Risk Modeling, Return Forecasting, and Optimal Portfolio Selection

An Empirical Study on Amman Stock Exchange

Authors

  • Fawaz Khalid Al Shawawreh Finance and Banking Department, Faculty of Business, Mutah University

DOI:

https://doi.org/10.35682/mjhss.v39i2.994

Keywords:

Risk Modeling, Forecasting Return, Optimal Portfolio

Abstract

The main objective of this research is to form an optimal investment portfolio consisting of a number of stocks selected according to specific criteria in Amman Stock Exchange and to test the ability of a various economic models to predict the performance of this portfolio in the foreseeable future. A time series for the return of the selected portfolio and for the return of a market index are formed. A set of tests were conducted to reach a stationary time series return and, then, to follow the Box-Jenkins methodology in order to build predictive models (ARMA) and to examine the residuals of models and to model them using ARCH and GARCH models to reach the best prediction of the performance of the portfolio and the market index in the forecasted periods. The data were tracked on a daily basis for the study sample and the market index simultaneously for a period of three years. Twenty-one companies were selected in the investment portfolio distributed among several sectors. The study concluded that the formed portfolio achieved a good diversification and gave a high return in relation to the lowest possible risk according to the Sharpe scale. Also, it is concluded that the model ARMA (1,1) is the most suitable for estimating market portfolio returns and forecasting risks for the market index return, and ARMA (2,1) and the model ARMA - GARCH are the most capable one of achieving good results that can be relied upon in tracking the performance of the studied investment portfolio.

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Published

2024-04-23

How to Cite

Al Shawawreh, F. K. . (2024). Risk Modeling, Return Forecasting, and Optimal Portfolio Selection: An Empirical Study on Amman Stock Exchange. Humanities and Social Sciences Series Mutah Lil-Buhuth Wad-Dirasat, 39(2). https://doi.org/10.35682/mjhss.v39i2.994

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Articles