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An essential economic pillar that greatly aids in the economic development of the country is the stock market. Based on historical data, stock market investments have yielded profitable outcomes. The ability to predict future stock market performance is crucial for optimizing investment returns. In this research, machine learning techniques for stock market prediction are compared and examined. Two stock market indices, the NIFTY and the NASDAQ, are exploited for research. The most accurate forecast was determined by comparing the outcomes of the machine learning algorithms for Linear Regression, Decision Trees, and Random Forests. It is observed that random forest gives better performance than other models. For the Nifty dataset, there is an increase of R2 score by 22 and 41% concerning Linear Regression and Decision Tree respectively. For the Nasdaq dataset, Random Forest gives a better performance of R2 score by 24 to 46% when compared with other models. Additionally explored is the association between the US and Indian stock market indices. Here, correlation analysis gives a positive score indicating a semi-direct link between the two stock indices. This specifies that the US economy has a big influence on the Indian economy.

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Correlation Analysis and Stock Market Prediction of Nifty and Nasdaq Indices Using Machine Learning Techniques

  • 会议论文

  • 发表日期：

    2025年

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  • Pranjali Kasture,
  • Kamini Shirsath

摘要

An essential economic pillar that greatly aids in the economic development of the country is the stock market. Based on historical data, stock market investments have yielded profitable outcomes. The ability to predict future stock market performance is crucial for optimizing investment returns. In this research, machine learning techniques for stock market prediction are compared and examined. Two stock market indices, the NIFTY and the NASDAQ, are exploited for research. The most accurate forecast was determined by comparing the outcomes of the machine learning algorithms for Linear Regression, Decision Trees, and Random Forests. It is observed that random forest gives better performance than other models. For the Nifty dataset, there is an increase of R2 score by 22 and 41% concerning Linear Regression and Decision Tree respectively. For the Nasdaq dataset, Random Forest gives a better performance of R2 score by 24 to 46% when compared with other models. Additionally explored is the association between the US and Indian stock market indices. Here, correlation analysis gives a positive score indicating a semi-direct link between the two stock indices. This specifies that the US economy has a big influence on the Indian economy.