Detecting and Analysing Possible Outliers in Global Stock Market Returns - Ali Shehadeh; Sadam M. Alwadi; Mohammad I. Almaharmeh (Cogent Economics & Finance, 2022)
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: G10; G15; G11; C19; 330; Outliers; outliers in stock returns; Boxplot; stock investment; international stock markets; daily stock market returns; global stock market indices