FINANCIAL DETERMINANTS OF EQUITY SHARE PRICES: AN EMPIRICAL ANALYSIS STUDY WITH REFERENCE TO SELECTED COMPANIES LISTED ON BOMBAY STOCK EXCHANGE

FINANCIAL DETERMINANTS OF EQUITY SHARE PRICES: AN EMPIRICAL ANALYSIS STUDY WITH REFERENCE TO SELECTED COMPANIES LISTED ON BOMBAY STOCK EXCHANGE Kiran Challa 25 G. V. Chalam 26 ABSTRACT The stock market plays a crucial role in the economic development of any economy. From both industry and investor point of view, the stock market is imperative. It is necessary to analyze the basic factors of stock market, which might influence the investors' investment decision. The market price of share depends on many factors, such as earnings per share, dividend per share, dividend payout ratio, size of the firm and dividend yield, management, diversification, etc. Against this background, the present study examines the empirical relationship between equity share prices and explanatory variables, such as book value per share, return on net worth, size in terms of sales, etc. and also to analyze the relationship between the equity share price and variables, such as earnings per share, price-earnings ratio, dividend yield, dividend per share, dividend payout during the period of study. The sample companies from the complementary groups of Cement and Steel are selected based on the convenience of the researcher, whose financial data is available continuously for a period of ten years, i.e., from 2003-04 to 2013-14 in Bombay Stock Exchange Directory. It is find out from the analysis that the explanatory variables, like book value and size are the determinants of market price of the equity, while from the select financial variables, only dividend payout ratio and dividend yield are observed to be determining factors. The other variables, like dividend per share, dividend yield, dividend payout ratio and price to earnings ratio are all influencing the market price, while earnings of the company has nothing to do with the market price of the equity share. KEYWORDS Book Value of Share, Market Price of Stock, Explanatory Variables, Earnings Per Share, Return on Net Worth etc. INTRODUCTION The stock market plays a crucial role in the progress of the industry of any economy that in due course affects the country s economic status largely. From both industry and investor point of view, the stock market is imperative. Hence, the stock market is renowned as an indicator of economic growth of a nation. The Bombay Stock Exchange in India being one of the fastest stock exchanges in Asia is a key financial organization and plays a very vibrant role in increasing investment. It is necessary to analyze the basic factors of stock market, which might influence the investor to invest in the equity share prices. In fact, investment in equity share is one of the liquid forms of investment. The market price of the share (MPS) is one of the most important factors, which affects the investment decision of investors. The MPS depends upon many other factors, such as earnings per share, dividend per share, dividend payout ratio, size of the firm and dividend yield, management, diversification, etc. At the first place, the inclusive asset and strength of a company is repeatedly enumerated by its price of shares. It is also put forward from the theories that there are many factors on which the market price of the share depends, for instance earning per share, dividend per share, price-earnings ratio, dividend payout ratio, size of the firm, dividend yield, etc. Therefore, comprehending the influence of several fundamental variables on share price plays a vital role in taking decisions for parties like investors, government, etc. As far as stocks are concerned, the most recent price that the particular stock has been traded is known as the market price of a stock. But no assurance is given that an investor will obtain similar price upon procurement of the stock subsequently. However, the book value has been the original prices of the assets as of the books; it does not show the current market price of the company's assets. Accordingly, while estimating the market price of the shares, this ratio has a limited use. However, the minimum price of the company's shares can be understood for the estimation. It will also help in judging whether the share price is overpriced or underpriced. What is meaningful to investors is the benefits on the valuation of impending income spawning prospects. Once the investment is done on the equity share, future income can be in two ways, viz., dividend income and appreciation of market price of the share. Hence, the factors that can influence the forthcoming prospects can be characterized as value pavilions of the share. Thus, 25 Research Scholar (FT), Department of Commerce & Business Administration, Acharya Nagarjuna University, Andhra Pradesh, India, saykiran@gmail.com 26 Professor, Department of Commerce & Business Administration, Acharya Nagarjuna Univers ity, Andhra Pradesh, India, chalam.goriparthi@gmail.com 1761 P a g e

It is always better to have higher cash reserves in the balance sheet, Increase in book value per share as carried out in balance sheet, Increase in the sales volume of the company, Increase in earnings per share and net income, Subsiding number of shares outstanding in the market (share buyback), Affirmative net cash flow of the company, Standard dividend policy, dividend payout, type of dividend, etc. OBJECTIVES OF STUDY Against this backdrop, the present study has undertaken the following specific objectives: To examine the empirical relationship between equity share prices and explanatory variables, such as book value per share, return on net worth, size in terms of sales, etc. To analyze the relationship between the equity share price and variables, such as earnings per share, price-earnings ratio, dividend yield, dividend per share, dividend payout during the period of study. HYPOTHESES OF STUDY For this study, eight hypotheses have been formulated based on the relationship between the seven accounting ratios and market price of share applied Pearson s correlation and multiple regression models to measure the strength of these relationships: H 1 : There is a positive relationship between BV and MPS H 2 : There is a positive relationship between size and MPS H 3 : There is a positive relationship between RONW and MPS H 4 : There is a positive relationship between DPS and MPS H 5 : There is a positive relationship between EPS and MPS H 6 : There is a positive relationship between DY and MPS H 7 : There is a positive relationship between DP and MPS H 8 : There is a positive relationship between P/E and MPS Selection of Financial Variables The present study identified seven financial ratios to test the impact of these ratios on Market Price of Share (MPS) of the select listed companies. The financial ratios include the Book Value per Share (BV), Dividend per Share (DPS), Dividend Payout Ratio (DP), Dividend Yield (DY), Earnings per Share (EPS), Price to Earnings ratio and Return on Net worth (RONW). In general, Dividend per share has emerged as a significant determinant of share price; yield also emerged as highly significant determinant with its negative association with market price of share. The coefficient of book value was positive and highly significant with market price of share. The influence of price-earning multiplier on share prices is more. Hence, this study tries to examine the financial factors responsible for affecting the market price of equity shares in select companies. Research Design Based on the above-mentioned objectives of the study, a descriptive research has been adopted. Further, the non-probability sampling technique is used for the analysis of the study. The sample companies are selected based on the convenience of the researcher, whose data is available continuously for a period of ten years. The Steel and Cement are complementary industries, which are major contributors of construction industry in any economy. The companies of respective industries are selected for an empirical analysis of the present study, which are listed in Bombay Stock Exchange. Selection of Sample While selecting a sample of ten companies, five each from Cement and Steel industrial groups, a company has been regarded as eligible for selecting as a sample, if it satisfies the following conditions: It is listed in Bombay Stock Exchange, the necessary financial data required for calculating the measures of dependent and independent variables pertaining to all the years 2003-04 to 2012-13 is available and thus only those companies, whose price data is continuously available. The following are the companies selected for the empirical analysis in the present study: Grasim Industries Limited. OCL India Limited. Prism Limited. RAMCO Cements Limited. 1762 P a g e

Sagar Cements Limited. FACOR Steel Limited. JSW Steel Limited. RATHI Steel and Power Limited. Steel Authority of India Limited. Tata Steel Limited. ANALYSIS OF DATA & INTERPRETATION Table-1: ANOVA, Model Summary & Regression Analysis for Each Variable (BV, SIZE, and RONW) with dependent variable (MPS) of Cement Industry Model Un-standardized Coefficients Standardized Coefficients t Sig. Constant -936.738 392.097-2.389.021 Book Value(BV) 1.890.287.677 6.590.000 Size of the Firm(SIZE) 150.962 64.288.238 2.348.023 Return on Net Worth (RONW) 3.404 3.960.061.860.394 R Square Value.789 F Value 57.263 F- Sig.000 b. Predictors: (Constant), Return on Net Worth (RONW), Book Value (BV), Size of the Firm (SIZE). Table-1 shows the coefficient of multiple determinations R Square that explains the degree to which the independent variables affect the dependent variable. In this case, 0.789 or 78.9 per cent of the variation in the dependent variable are explained by the independent variables, while 0.211 or 12.1 per cent were affected by other variables outside the independent variables. The adjusted R-square, a more conservative way of looking at the coefficient of determination is also more than 75 per cent. This indicates that Book value, size and RONW are the major determining factors of MPS. The analysis of variance (ANOVA) is used in testing the hypotheses and to measure the differences and similarities between the variables according to their different characteristics. The results from the Fisher's ratio (i.e. the F-Statistics, which is a proof of the validity of the estimated model) as reflected in table-1, indicates that the F is about 57.263 and a p-value that is less than to 0.05 (P-value =0.000), this consistently suggests clearly that the explanatory variables are significantly associated with the dependent variable. It explains, they strongly determine the behavior of the market value of the share prices. The data on regression analysis shows that the book value has a strong and positive relationship with market price. The t calculated of book value 6.590 with significance value 0.00 is statistically significant. The beta value represents that for one per cent increase in book value, the market price of the companies will increase by about 0.67 percent, which is significant. Hence, H 1 is accepted since the BV has a significant relationship with MPS. In addition, the return on net worth has a positive relationship with MPS. The 't' calculated of RONW is observed as 0.86 with p-value > 0.05, which is statistically insignificant. Hence, we reject H 2. On the other hand, the size has a positive relationship with MPS with P<0.05, which is statistically significant. Hence, an increase in terms of size invariably brings about an increase in market price. The empirical findings from the regression analysis also shows that this outcome principally implies that an increase in book value and size will invariably bring about a significant increase in MPS, while RONW shows no impact. Therefore, the BV and Size has significant relationship with MPS during the period of study, stating that the select variables have explanatory power towards stock price movement. Thus, the weight of the evidence suggests that we accept both H 1 and H2 and reject the H3. Table-2: Correlation Co-efficient for each variable (BV, SIZE, and RONW) with dependent variable (MPS) of Cement industry Variables Market Price of the share Book Value Return on Net Worth Size Market Price of the Share 1.871 **.308 *.756 ** Book Value.871 ** 1.282 *.744 ** Return on Net Worth.308 *.282 * 1.235 Size.756 **.744 **.235 1 1763 P a g e

The Pearson co-efficient of correlation is used to find out the relationship between market price of share and book value, return on net worth, size of the firm at 1 per cent and 5 per cent level of significance. The result presented in table-2 shows that there is a positive correlation between MPS and the independent variables BV (correlation coefficient = 0.871), RONW (correlation coefficient 0.308), SIZE (correlation coefficient 0.756). The BV and SIZE is showing a significant relationship with dependent variable MPS at 1 percent probability level, whereas, the RONW is showing a significant relationship with dependent variable MPS at 5 per cent probability level. It indicates that the independent variables, viz., BV, RONW and SIZE are showing an impact on dependent variable MPS. Table-3: ANOVA, Model Summary & Co-efficient for each variable (DPS, EPS, DY, DP, P/E) with dependent variable (MPS) of Cement industry Model Un-standardized Coefficients Std. Coefficients T Sig. Constant 195.505 88.511 2.209.032 Dividend Price Share (DPS) 83.547 12.442.885 6.715.000 Earnings Per Share (EPS).707 1.779.054.398.693 Dividend Yield (DY) -158.397 59.345 -.174-2.669.011 Dividend Paid (DP).515 4.243.012.121.904 Price to Earnings Ratio (P/E) 1.546 2.915.052.530.599 R Square Value 0.915 F Value 94.674 F- Sig 0.000 b. Predictors: (Constant), Price to Earnings Ratio (P/E), Dividend Price Share (DPS), Dividend Yield (DY), Dividend Paid (DP), Earnings per Share (EPS). The R-square of the regression model is the fraction of the variation in the dependent variable that is predicted by the independent variables. It can be observed from table-3 that the R has a value of.894, which shows a simple correlation between market price of the share and explanatory variables. The R-square, which is also a measure of the overall fitness of the model, indicates that the model can explain about 91.5 percent of the variability between independent and dependent variables. This model does not capture the remaining 9.5 percent of variation, because there might be many other factors that can explain this variation. The next part of the output report is analysis of variance (ANOVA). The data also shows that the various sums of squares and the degre e of freedom associated with each. From these two values, the average sums of squares (mean squares) can be calculated by dividing the sums of squares by the associated degrees of freedom. The most important part of data in the above table is the F-ratio, which reveals the associated significance value. For the data, F is 94.674 with the p value less than 0.05, which is significant (P-value =.000), this unvaryingly suggests that the select explanatory factors are significantly associated with the dependent variable. It implies that they are strongly determining the behavior of the market values of share prices. However, further empirical findings provided in the above table shows that there is a significant relationship between