ANALYSING THE SECTORAL PREDICTABILITY OF RISK AND RETURN IN INDIA

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1 International Journal of Management (IJM) Volume 7, Issue 4, May June 2016, pp , Article ID: IJM_07_04_016 Available online at Journal Impact Factor (2016): (Calculated by GISI) ISSN Print: and ISSN Online: IAEME Publication ANALYSING THE SECTORAL PREDICTABILITY OF RISK AND RETURN IN INDIA Dr. Prema Chandran Amity Global Business School, Bangalore, India INTRODUCTION Risk and return are both relevant to investment decisions. India has been an emerging nation and since liberalisation, there have been a number of reforms that have witnessed stock market reactions. This has caused both the risk and return of the different sectors of the Indian market to frequently change and become unpredictable. There is no clear answer to whether the risks and returns of these indices remain stable over a period of time. This study takes the daily returns of the ten different sector indices from the National Stock Exchange (NSE) in the Indian market for analysis. The NIFTY indices considered here include bank (Nifty Bank Index), Auto (Nifty Auto Index), Energy (Nifty Energy Index), Financial services (Nifty Financial Services Index), FMCG (Nifty FMCG Index), IT (Nifty IT Index), Media (Nifty Media Index), Metal (Nifty Metal Index), Pharma (Nifty Pharma Index) and Realty (Nifty Realty Index). The returns and the risk of the ten sectors are compared and analysed over a ten year period to examine their stability. According to NSE ( A stock market index is a measure of the relative value of a group of stocks in numerical terms. As the stocks within an index change value, the index value changes. An index is important to measure the performance of investments against a relevant market index. Sectoral indices are normally used to provide information about price movements or overall behaviour of the sector. The sectoral index is created by selecting a group of stocks that represent that specified sector of the market. Usually indices are calculated with reference to a base period and a base index value. The level of the index reflects the total free float market value of all the stocks in the index relative to particular base market capitalisation value. Table one gives a snapshot of the indices selected for the study as on 31 st March editor@iaeme.com

2 Dr. Prema Chandran Cite this Article: Dr. Prema Chandran, Analysing The Sectoral Predictability of Risk and Return In India. International Journal of Management, 7(4), 2016, pp Table 1 NSE Sectoral Indices a snapshot Sectoral indices of No. of Base Sectoral Market Base date NSE companies value Representation Representation Auto Jan 1, % 8.60% Bank Jan 1, % 15.60% Energy Jan 1, % 8.60% Financial services Jan1, % 19.20% FMCG Dec, % 8.60% IT # Jan, % 12.15% Media Dec, % - Metal Jan 1, % 2.60% Pharma Jan1, % 6.10% Realty Dec 29, % 0.40% Source: Computed from # The base value of the index was revised from 1000 to 100 with effect from May 28, REVIEW OF LITERATURE Guha et al (2016) used monthly closing prices of sectoral indices of NSE for data collected from Realty, Metal and IT sectors were found to be very sensitive and FMCG, Pharma and Auto were found to be defensive. Bora and Adhikary (2015) conducted an empirical study on the risk-return relationship using BSE Sensex companies. The monthly closing prices of the 30 companies was used to analyse the risk and returns for the period between 2010 and The findings revealed positive relation between security returns and market returns and betas were found to be unstable. Harish and Mallikarjunappa (2015) examined the beta stability of the Indian capital market. Using the BSE Sensex companies, the consistency of betas was examined for the period between 2000 to The study cautions portfolio managers on their use of historical betas. Bhowmik.D (2013) evaluated the framework of stock market volatility at the country level. According to the study volatility would be spurred by political turmoil or instability and high volatility reduces growth rate of the economy. Volatility also influences the volume of international trade and increases current account deficits. Shanmugasundram and Benedict (2013) conducted a study on the volatility of the sectoral indices with reference to NSE. In this study the risk relationship in different time intervals of the CNX NIFTY index and five sectoral indices including Auto index, Bank index, FMCG index, Infrastructure index and IT index was examined. The results of the study did not support any significant difference across the risk of sectoral indices and NIFTY. Swarna Lakshmi (2013) used the ARCH model to measure the volatility in NIFTY and other 11 select sectoral indices in India for the period 2008 to A conclusion was made on the 11 sectors volatility in comparison with the NIFTY and it was found that among the 11 sectors, the realty sector was the most volatile than any other sector. The paper also has discussions on the reasons for the same editor@iaeme.com

3 Analysing The Sectoral Predictability of Risk and Return In India Aman(2012) conducted a study on the stability of sector wise beta considering the sectors wise indices from Bombay Stock Exchange for the period between The study concluded that the FMCG, healthcare were the most defensive sectors, whereas realty and metal stocks were highly volatile. Chaturvedi and Jauhari (2012) investigated the issue of beta stability in the Bombay Stock Exchange from 2007 to The study considered monthly return data of 15 stocks and concluded that beta was instable over the different market phases. OBJECTIVES OF THE STUDY The study intends to analyse the risk and returns of the selected sectoral indices. The stability of returns and risk will also be examined. This is expected to throw light on the stability and behaviour of the major sectors. Risk of the indices is measured standard deviation of the returns and by calculating beta values of the sectoral indices to show the sensitivity of the sector returns. The final result will help in establishing the stability of risk and returns of the Indian sectoral indices. RESEARCH METHODOLOGY Data Daily closing prices of the selected sectoral indices of NSE have been taken for a ten year period from April 2006 to March Data for the realty sector starts from January 2007 as was available in the NSE website. Tools used for analysis For data analysis here the descriptive statistics with regard to daily closing prices average, high and low and the daily returns are used. Volatility is explained using standard deviation and beta. The NIFTY index returns and the sectoral indices returns are also calculated. Daily returns To measure daily returns of the sectoral indices and the NIFTY index as a percentage between any two days the difference between the closing index values of both the days divided by the preceding day was taken. Standard deviation as a measure of volatility Volatility is a measure of dispersion. If volatility is high, the risk of the sector is considered high as well. Here standard deviation is used as a tool to measure volatility. Standard deviation is measure of dispersion of a set of data from its mean. The formula for standard deviation is Where the standard deviation, x is is each value of the data set, is the mean value of the data set and n is the number of values in the data set editor@iaeme.com

