Calendar Anomalies in Financial Markets: A Bibliometric Analysis

Calendar anomalies are predictable pattern in stock returns, questioning the Efficient Market Hypothesis and enabling investors to enhance their trading strategies. There has been a proliferation of studies investigating these calendar-based effects across diverse financial markets in recent years. This paper aims to provide a comprehensive bibliometric analysis of the existing literature on calendar anomalies in financial markets to uncover insights on the relevant themes, influential authors, and emerging research fronts. The bibliometric analysis is grounded in 432 articles from the Scopus database related to seasonal anomalies, published between 1987 and 2023. The results indicate that academic research predominantly focuses on calendar effects within the stock market, with a significant amount of attention directed towards the US market. The review found a surge in research on calendar anomalies, with 89.99% of articles published between 2000-2022 with US being the dominant contributor totalling 86 publications and 2,075 citations. The study also reveals six prominent research themes, including the day of the week effects on global financial markets, seasonality in market returns and volatility, evidence of disappearing anomalies, explorations of the January effect, comparative studies of stock market seasonality across nations, and inquiries into seasonality effects during the late 1990s. Another key observation is the heterogeneity observed in the patterns of diverse calendar-based anomalies across international financial markets.

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Publication Details

Journal
International Journal of Economics and Financial Issues
Published
2025-04-12
DOI
https://doi.org/10.32479/ijefi.17669
Citations
1
Primary Topic
Complex Systems and Time Series Analysis
Type
article
Field-Weighted Citation Impact
3.13
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article

Calendar Anomalies in Financial Markets: A Bibliometric Analysis

Mujeeb Saif Mohsen Al-Absy, Mosab I. Tabash, M. V. Ayishana, Abdussalam P.K.
1 citations
International Journal of Economics and Financial Issues
Complex Systems and Time Series Analysis
3.13
article

Calendar Anomalies in Financial Markets: A Bibliometric Analysis

Mujeeb Saif Mohsen Al-Absy, Mosab I. Tabash, M. V. Ayishana, Abdussalam P.K.
article en
1 citations

Abstract

Calendar anomalies are predictable pattern in stock returns, questioning the Efficient Market Hypothesis and enabling investors to enhance their trading strategies. There has been a proliferation of studies investigating these calendar-based effects across diverse financial markets in recent years. This paper aims to provide a comprehensive bibliometric analysis of the existing literature on calendar anomalies in financial markets to uncover insights on the relevant themes, influential authors, and emerging research fronts. The bibliometric analysis is grounded in 432 articles from the Scopus database related to seasonal anomalies, published between 1987 and 2023. The results indicate that academic research predominantly focuses on calendar effects within the stock market, with a significant amount of attention directed towards the US market. The review found a surge in research on calendar anomalies, with 89.99% of articles published between 2000-2022 with US being the dominant contributor totalling 86 publications and 2,075 citations. The study also reveals six prominent research themes, including the day of the week effects on global financial markets, seasonality in market returns and volatility, evidence of disappearing anomalies, explorations of the January effect, comparative studies of stock market seasonality across nations, and inquiries into seasonality effects during the late 1990s. Another key observation is the heterogeneity observed in the patterns of diverse calendar-based anomalies across international financial markets.

International Journal of Economics and Financial IssuesVol. 15(3)
University of Calicut (IN), Al Ain University (AE), Gulf University (RS)
Openalex Percentile: Top 9%
Complex Systems and Time Series Analysis
3.13
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