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Application of descriptive statistics methods to Kazakhstan stock market

https://doi.org/10.46914/1562-2959-2023-1-3-141-152

Abstract

The article discusses the methods of descriptive statistics and its application for the most liquid shares of the Kazakhstan stock market KASE (trading codes: HSBK, KZTK, CCBN, KZTO, KCEL, KEGC, KZKAK) from 2007 to 2022. Descriptive statistics are calculated for each stock, such as average return, variance, sample range, standard deviation, coefficient of variation, kurtosis, and skewness. The coefficients of the regression equation and the coefficient of determination R2 are estimated, which show the relationship between the market return and the return on an individual stock. The comparative analysis of index growth of stocks was carried out from 2013 to 2022. The histogram of the frequency distribution of KASE market returns based on annual and monthly periods and its empirical distribution function have been constructed. The hypothesis about the normal distribution of returns according to the Pearson criterion was tested. The distributions of returns for many stocks do not fully correspond to the normal distribution and have a positive skewness, i.e. have a long tail on the right side. The market curve has been constructed that characterizes the dependence of stock returns on the level of risk. According to this curve, it is established that HSBK stocks are the most risky of all the considered stocks, undervalued and attractive investments for investors using an aggressive investment strategy. KEGC stocks are less risky and very attractive investments for investors who use a more conservative investment strategy in the Kazakhstani stock market.

About the Author

A. O. Maulenov
International University of Information Technology
Kazakhstan

PhD, assistant professor.

Almaty



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Review

For citations:


Maulenov A.O. Application of descriptive statistics methods to Kazakhstan stock market. Bulletin of "Turan" University. 2023;(3):141-152. (In Russ.) https://doi.org/10.46914/1562-2959-2023-1-3-141-152

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ISSN 1562-2959 (Print)
ISSN 2959-1236 (Online)