4 Dr. Prema Chandran Beta as a measure of volatility Beta has also been calculated for the ten sectoral indices. Beta is also known systematic risk to indicate if the sectoral indices are more, or less volatile than the NIFTY. Here beta measures the volatility of the sectoral index to the market index (NIFTY). A beta of more than 1 is an indication that the stock s price is more volatile than the market. So for e.g., if the beta of a stock is 1.3, it is meant to be 30 percent more volatile than the market. The formula used to arrive at Beta ( ) is as follows: Where is Beta ) is the covariance between the market returns and sectoral index returns and is the variance of the market returns. All calculations have been done using MS Excel 2013 RESULT AND DISCUSSION With the objective of analysing the risk and returns of the sectoral indices listed in NSE, first the descriptive statistics are presented in table two below. Table 2 Risk and return statistics of daily returns of selected indices Index Maximum Minimum Mean Standard Deviation Coefficient of variation Skewness Kurtosis NIFTY Auto Bank Energy Financial Services FMCG IT Media Metal Pharma Realty Source: Computed The ex-post returns and the risk in terms of standard deviation and coefficient of variation are shown in the table. The maximum returns in the ten year period has been from the Metal index, followed by Realty, Financial services, Bank, and Energy sectors, in that order. The least returns have come from the FMCG, Pharma, IT, Media and Auto sectors in that order. An analysis of the standard deviation values shows that the Realty sector, Metal, Bank, Financial services and Media in that order have higher standard deviations meaning have relatively higher risks. When compared to the NIFTY risk and returns, the returns of Metal, Realty, Financial services and bank sectors have been higher that NIFTY in the ten year period. The standard deviation of these sectors also reflect higher volatility therefore higher risk. In order to analyse the risk of the sector, both the systematic and the unsystematic risks must be considered. It is a well-known fact that the sector specific risk or the editor@iaeme.com

5 Analysing The Sectoral Predictability of Risk and Return In India unsystematic risk is reflected in beta which shows the sensitivity of the sectoral return to the market return, whereas the systematic risk are caused by macro level economic factors. Table three below shows the beta calculations in the different phases as can be seen in the table. Table 3 Calculated Beta ( ) values for different time periods Time periods Auto Bank Energy Financial Services FMCG IT Media Metal Pharma Realty Last one year Last two years Last three years Last four years Last five years Last six years Last seven years Last eight years Last nine years Last ten years Source: Computed As reflected in the standard deviation values, the top four high risk industries with high returns namely: Realty, Metal, Bank, Financial services show higher beta values of greater than one on an average, meaning that the index is more volatile than the market (NIFTY). The average beta value of the different time period betas is shown in table four below CONCLUSION Table 4 Average beta value of the sectoral indices Average beta values Auto 0.93 Bank 1.25 Energy 1.00 Financial Services 1.22 FMCG 0.63 IT 0.69 Media 0.85 Metal 1.24 Pharma 0.60 Realty 1.57 Source: computed In this paper the index return volatility has been examined for the various sectors for ten years from April 2006 to March As far as the Indian stock market is concerned, the analysis shows that both the risk and the returns have been on the higher side for the Realty, Metal, Bank, and Financial services industries. Whereas editor@iaeme.com

6 Dr. Prema Chandran the least returns have come from the FMCG, Pharma, IT, Media and Auto sectors with their risks also remaining relatively lower over the ten year period of the study. The study has not considered specific economic or other related factors during the ten year period and has not analysed the reasons behind such movement. Further studies can be conducted in these lines. REFERENCES [1] Aman, S. (2012, July -Dec), Stability of Sector wise Beta: Case Study of India. GFJMR, 5 [2] Bhowmik, D. (2013). Stock Market Volatility: An Evaluation. International Journal of Scientific and Research Publications, 3(10). [3] Bora, B, & Adhikary, A. (2015), Risk and Return Relationship -An Empirical Study of BSE Sensex Companies in India. Universal Journal of Accounting and Finance, 3(2), [4] Chaturvedi, S. K., & Jauhari, S. (2012, January-June). A Study on Beta Instability Over Market Phases in Bombay Stock Exchange. IJRDMS, 6 (1), [5] Guha, B., Dutta, A., & Bandyopadhyay, G. (2016, March ). Measurement of Risk Vs Return of Indian Sectoral Indices. Journal of Advanced Management Science, 4(2), [6] Harish, S. N., & Mallikarjunappa, T. (2015). An Examination of the Beta Stability in the Indian Capital Market. Twelfth AIMS International Conference on Management, pp [7] Dr. Ramachandran Azhagaiah and Sandanam Gejalakshmi, The Relationship Between Dividend Policy And Shareholders Wealth: Evidence From Fmcg Sector In India. International Journal of Management, 6(1), 2015, pp [8] Cma. Dr. M. Sheik Mohamed and Mr. M. Kaja Muhaideen, A Study on Top Performed Equity FMCG Mutual Fund Schemes In India. International Journal of Management, 7(2), 2016, pp [9] Prema.C. (2016, April). A Study on the Volatility and Returns of the Indian Banking Sector Stock with Reference to NSE NIFTY. International Journal of Advance Research in Computer Science and Management Studies, 4 (4), [10] (n.d.) editor@iaeme.com