Advanced International Journal for Research

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The New Frontier of Financial Forecasting: A Comprehensive Review of Deep Learning Architectures, Hybrid Models, and the Imperative of Explainable AI in Global Stock Markets

Author(s) Dr. Jaspal Singh
Country India
Abstract Accurate stock market forecasting remains one of the most intellectually challenging and financially critical tasks in quantitative finance, driven by the inherent volatility, non-linearity, and chaotic nature of financial time series data. Traditional statistical and econometric models (such as ARIMA) often prove inadequate in capturing the complex, long-term dependencies and multimodal influencing factors, leading to a paradigm shift toward advanced Deep Learning (DL)methodologies. This comprehensive review synthesizes recent research (2022–2025) across diverse global markets, including the S&P 500, the Indian National Stock Exchange (NSE), and cryptocurrency exchanges, to evaluate the efficacy of cutting-edge DL architectures. We focus on the performance of stand-alone Recurrent Neural Network (RNN) variants Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) alongside Convolutional Neural Networks (CNNs) and emerging hybrid models (e.g., CNN-LSTM, LSTM-DNN, LSTM-GNN, and Attention-based architectures). Furthermore, this paper details the critical role of multimodal data fusion, integrating market sentiment derived from financial news and social media (NLP), and addresses the growing demand for Explainable Artificial Intelligence (XAI) to foster transparency and trust in automated investment systems. The evidence overwhelmingly supports the superior predictive power and robustness of hybrid and deep recurrent models, affirming their role in advanced algorithmic trading and robust portfolio optimization.
Keywords Stock Market Prediction, Deep Learning, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Hybrid Models, Explainable AI (XAI), Financial Time Series, Algorithmic Trading, Sentiment Analysis.
Field Business Administration
Published In Volume 7, Issue 1, January-February 2026
Published On 2026-01-18
DOI https://doi.org/10.63363/aijfr.2026.v07i01.2947
Short DOI https://doi.org/hbk6z5
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