Abstract
The use of artificial intelligence (AI) techniques for stock market prediction has gained increasing attention in recent years, and the Indian stock market is no exception. In this paper, we present a comparative study of three AI techniques, namely, Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), and Random Forests (RFs), for Indian stock market prediction. The study is based on historical data of the National Stock Exchange (NSE) Nifty 50 index from 2000 to 2021. The performance of the techniques is evaluated using various metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Directional Accuracy (DA). Our results show that ANNs outperform both SVMs and RFs in terms of prediction accuracy and DA for the Indian stock market.
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