Document Type : Research Paper
Authors
1
Department of Financial Management, School of Management and Economics, Islamic Azad University, Science and Research Branch, Tehran, Iran
2
Department of Financial Management, Yazd Branch, Islamic Azad University, Yazd, Iran
3
Department of Management and Accounting, Gonbad Kavus Branch, Islamic Azad University, Gonbad Kavus, Iran
4
Department of Economics, Bushehr Branch, Islamic Azad University, Bushehr, Iran
Abstract
Objectives: This study aims to optimize the settings of the nonlinear autoregressive network with exogenous inputs (NARX) model for predicting the next-day Tehran Stock Exchange Main Index (TEDPIX), compare its performance with the nonlinear autoregressive (NAR) and nonlinear input-output (NIO) models, extend the prediction horizon using the NAR model, and validate the NARX model through sensitivity analysis and performance comparison with the traditional autoregressive integrated moving average (ARIMA) model.
Methodology: This study uses TEDPIX data from 2009 to 2023. The NARX model was employed to predict the next day's index, and the NAR model was used to extend the forecasting horizon. The performance of the NARX model was compared to the NAR, NIO, and ARIMA models, using the percentage of absolute error as the evaluation metric. The mean squared error was used to determine the optimal settings for the NARX model and compare the performance of the NAR model with ARIMA.
Results: The findings indicate that the proposed NARX model, when combined with open, close, high, and low prices, trading volume, simple moving average, and exponential moving average, delivers the best prediction performance. Additionally, the Levenberg-Marquardt training algorithm achieves the highest accuracy. The model validation results confirm the superiority of the NARX algorithm over the NAR and NIO models and the traditional ARIMA model.
Originality: This study is the first to assess the relative performance of the NARX neural network for predicting TEDPIX, contributing to the field and offering an innovative tool for market analysts.
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