Descriptive statistics to understand stock market performance

The stock price of a company reflects its financial performance in terms of capital value which is influenced by various economic and non-economic factors. Descriptive statistics aids in analysing stock performance for useful insights. A better understanding of the stock and market return trend will facilitate the allocation of financial sources to the most profitable investment opportunity. Thus, it is necessary to study fluctuations in the stock market to understand its behaviour (Shama and Sharma, 2019). However, before assessing the fluctuations in detail, there is also a requirement to condense the data set which contains a vast number of data points and observations.

Using descriptive statistics for stock examination

In contemporary times, a significant and ever-increasing amount of data is generated daily by various commercial, administrative, and scientific entities. This relentless growth is substantially expanding the sizes of databases. Statistical techniques have been employed to examine the nature, trend, variation, and distribution of such accumulating data.

Descriptive statistical techniques seek to furnish consolidated information, specifically by examining the average and dispersion of data. A measure of central tendency, dispersion, skewness, kurtosis, and correlation study are some of the conventional standards of descriptive statistics (Sarmento and Costa, 2017). It enables having basic information about the stocks by examining the returns and their volatility. Hence, descriptive analysis is commonly employed in stock market studies to conduct examinations of stock performance (Jagathi, 2019).

This study aims to examine the nature of stock and market data of companies from the energy and pharmaceutical sectors using descriptive analysis. Herein, Descriptive analysis was applied to abnormal return and average abnormal return to analyze the financial soundness of the companies under study and provide crucial insight. The descriptive measures used for the analysis were standard deviation, variance, min, max, skewness, and kurtosis.

Results of the descriptive analysis of the energy sector

The descriptive statistics for average abnormal return represent that on average the return for the energy sector is Rs. 449.63 witnessing huge deviation among the companies i.e. 620.37. The minimum average abnormal return is -44.28, while the maximum is 1451.68. The skewness value of the return is 0.64 showing positive skewness and slight deviation from symmetric distribution. Kurtosis value on the other hand is less than 3 representing that the curve of return is platkurtic i.e. with a flat top or the return values are spread. The descriptive statistics of average abnormal return shows that there is a huge variation in the total earns derived in the energy sector. Investing careful examination of each company is required to get more clarity.

The abnormal return provides the specific company-oriented details. Herein, the analysis reveals that the highest abnormal return could be derived from Bhel and Adani Power so these are the most profitable investments but the stocks also have the highest level of fluctuations with a standard deviation of more than 2000.

The lowest return is derived from Adani Green Energy and Kirloskar Ind as these have negative returns. Skewness reveals that for NHPC, NTPC, GVK Power, Power Gird, Ratan Power, and Reliance Power the level is approximately 0 therefore, the return of these stocks is symmetric.

The descriptive analysis reveals that only 2 companies in the energy sector offered the most returns which tend to provide benefits but these stocks due to high standard deviation also carry higher risks with them.

Results of the descriptive analysis of the pharma sector

In the descriptive analysis of the first pharmaceutical group, negative returns are observed, whereas the second group exhibits positive returns. The first group displays a higher standard deviation, indicating a wider spread of observations, whereas the second group has a comparatively lower standard deviation, signifying less spread of data. The skewness of the first group is negative, while that of the second group is positive. However, neither group’s skewness value is close to 0, suggesting a lack of symmetry.

Finally, the kurtosis value for each of these exceeds 3, indicating a leptokurtic curve characterized by a higher concentration of values near the mean. The analysis indicates that investing in the second group is advantageous due to its positive return and lower risk, as reflected by a smaller standard deviation. However, the identification of suitable companies for investment necessitates individual assessments of each company.

In evaluating the performance of each company, the abnormal return highlights Morphen Labs as the most favourable, displaying a positive return. Conversely, all other companies either exhibit very low returns or negative ones. Despite Morphen Labs providing benefits, it also carries risks due to the presence of a high standard deviation. The skewness of companies such as Amrutanjan Health, AstraZeneca, Bajaj Healthcare, Biofilm Chemicals, Gland Pharma, Hagsonpal Pharma, Lupin, Manglam Drugs, Mankind Pharma, Windlass Biotech, IOL Chemicals, Ind-Swift Labs, Shilpa Medicare, Syngene, and Themis Medicare is close to 0, indicating a symmetric distribution of returns. Furthermore, the kurtosis value for each company is either less or more than 3, suggesting an absence of symmetry.

Within the pharmaceutical sector, Morphen Labs stands out as the sole avenue for achieving higher abnormal returns, albeit accompanied by inherent risks.

Descriptive statistics of abnormal and average abnormal returns offer investors valuable insights into stock movements and help identify securities with the potential for higher returns. Summarized descriptions of company-wise abnormal returns facilitate comparisons among companies within the same industry, providing a snapshot of their respective performances. While the initial statistical tools provide meaningful information, there is a need for more detailed analysis using advanced statistical indices to explore the depth and relationships among observations in the dataset.

References

• Jagathi, P. H. (2019) ‘Descriptive analysis of stock market investor’, ICTATCT Journal of Management studies, 05(03), pp. 1068–1072. doi: 10.21917/ijms.2019.0147.
• Sarmento, R. and Costa, V. (2017) ‘Descriptive Analysis’, in Comparative Approaches to Using R and Python for Statistical Data Analysis, pp. 66–90. doi: 10.4018/978-1-68318-016-6.ch004.
• Shama, R. P. and Sharma, A. (2019) ‘Statistical Analysis of Stock Prices of Selected Companies in Construction Industry’, Advances in Management, 12(1), pp. 39–47.