Evaluation of the machine learning and deep learning models for forecasting the Indian stock market

Abstract

Stock market trading is one of the most integral financial transactions as it affects the economy. The stock market is extremely volatile and unpredictable, and using precise forecasting models seems to be the only way for investors to mitigate risk and maximise profits. Attaining complete accuracy in forecasting market trends is still a challenge, in spite of continuous efforts. However, in the past few years, the research on the development of data-driven models to enhance the accuracy of stock price forecasting has become a critical development, which has led to the development of Machine Learning (ML) and Deep Learning (DL) techniques. In this research, we have trained six different algorithms to predict the share prices of 11 highly successful companies in India, such as Support Vector Regression (SVR), Random Forest (RF), XGBoost, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) [4]. This study, it is based on the literature data for 5 years, which includes all the data of the stock market. The paper gives a brief summary of the technique, which includes the data pretreatment technique, the model development, as well as performance evaluation metrics. In this study, the two standard and data-driven methods to predict the stock market have been examined, and they have gained popularity in recent years’ stock market prediction studies. The implementation and the training of the model procedure are explained in detail and in a step-by-step fashion, and a comprehensive comparison of the selected algorithms is provided. The outcome of such techniques as the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) [16] outperform ordinary machine learning techniques in terms of accuracy and predictive performance. The number of acceptances is described in tabular form and graphically.