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<p>Stock market forecasting presents a significant challenge due to the inherent volatility and dynamic behavior of financial markets. Achieving accurate predictions is essential, as it directly influences investment strategies, risk mitigation practices, and broader economic planning. This paper provides a comprehensive review of advanced forecasting methodologies, with a particular emphasis on deep learning models. This survey analyses the evolution of these forecasting methodologies with emphasis on the latest Hybrid model era. The next-generation architectures that synthesize Convolutional Neural Networks for spatial feature extraction with attention-based LSTMs and Gated Recurrent Units are critically evaluated. Furthermore, the emergence of Transformer models and Reinforcement Learning in addressing the ‘stability-plasticity’ dilemma are examined. Comparative analysis reveals that hybrid systems integrating signal decomposition (e.g., VMD-LSTM) and domain-specific pre-processing significantly outperform standalone deep learning models in capturing market dynamics. The study concludes by identifying open challenges in model interpretability and edge deployment for high-frequency trading.</p>

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Decoding the Market Pulse: A Survey of Next-Gen Hybrid Machine Learning Architectures for Stock Forecasting

  • 期刊论文

  • 发表日期：

    2026年6月2日

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  • Shraddha Khonde,
  • Manisha Dudhedia,
  • Sarita Ambadekar,
  • Archana Kale,
  • Tanaji A. Dhaigude,
  • Vaishali Sonawane,
  • Sangita B. Nemade,
  • Yogiraj P. Patil,
  • Prithviraj P. Mulay,
  • Balaji K. Bodkhe,
  • S. H. Gawande

摘要

Stock market forecasting presents a significant challenge due to the inherent volatility and dynamic behavior of financial markets. Achieving accurate predictions is essential, as it directly influences investment strategies, risk mitigation practices, and broader economic planning. This paper provides a comprehensive review of advanced forecasting methodologies, with a particular emphasis on deep learning models. This survey analyses the evolution of these forecasting methodologies with emphasis on the latest Hybrid model era. The next-generation architectures that synthesize Convolutional Neural Networks for spatial feature extraction with attention-based LSTMs and Gated Recurrent Units are critically evaluated. Furthermore, the emergence of Transformer models and Reinforcement Learning in addressing the ‘stability-plasticity’ dilemma are examined. Comparative analysis reveals that hybrid systems integrating signal decomposition (e.g., VMD-LSTM) and domain-specific pre-processing significantly outperform standalone deep learning models in capturing market dynamics. The study concludes by identifying open challenges in model interpretability and edge deployment for high-frequency trading.