Ramadan effect and indices movement estimation: a case study from eight Arab countries


Al- Najjar D., Assous H. F., Al-Najjar H., Al-Rousan N.

Journal of Islamic Marketing, vol.14, no.8, pp.1989-2008, 2023 (ESCI) identifier

  • Publication Type: Article / Article
  • Volume: 14 Issue: 8
  • Publication Date: 2023
  • Doi Number: 10.1108/jima-01-2022-0008
  • Journal Name: Journal of Islamic Marketing
  • Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus, ABI/INFORM, Index Islamicus
  • Page Numbers: pp.1989-2008
  • Keywords: Arab countries, Ramadan effect, Stock market prediction, Weight least square
  • Istanbul Gelisim University Affiliated: Yes

Abstract

© 2022, Emerald Publishing Limited.Purpose: This study aims to investigate the Ramadan effect anomaly on the stock markets’ indices and estimate the movement of these indices in the light of the phenomenon. Design/methodology/approach: Stock market indices are used as financial indicators to show the Ramadan effect. To validate this effect, eight Arab countries, which comprises Jordan, Saudi Arabia, Oman, Qatar, United Arab Emirates, Bahrain, Kuwait and Egypt, are adopted. A linear regression with R2, error, F-value and p-value is considered to analyze and understand the effect of Ramadan on the aforementioned Arab countries. Findings: Results found that Ramadan has a strong effect on estimating and predicting the performance of stock market indices in all studied Arab countries, except Kuwait. Results found that the majority of the Ramadan effect occurred after the second 10 days of Ramadan, where the direction of stock indices is opposite of Ramadan variables in all aforementioned cases. Originality/value: This study is considered as an enrichment of the existing literature review with regard to the Ramadan effect. The study presents a new methodology that can be followed to improve the predictions of stock market indices by using a weight least square method with linear regression. This study presents the most affected periods of time that could decrease or increase the stock prices. Finally, the study proves the capability of the weight least square method in building a predictive model that takes the date into consideration in predicting stock market indices.