Impact of Artificial Intelligence on Stock Price Prediction in India

This study explores the impact of artificial intelligence (AI) on stock price prediction in India, focusing on the application of Long Short-Term Memory (LSTM) networks and hybrid models. By analyzing data from the Bombay Stock Exchange (BSE) including stock prices, trading volumes, and sentiment data from social media and news, the study compares AI-based models with traditional methods. The research aims to determine whether AI models offer superior predictive accuracy using RMSE, MAE, and MAPE. The findings suggest that AI models, particularly LSTM and hybrid approaches, outperform traditional models in forecasting accuracy, offering a significant advantage for stock market prediction in India. The study concludes AI models provide a significant improvement in stock price prediction accuracy compared to traditional methods. This suggests the adoption of AI models in stock price prediction by Indian financial institutions and regulatory bodies. This study provides valuable insights for investors, financial institutions, and policymakers looking to integrate AI into market forecasting strategies.

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Publication Details

Journal
Journal of finance and accounting
Published
2025-01-16
DOI
https://doi.org/10.12691/jfa-13-1-1
Primary Topic
Stock Market Forecasting Methods
Type
article
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0.00
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article

Impact of Artificial Intelligence on Stock Price Prediction in India

Amalendu Bhunia
Journal of finance and accounting
Stock Market Forecasting Methods
article

Impact of Artificial Intelligence on Stock Price Prediction in India

Amalendu Bhunia
article en

Abstract

This study explores the impact of artificial intelligence (AI) on stock price prediction in India, focusing on the application of Long Short-Term Memory (LSTM) networks and hybrid models. By analyzing data from the Bombay Stock Exchange (BSE) including stock prices, trading volumes, and sentiment data from social media and news, the study compares AI-based models with traditional methods. The research aims to determine whether AI models offer superior predictive accuracy using RMSE, MAE, and MAPE. The findings suggest that AI models, particularly LSTM and hybrid approaches, outperform traditional models in forecasting accuracy, offering a significant advantage for stock market prediction in India. The study concludes AI models provide a significant improvement in stock price prediction accuracy compared to traditional methods. This suggests the adoption of AI models in stock price prediction by Indian financial institutions and regulatory bodies. This study provides valuable insights for investors, financial institutions, and policymakers looking to integrate AI into market forecasting strategies.

Journal of finance and accountingVol. 13(1)
University of Kalyani (IN)
Decent work and economic growth
Openalex Percentile: Top 99%
Stock Market Forecasting Methods
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