Navigating AI-Driven Financial Forecasting: A Systematic Review of Current Status and Critical Research Gaps

László Vancsura, Tibor Tatay () and Tibor Bareith
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László Vancsura: Department of Agricultural Logistics, Trade and Marketing, Institute of Agricultural and Food Economics, Hungarian University of Agriculture and Life Sciences, 7400 Kaposvár, Hungary
Tibor Tatay: Department of Statistics, Finances and Controlling, Széchenyi István University, 9026 Győr, Hungary
Tibor Bareith: HUN-REN, Centre for Economic and Regional Studies, Institute of Economics, 1097 Budapest, Hungary

Forecasting, 2025, vol. 7, issue 3, 1-49

Abstract: This systematic literature review explores the application of artificial intelligence (AI) and machine learning (ML) in financial market forecasting, with a focus on four asset classes: equities, cryptocurrencies, commodities, and foreign exchange markets. Guided by the PRISMA methodology, the study identifies the most widely used predictive models, particularly LSTM, GRU, XGBoost, and hybrid deep learning architectures, as well as key evaluation metrics, such as RMSE and MAPE. The findings confirm that AI-based approaches, especially neural networks, outperform traditional statistical methods in capturing non-linear and high-dimensional dynamics. However, the analysis also reveals several critical research gaps. Most notably, current models are rarely embedded into real or simulated trading strategies, limiting their practical applicability. Furthermore, the sensitivity of widely used metrics like MAPE to volatility remains underexplored, particularly in highly unstable environments such as crypto markets. Temporal robustness is also a concern, as many studies fail to validate their models across different market regimes. While data covering one to ten years is most common, few studies assess performance stability over time. By highlighting these limitations, this review not only synthesizes the current state of the art but also outlines essential directions for future research. Specifically, it calls for greater emphasis on model interpretability, strategy-level evaluation, and volatility-aware validation frameworks, thereby contributing to the advancement of AI’s real-world utility in financial forecasting.

Keywords: systematic literature review; machine learning; equities; cryptocurrencies; commodities; foreign exchange markets; research gaps (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2025
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