Detecting and Analysing Possible Outliers in Global Stock Market Returns

Abstract

We employ a Boxplot method for detecting and analyzing outlying daily returns of 14 international stock market indices sampled from around the world. The main objective of the paper is to provide an extensive analysis of the main characteristics, features and effects of the detected outlier returns. The results show that from about 4–10% of observations constitute outlying returns with an average of 6%. Conservatively, about 1.4% of return series are extreme outliers. Negative outliers are found more frequent, influential, severe and transmissible. The bulk of detected outliers are found to be in the magnitude of three standard deviations. Also, outliers tend to cluster together, both within individual return series over time and across stock markets. We find a sequential pattern in outlier occurrence within individual return series, and a concurrent pattern across stock markets. Moreover, adjusting for outlying returns leads to a decrease in standard deviation, negative skewness and kurtosis by about 18%, 74% and 69% on average, respectively. We do not find consistent evidence that advanced and well-developed stock markets have less frequent and/or sever outliers. Overall, the results and analysis of the paper provide important considerations about international stock market returns which are relevant to stock investment, portfolio and risk management. The results show that the best (worst) outlying returns which represent about only 1% of the return observations have an enormous effect on the stock return performance and realization.

Keywords:

• Outliers
• outliers in stock returns
• Boxplot
• stock investment
• international stock markets
• daily stock market returns
• global stock market indices

JEL Classification:

• G10
• G15
• G11
• C19

Public interest statement

Stock markets are among the main destinations for investment and portfolio management. Aberrant stock market fluctuations which result in outlying returns constitute very important issues to be examined and analyzed carefully. Having a better understanding of the features and effects of stock outlying returns is relevant to stock investment, portfolio and risk management as well as diversification decisions. For 14 international stock markets sampled from around the world, the paper provides an extensive analysis of the features, characteristics and effects of the detected outlying returns, including frequency, signs, magnitude, severity and influence. We also examine and analyze the potential patterns of outlying returns within individual return series as well as across the stock markets of our sample. The paper also examines the effect of outlying returns on some stock return statistics. Importantly, the paper examines the impact of outlying returns on the performance and realization of returns in stock markets.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Notes

1. A concern about the classical Boxplot method in detecting outliers is the asymmetry of data distribution. However, Walker et al. (2018) find that the classical Boxplot method perform well compared to other modified alternatives when sample sizes are large even with the existence of asymmetry in data distribution.

2. Although outliers can be replaced with zero, mean or median as usually suggested in the literature, we believe that our exercise is more realistic.

3. We exclude Brazil and Hong Kong as skewness in their original return series was positive but turned to negative in adjusted series.

Additional information

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Notes on contributors

Ali A. Shehadeh

Ali A. Shehadeh is an assistant professor at the Department of Finance, Faculty of Business, the University of Jordan/Aqaba. His research interests include international finance, financial markets, stock markets, corporate finance, financial analysis and financial economics. Among others, he has publications in International Review of Financial Analysis and Czech Journal of Economics and Finance.

Sadam M. Alwadi

Sadam M. Al-Wadi is an associate professor at the Department of Finance, Faculty of Business, the University of Jordan/Aqaba. His research focuses on modelling and forecasting financial time series and forecasting stock market decomposition.

Mohammad I. Almaharmeh

Mohammad I. Almaharmeh is an associate professor at the Department of Accounting, Faculty of Business, the University of Jordan/Aqaba. His research interests include earnings management, stock price informativeness, stock price synchronicity, IFRS, analyst coverage, and market-based research.