Adaptive Machine Learning Framework for Stock Market Prediction under Non-Stationary Conditions

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@article{1724981,
      author = {Victor Gbenga LIJOKA, Prof. B. C. Iloka, Dr. Hope Anyaragbu, Dr. Emmanuel Omeye, Dr. Emeka Okorie},
      title = {Adaptive Machine Learning Framework for Stock Market Prediction under Non-Stationary Conditions},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {5},
      pages = {2157-2167},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1724981.pdf},
      abstract = {This research seeks to contribute to the evolving landscape of financial forecasting by proposing an innovative and comprehensive machine learning framework for modeling and predicting stock market dynamics, while considering the impact of beta on model performance. With the unprecedented growth of financial data and the increasing complexity of global markets, traditional models are often limited in their ability to capture the intricate patterns and volatilities inherent in stock price movements. The primary objective of this research is to develop a robust and adaptive machine learning model that leverages advanced techniques in data analysis, feature engineering, and algorithmic optimization, while accounting for the effect of beta, to enhance the accuracy and reliability of stock market predictions. The study will explore a diverse range of machine learning methodologies, including but not limited to deep learning, ensemble methods, and reinforcement learning, to extract meaningful insights from historical market data and adapt to changing market conditions, with particular attention to how beta influences the performance of these methodologies.},
      keywords = {financial forecasting, stock market, machine learning, data analysis, algorithmic optimization},
      month = {November},
  }