The Comparison of Methods Artificial Neural Network with Linear Regression Using Specific Variables for Prediction Stock Price in Tehran Stock Exchange

TL;DR

This study compares the effectiveness of Artificial Neural Networks and Linear Regression in predicting stock prices on Tehran's stock exchange, finding neural networks to be more efficient.

Contribution

The paper introduces a comparison between ANN and linear regression for stock prediction, using ICA to select relevant variables, which is a novel approach in this context.

Findings

ANN outperforms linear regression in prediction accuracy

ICA effectively selected key variables for modeling

Neural network provides more reliable stock price estimates

Abstract

In this paper, researchers estimated the stock price of activated companies in Tehran (Iran) stock exchange. It is used Linear Regression and Artificial Neural Network methods and compared these two methods. In Artificial Neural Network, of General Regression Neural Network method (GRNN) for architecture is used. In this paper, first, researchers considered 10 macro economic variables and 30 financial variables and then they obtained seven final variables including 3 macro economic variables and 4 financial variables to estimate the stock price using Independent components Analysis (ICA). So, we presented an equation for two methods and compared their results which shown that artificial neural network method is more efficient than linear regression method.