An Empirical Study on Machine Learning Approaches for Indian Stock Market Forecasting

Abstract

With the mixing of machine learning (ML) algorithms, stock market forecasting has become an area of interest for researchers. This paper presents a review of the literature on ML techniques for price prediction in Indian stock markets. It analyses major algorithms such as linear regression, support vector machines (SVM), random forest, and neural network, as well as discussing their theoretical, advantageous and disadvantageous aspects. This research offers an exhaustive assessment of the implementation of diverse ML techniques for predicting trends in the Indian stock market. The swift changes and the nonlinear nature of stock markets make the problem of prediction extremely difficult. The author demonstrates how ML techniques are used to improve the accuracy of predictions by analysing historical data, sentiments of the market, and various technical indicators. Furthermore, it discusses issues such as crash in data, unpredictability of the market, and overfitting that create challenges for forecasting in ML. This review is aimed at providing substantial evidence in the implementation of various ML techniques adjusted to the Indian stock market for predicting stock price movements and providing information where further research is needed.

Keywords: Artificial intelligence in finance, Indian stock market, Machine learning, Stock market forecasting, Stock price prediction.

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