Search published articles


Showing 2 results for Shafiei
Evaluation of the international Islamic capital market behavior in three periods of pre-COVID-19, COVID-19 and post-COVID-19 based on multi-fractal analysis
Dr. Mahdi Ghaemi Asl, Dr. Mohammad Nasr Esfahani, Ms. Elham Sadat Mirshafiei,
Volume 14, Issue 51 (5-2023)
Abstract
In this research, the behavior of the international Islamic capital market in the three periods before Corona, Corona and after Corona, as well as multi-fractal analysis is carried out on Sharia-compliant stock markets. Multifractal Detrended Fluctuation Analysis (MFDFA), Multiscale multi-fractal analysis (MMA), are the methods used in this study. We used the Dow Jones index data from 2011 to 2022, the variables are the emerging countries, developed countries, Asia Pacific, America and Europe. The research results shows that Corona has reduced the efficiency of all variables. In all periods, the variables are ineffective, except for the Asia variable in the pre-Corona period, developed countries and America in the post-Corona period. Also, all the variables had persistency in the Corona period. But in the pre-corona period, all the variables had an anti-persistency behavior, except for the variable of emerging countries, which had a persistence behavior, and the variable of Asia, which had a random behavior. In the post-corona period, all the variables have had an anti-persistence behavior, except for the variable of developed countries, which has had a random behavior.

Predicting the Price Index in the Iranian Stock Market with Emphasis on the Monetary Variables: A Machine Learning Approach
Majid Shafiei, Parviz Rostamzadeh, Mohammad Rastegar, Zahra Dehghan Shabani,
Volume 14, Issue 53 (11-2024)
Abstract

The stock market, as one of the vital components of the capital market, is an important part of the country's economy that can manage the flow of capital, optimize capital allocation, and thereby contribute to economic growth and development. More accurate prediction of the stock market trend can help investors' decision-making for higher returns by reducing risk. In general, the stock market is constantly changing and many factors influence the trend of this market, so predicting the patterns of movement in the stock exchange requires sufficient information about the past and influencing factors of the market. This article is part of the forecast of the stock market index of Iran, seeking to interpret the model and identify the most influential economic variable on the price index prediction. For this purpose, daily stock market and economic data, during the period 1394-1401 were used. Machine learning models are also used for prediction and the Shapley Additive exPlanations (SHAP) to interpret how to predict and determine the most important variables in the predictive model. Based on results from tree-based ensemble methods, the proposed model in this study, ExtraTrees, performed best based on predictive error criteria. In the study of the feature importance is also based on the ExtraTrees model, in order of the dollar rate (Nima), unemployment rate, dollar rate of market and liquidity, the most important economic variables influencing the forecast model. Also, according to other models used in the research, liquidity is the most effective variable on the stock index trend. Finally, it can be said that the most effective monetary variables on the stock market index in Iran are liquidity and exchange rate variables, so monetary policymakers and stock market investors should be more sensitive to these variables in their decisions.

Page 1 from 1     

© 2025 CC BY-NC 4.0 | Journal of Economic Modeling Research

Designed & Developed by : Yektaweb