Scholarlink

The prediction of stock prices is an evolving domain, especially in emerging nations such as India, where it substantially influences governmental, commercial, and individual investments. The precision of traditional statistical and econometric models diminishes due to the difficulties encountered in handling non-stationary financial time series data. This matter is resolved using machine learning techniques that utilize historical stock prices to enhance predictions. Debt or equity financing, encompassing dual registration on domestic and international exchanges, is how corporations procure capital in global markets. Precise stock price forecasts are essential for educated decision-making and financial progress in global markets, as investors in these equities enhance capital flow. The proposed system utilizes an iterative optimization procedure to seek optimal solutions in the hybrid LSTM model by dynamically adjusting the placements of wolves throughout the search space. The performance of the proposed model is quantitatively evaluated using statistical measures, including the accuracy, coefficient of determination R2, Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE).

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

An Optimized Learning Framework for Stock Market Prediction Using Multi-source Data and Grey Wolf Optimizer

  • 会议论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年

查看全文
  • Harmanjeet Singh,
  • Rupali Dhir,
  • Chander Prabha,
  • Balamurugan Balusamy,
  • Ahmad Alkhayyat,
  • Nithya Rekha Sivakumar

摘要

The prediction of stock prices is an evolving domain, especially in emerging nations such as India, where it substantially influences governmental, commercial, and individual investments. The precision of traditional statistical and econometric models diminishes due to the difficulties encountered in handling non-stationary financial time series data. This matter is resolved using machine learning techniques that utilize historical stock prices to enhance predictions. Debt or equity financing, encompassing dual registration on domestic and international exchanges, is how corporations procure capital in global markets. Precise stock price forecasts are essential for educated decision-making and financial progress in global markets, as investors in these equities enhance capital flow. The proposed system utilizes an iterative optimization procedure to seek optimal solutions in the hybrid LSTM model by dynamically adjusting the placements of wolves throughout the search space. The performance of the proposed model is quantitatively evaluated using statistical measures, including the accuracy, coefficient of determination R2, Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE).