Financial Performance Drives Market Performance-An Evidence from Indian Industries

Transcription

1 Volume 116 No , ISSN: (printed version); ISSN: (on-line version) url: ijpam.eu Financial Performance Drives Market Performance-An Evidence from Indian Industries 1 E. Geetha and 2 Satish Kumar 1 Department of Commerce, Manipal University, Manipal. geetha.e@manipal.edu 2 Department of Commerce, Manipal University, Manipal. Abstract The use of technical analysis enables the practitioners such as investors, financial analysts, and traders, to formulate a basic trendline, to help identify how the prices of the stocks would change. Fundamental analysis, on the other hand, uses the resources provided by the company s financial reports such as annual growth, Revenue, and expenses etc., to provide evidence of price fluctuation in the near future. The paper aims to understand the trends in the market fluctuation of three major sectors of the Indian market. The paper investigates the dependence of the change in the market price of a share due to factors such as Eps and Profit. A correlation analysis is carried out to understand the extent to which the earnings per share and the profits of the company affect the average prices of the same company. Overriding the significance of the correlation between the factors a regression analysis is also conducted to identify the association between the prices and EPS/Profit. Further, a regression analysis is conducted to analyze the relationship between Average Price of a share and the following factors: Dividend Paid per share, Dividend Yield per Share, Book value of the share, EPS, Profit and Return on Equity. Key Words:Technical, financial, market price, EPS,DPS. 787

2 1. Introduction The use of Technical and financial analysis to predict the movement of share prices in the market has been an age-old trend. The use of such tools enables the practitioners such as investors, financial analysts, and traders, to formulate a basic trendline, to help identify how the prices of the stocks would change. Fundamental analysis approach uses the resources provided by the company s financial reports such as annual growth, Revenue, and expenses etc. (Murphy, 1999). However, the technical analysis is solely based on the historic prices of the share and involves the use of trend analysis and identification of recurring patterns. This information is then used to predict the possible value of the shares (Turner, 2007). This paper focuses on highlighting the correlation between the Earnings per share of a firm and Profits of the firm with the average prices of the same firm. Three of the booming sectors of the Indian market were identified and 10 firms each were chosen based on their market capitalization. A correlation analysis was carried forward between the firm s average opening price, average closing price, average high price and average low price. Further regression analysis was conducted to identify the dependence of the average share price of the firm with the factors such as dividend yield ratio, dividend per share, the book value of the share and the Eps of the firm specifically for the banking industry. Earnings per share are the part of the profit that has been given out to each common share after deducting the net taxes and preferred share dividends. It is calculated by dividing the net income (fewer taxes and preferred dividend) by the total number of shares outstanding in the market. Accounting Profit is calculated as a difference between the revenue and the expenses of a firm. Here explicit costs are considered i.e. those incurred due to the production and sale of goods and services by the firm. The taxable income and the financial performance of the firm. 2. Review of Literature The effect of a change in price due to the dividend stream was first mapped by Gordon (Gordon, 1959) since there have been multiple models developed in this field. A positive and non-linear relationship was found between the stock prices, its returns and the expected dividend yield from that investment made. The prediction of the dividends, however, is based solely on the information that the investor had ex-ante. (Litzenberger & Ramaswamy, 1982). Some researchers also argue that changes in the financial markets are purely based on the investment trends followed by the investors. Though the market patterns give a basic understanding of the market trends the relevance of the prediction is not absolute (Chitra, 2011). 788

3 The use of the technical analysis to identify the profitability of an investment made in a particular share helps investors identify and recognize the market patterns and make a successful investment. The use of technical indicators which are published by the firms themselves helps predict the trends and movement of the shares (Pandya, 2013).The analysis of the right data in the right manner under that guideline of technical analysis can help investors gauge the short term and medium term movement in the share prices. This enables the investor to take the right investment decision which is beneficial and remunerative in nature (Boobalan, 2014).In a study done by Anup Kumar Saha and Ashiquer Rahman Bhuiyan showed that there is a positive relationship between Dividend Yield Ratio (1% sig.) and Earnings Per Share (5% sig.) with the price of the share (Saha & Bhuiyan, 2014). Some researchers have also found that factors such as the rate of exchange of the currency of a country have an impact on the price of the shares for a company which is listed on that particular stock exchange. However, there is no dependency on the value of the dollar or the stock cannot be used to predict the value of the stock in the near future. (Nieh & Lee, 2001). 3. Objective of the Study Analyzing the market trends of the Banking, Pharma and Cement Sector. Finding the relationship between the average prices of the company with its EPS and Profits To examine the correlation, dependence and significance between EPS and Profit with that of the average prices of the company Identifying the relationship between Average price and the following factors: Dividend Paid per share, Dividend Yield per Share, Book value of the share, EPS, Profit and Return on Equity. 4. Methodology The paper has been completed in three parts as shown in the flow chart: Identification of companies Data sourcing for each company Analysis and Interpretation of data a. Identification of companies within the three industries The market capitalization of each of the banks, cement factors, and pharma companies was found and based on their market capitalization as on 6th September The study restricted itself to the companies listed in the national Stock Exchange of India. The following are the companies under each of the sectors. 789

4 Banking Pharmaceutical Cement State Bank of India Sun Pharma UltraTechCement Bank of Baroda Dr.Reddy's Labs Shree Cements Punjab National Bank Cipla Ambuja Cements Central Bank Aurobindo Pharm ACC Canara Bank Cadila Health Dalmia Bharat HDFC Bank Divis Labs Ramco Cements ICICI Bank Piramal Enter Prism Cement Axis Bank Torrent Pharma J. K. Cement Kotak Mahindra Bank GlaxoSmithKline JK Lakshmi Cement IndusInd Bank Glenmark Birla Corp b. Data collection for each of the industries and its companies Data related to the market capitalization of each of the firms was retrieved from moneycontrol.com while data related to the shares prices and other economic factors was sourced from the National stock Exchange of India s online historical data section. Under this, the information relating to the security wise price and deliverable position data specific to the Equity shares of the company was retrieved and analyzed. Company wise information for the previous 5 years starting FY, 2011 and ending 31st August 2016 was taken into consideration for the analysis. c. Analysis and Interpretation of data The data was analyzed in two ways A correlation analysis was performed in order to identify the strength of the relationship between the Average prices of the companies i.e Average high price, Average low price, Average open price and Average close price with that of the Earnings per share and the Net profit of the company. The correlation was not only for each company but also for the industry as a whole. A multi-level regression analysis was performed on understanding the extent of the relationship between the average price of the share and the following factors: Dividend paid per share, dividend yield, the book value of the share and the return on equity of the share. This analysis was done only at an industry level and not company level. 5. Analysis and Findings This study draws its data from 3 primary sectors banking, pharma, and cement. 10 firms in each of the sectors were identified based on their market capitalization. Data pertaining to High price, Low price, Opening price and the closing price was collected for the financial years starting 201, on a daily basis. Yearly data comprised of five or six years as available data points were collected for each of the sectors amounting to a total of data points. The mathematical model used in this study is based on the assumption that the Earnings per share (EPS) is a function of price highest, lowest, opening and closing. The same has been assumed for Net profit. This study, however, 790

5 restricts itself to identifying the correlation between the predictor and predicted variables and the study limit itself to inferring about the significance of the correlation and no further. The analysis is done sector wise as well as company wise: Table 5.1: Banking (Overall) Correlation T- Observed (n=60) P Value EPS Profit EPS Profit EPS Profit Averages High price Average Open price Average low price Average Close Price t Critical for n=60, 2Tailed Findings: In a holistic way most banks have shown a positive correlation between profit and the predictor variables. Table 5.2: Pharmaceuticals (Overall) Correlation T- Observed (n=58) P Value EPS Profit EPS Profit EPS Profit Averages High price Average Open price Average low price Average Close Price t Critical for n-2=56,2tailed Findings: Although the correlation recorded between Profit and the impacting variables was negative, it was found to be insignificant ( P>0.05). Table 5.3: Cement industry (Overall - Correlation) Correlation T- Observed (n=58) P Value EPS Profit EPS Profit EPS Profit Averages High price E Average Open price E Average low price E Average Close Price E t Critical for n-2=56, 2Tailed Findings: EPS exhibits a significant correlation with the impacting factors, unlike profit. The data did not have enough evidence to infer affirmatively about the correlation between the predictor variables and the EPS for the banking and the Pharma sectors, while a similar situation was observed for the profit variable in the Cement industry s data. This can also be attributed to the fact that EPS/Profit is not solely affected by the variables considered but is also due to the other impacting variables. Overriding the lack of significance and considering the scope of this study which aims at understanding the association between the prices and EPS/Profit, a regression analysis was conducted to give the following results. 791

6 Figure 5.1: Regression Analysis for Banking Sector - EPS The R 2 value for the regression line is (p-value for the regression coefficient is ) indicating a lack of significance at 5% loss. Figure 5.2: Regression Analysis for Banking Sector - Profit The R 2 value for the regression line is (p-value for the regression coefficient is ) indicating a lack of significance at 5% loss. Figure 5.3: Regression Analysis for Pharma Sector - EPS The R 2 value for the regression line is (p-value for the regression coefficient is 0.499) indicating a lack of significance at 5% loss. Figure 5.4: Regression Analysis for Pharma Sector - Profit 792

7 The R 2 value for the regression line is (p-value for the regression coefficient is ) indicating a lack of significance at 5% loss. Figure 5.5: Regression Analysis for Cement Sector - EPS The R 2 value for the regression line is (p-value for the regression coefficient is E-07) indicating a high significance at 5% loss. Figure 5.6: Regression Analysis for Cement Sector - Profit The R 2 value for the regression line is (p-value for the regression coefficient is 0.387) indicating a lack of significance at 5% loss. The regression analysis reiterates the insignificance of EPS as an impacting factor for the average price of the share. A study on factors affecting share prices indicate other factors such as return on equity, book value per share, dividend per share, dividend yield, price-earnings, firm size (Sharif, Purohit, & Pillai, 2015), broadly classified into micro and macro factors (Islam, Khan, Choudhury, & Adnan, 2014). Factors such as Book value, Earnings per share, Dividend cover, Growth rate and Dividend yield have been studied and identified as those impacting the share prices (Vijayakumar, 2010), (Qureshi, Abdullah, & Imdadullah, 2012). Based on the aforementioned variables the initial model fitted using multiple linear regression for the banking sector is as follows Table 5.4: Regression Analysis - Banking Sector Coefficients Standard Error t Stat P-value Intercept EPS Profit Dividend per share Return on equity Book value per share Dividend yield The goodness of the model shows a value of (p<0.05) with the adjusted 793

8 R 2 = , these indicating that the model is significant with an 87.56% predictability power. Table 5.5: Regression Statistics for the Banking Sector Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 10 ANOVA df SS MS F Significance F Regression Residual Total Ignoring the insignificant variables in the model, namely dividend per share, return on equity and book value of the share, the revised model showed a reduction in the predictability power Table 5.6: Regression Analysis (revised) for Banking Sector Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 10 ANOVA df SS MS F Significance F Regression Residual Total Hence, the best fit for the average price is given by the equation (as in table) It can be inferred that EPS, profit, dividend per share and book value of the share impact average price positively, whereas Return on equity and Dividend Yield Ratio impact negatively. This inference is limited by the fact that it is based on data from 10 firms only and cannot be completely generalized. Based on the aforementioned variables the initial model fitted using multiple linear regression for the Cement sector is as follows: Table 5.7: Regression Analysis for Cement Sector Coefficients Standard Error t Stat P-value Intercept EPS Profit Dividend yield

9 Return on equity Book value per share Dividend per share The goodness of the model shows a value of (p<0.05) with the adjusted R 2 = , these indicating that the model is significant with a 98.26% predictability power. Regression Statistics Table 5.8: Regression Statistics for Cement Sector Multiple R R Square Adjusted R Square Standard Error Observations ANOVA df SS MS F Significance F Regression Residual Total Hence, the best fit for the average price is given by the equation (as in table) It can be inferred that profit, return on equity and dividend yield per share has a negative effect on the average price, while EPS, Book value of the share and dividend per share has a positive impact. This inference is limited by the fact that it is based on data from 10 firms only and cannot be completely generalized. Based on the aforementioned variables the initial model fitted using multiple linear regression for the Pharmaceutical sector is as follows: Table 5.9: Regression Analysis for Pharmaceutical Sector Coefficients Standard Error t Stat P-value Intercept EPS Profit Dividend per share Dividend yield return on equity book value of the share

10 The goodness of the model shows a value of (p<0.05) with the adjusted R 2 = , these indicating that the model is significant with an 89.42% predictability power. Hence, the best fit for the average price is given by the equation. It can be inferred that profit, book value of the share and dividend per share has a negative effect on the average price, while EPS, return on equity and dividend yield per share has a positive impact. This inference is limited by the fact that it is based on data from 10 firms only and cannot be completely generalized. Table 5.10: Regression Statistics for Pharmaceutical Sector Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA df SS MS F Significance F Regression Residual Total Conclusion The information related to the prices of the shares i.e. high price, low price, open price and close price, EPS and Profits was retrieved and a correlation analysis was made. It was found that the data did not have enough evidence to infer affirmatively about the correlation between the predictor variables. It was also found that EPS/Profit is not solely affected by the variables considered but is also due to the other impacting variables. Overriding the significance of the correlation a regression analysis was conducted for the same factors but at an industry level. It was found that in the case of the Pharma industry and the profits there is a negative relationship, also a high significance was found in the case of regression for EPS in the Cement Sector. Broadening the spectrum of the study a regression analysis was conducted between the average price of the shares and the other relating factors such as dividend per share, dividend yield, the book value of the share, return on equity ratio, Eps, and profit. This regression model was applied only at the industry level and not for each company individually. An 87.56% predictability power was found in the analysis for the banking sector, 98.26% predictability power for the cement sector and 89.42% predictability power for the pharmaceutical sector. 796

11 References [1] Boobalan C., Technical analysis in select stocks of indian companies, International Journal of Business And Administration Research Review (2014), [2] Chitra R., Technical Analysis on Selected Stocks of Energy Sector, International Journal of Management & Business Studies (2011), [3] Gordon M.J., Dividends, Earnings and Stock Prices, Review of Economics and Statistics 41(2) (1959), [4] Litzenberger R.H., Ramaswamy K., The Effects of Dividends on Common Stock Prices Tax Effects or Information Effects?, The Journal of Finance (1982), [5] Murphy J.J., Technical analysis of the financial markets: A comprehensive guide to trading methods and application, Prentice Hall Press (1999). [6] Nieh C.C., Lee C.F., Dynamic relationship between stock prices and exchange rates for G-7 countries, The Quarterly Review of Economics and Finance 41 (2001), [7] Pandya H., Technical Analysis for Selected Companies of Indian IT Sector, International Journal of Advanced Research 1 (2013), [8] Saha A.K., Bhuiyan A.R., Predictability of Share Prices through Corporate Annual Reporting: A Focus on the Dhaka Stock Exchange, Journal of Applied Management Accounting Research 12(2) (2014), [9] Turner T., A Beginner s Guide to Day Trading Online, Adams Media, 2nd Edition, [10] Islam M.R., Khan T.R., Choudhury T.T., Adnan A.M., How Earning Per Share (EPS) Affects on Share Price and Firm Value, European Journal of Business and Management 6(17) (2014), [11] Qureshi M.A., Abdullah A., Imdadullah M., Stock Prices Variability around Earnings Announcement Dates at Karachi Stock Exchange, Economics Research International (2012). [12] Sharif T., Purohit H., Pillai R., Analysis of Factors Affecting Share Prices: The Case of Bahrain Stock Exchange. International Journal of Economics and Finance 7(3) (2015), [13] Vijayakumar D.A., Effect of Financial Performance on Share Prices in the Indian Corporate Sector: An Empirical Study, Management and Labour Studies 35 (2010). 797

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