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Forecasting stock market prices is complex due to daily changes influenced by economic trends, global events, and investor behaviour. Accurate predictions are crucial for informed decision-making, but achieving this accuracy is challenging. This study examines advanced stock market prediction techniques, analyzing the strengths and limitations of traditional statistical methods and machine learning algorithms to identify the most effective methods to capture market volatility and provide reliable forecasts. Traditional stock market prediction models struggle with unpredictability, but newer techniques like AI analytics and sentiment analysis offer real-time data and broader market influences. This paper explores methods for improving forecast accuracy by combining historical data with real-time insights, aiming to bridge the gap between theoretical models and practical applications.

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An In-Depth Review of Stock Market Prediction Using Advanced Machine Learning Models

  • 会议论文

  • 发表日期：

    2026年

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  • Chandresh Yadav,
  • Dheeraj Yadav,
  • Himanshu Saroj,
  • Shubham Kumar,
  • Shweta Tiwari,
  • Sudhakar Tripathi

摘要

Forecasting stock market prices is complex due to daily changes influenced by economic trends, global events, and investor behaviour. Accurate predictions are crucial for informed decision-making, but achieving this accuracy is challenging. This study examines advanced stock market prediction techniques, analyzing the strengths and limitations of traditional statistical methods and machine learning algorithms to identify the most effective methods to capture market volatility and provide reliable forecasts. Traditional stock market prediction models struggle with unpredictability, but newer techniques like AI analytics and sentiment analysis offer real-time data and broader market influences. This paper explores methods for improving forecast accuracy by combining historical data with real-time insights, aiming to bridge the gap between theoretical models and practical applications.