A Regression Analysis on the Car Index in the Tehran Stock Exchange - Salehpour, Arash; Salehpour, Elaheh (Journal of Soft Computing Paradigm, 2022)
One of the best ways to make money on the capital market is to buy shares on the stock exchange. The stock market has a nonlinear and chaotic system that is influenced by political, economic, and psychological conditions, and systems such as regression can be used to predict stock prices. In this research, different regression models are used, each of which measures information in a different way and tests the ability to predict the behaviour of index prices with this information. This paper examines linear regression, robust regression, ridge regression, polynomial regression, and elastic net on the historical daily data from 2018-07-01 to 2022-09-28 in the Car index of the Tehran Stock Exchange. Based on the empirical results, it is found that the best R2 score has been attained by the robust regression model. MSE, RMSE, MAE, and R2 for all models have been compared.
Keywords: Stock market prediction; Regression; Stock market; Machine learning; Tehran stock exchange