 2504-4990 (Online), MDPI AG, 7(3), 61-61. doi: 10.3390/make70300612504-49902504-4990http://hdl.handle.net/10292/19493The integration of artificial intelligence (AI) and advanced deep learning techniques is reshaping intelligent financial forecasting and decision-support systems. This study presents a comprehensive comparative analysis of advanced deep learning models, including state-of-the-art transformer architectures and established non-transformer approaches, for long-term stock market index prediction. Utilizing historical data from major global indices (S&P 500, NASDAQ, and Hang Seng), we evaluate ten models across multiple forecasting horizons. A dual-metric evaluation framework is employed, combining traditional predictive accuracy metrics with critical financial performance indicators such as returns, volatility, maximum drawdown, and the Sharpe ratio. Statistical validation through the Mann–Whitney U test ensures robust differentiation in model performance. The results highlight that model effectiveness varies significantly with forecasting horizons and market conditions—where transformer-based models like PatchTST excel in short-term forecasts, while simpler architectures demonstrate greater stability over extended periods. This research offers actionable insights for the development of AI-driven intelligent financial forecasting systems, enhancing risk-aware investment strategies and supporting practical applications in FinTech and smart financial analytics.© 2025 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).https://creativecommons.org/licenses/by/4.0/3801 Applied Economics35 Commerce, Management, Tourism and Services38 Economics3502 Banking, Finance and Investment46 Information and Computing SciencesMachine Learning and Artificial IntelligenceNetworking and Information Technology R&D (NITRD)Journal ArticleOpenAccess10.3390/make7030061