the DPS, DY and the MPS of select companies in Cement industry during the period of study. This is evident in the t-statistics value (DPS) 6.715 with a P-Value 0.00, which is statistically significant at 5 per cent level. That means, a change in DPS will have an impact on the market price of the share positively. Simultaneously, dividend yield with t- statistic -2.669 shows that there is a sturdy and negative relationship with MPS. The t-calculated value of DY, and P-value.011 is also statistically significant at 5 per cent level of significance. This significant negative relationship shows that the yield could significantly affect the market price of the share of Cement industry. In addition, the EPS, DP and P/E are statistically insignificant with P-value greater than 0.05, which means that these independent factors are not having a significant relationship with the market price of the share. Hence, we accept H 4 and H 6 and reject H 5, H 7 and H 8. Table -4: Correlation Co-efficient for each variable (DPS, DP, DY, EPS, P/E) with dependent variable (MPS) of Cement Industry Variables MPS DPS EPS DY DP P/E MPS 1.937 **.883 ** -.239 -.150 -.021 DPS.937 ** 1.937 ** -.058 -.162 -.129 EPS.883 **.937 ** 1 -.085 -.279 * -.212 DY -.239 -.058 -.085 1.253 -.251 DP -.150 -.162 -.279 *.253 1.766 ** P/E -.021 -.129 -.212 -.251.766 ** 1 1764 P a g e

The Pearson co-efficient of correlation is used to find out the relationship between MPS and DPS, EPS, DY, DP and P/E at 1 per cent and 5 per cent level of confidence. The results of analysis shows that there is a positive correlation between MPS and the independent variables DPS (correlation coefficient = 0.937), EPS (correlation coefficient = 0.833) and shows a negative correlation between MPS and the independent variables DY (correlation coefficient = -0.239), P/E (correlation coefficient = -.021) and DP (correlation coefficient = -1.50). The DPS and EPS are having a significant relationship with dependent variable MPS at 5 per cent probability level, whereas, the DP, DY and P/E are having an insignificant relationship with dependent variable MPS. It indicates that out of the five variables, only two independent variables DPS and EPS are showing an impact on dependent variable MPS. Table-5: ANOVA, Model Summary & Regression for each variable (BV, SIZE, and RONW) with dependent Variable (MPS) of Steel Industry Model Un-standardized Coefficients Standardized Coefficients T Sig. Constant -20.571 59.088 -.348.729 Book Value(BV) 1.890.287.677 6.590.000 Size of the Firm(SIZE) 8.193 9.164.107.894.376 Return on Net Worth (RONW) Return on net worth 2.710 1.367.207 1.982 R Square Value 0.579 F Value 21.128 F- Sig.000 b. Predictors: (Constant), Return on Net Worth (RONW), Book Value (BV), Size of the Firm (SIZE) The R square of.579 indicates 57.9 per cent of the variations in the dependent variable, which are expounded by the independent variables, while 0.428 or 42.8 per cent were affected by other variables outside the independent variables. The adjusted R-square, a more conventional method of observing at the coefficient of determination is less than 60 per cent. In this case, 55.2 per cent of the variations in the dependent variable is not explained by the independent variables. Hence, this indicates that independent variables are the some of the determining factors of MPS. Outcomes from the Fisher's ratio as shown in table-5 indicates that the F is about 21.128 and a p-value that is less than 0.05 (P-value = 0.00), reliably recommends that the explanatory factors which are independent variables associated with the dependent variable has relevance. It can be said that they determine the behavior of the market values of the share prices in the Steel industry during the period of study. It can also be observed from the data in table-5 that only the book value has a significant relationship with MPS. The t-calculated of book value (6.590) indicates that the BV has a strong and positive relationship with MPS with its significance level of 0.00, which is statistically significant. Thus, the weight of the evidence shows that we accept H 1. It can also be observed from the data in table-5 that both size and return on net worth has a positive relationship with MPS. The t calculated of size and RONW shows a positive relationship with the MPS. However, the significance level of 0.376 and 1.982 of size and RONW respectively are statistically insignificant, which indicates that the H2 and H3 are rejected. Therefore, in case of Steel industry, size and RONW are not playing any vital role towards the changes in market price of equity stocks. Table-6: Correlation Co-efficient for each variable (BV, SIZE, and RONW) with dependent variable (MPS) of Steel industry Variables Market Price of the share Book Value Return on Net Worth Size Market Price of the share 1.716 **.256.503 ** Book Value.716 ** 1.017.488 ** Return on Net Worth.256.017 1.358 * Size.503 **.488 **.358 * 1 The correlation matrix in table-6 shows the relationship between market price of share and book value, return on net worth, size of the firm at 1 per cent and 5 per cent level of confidence. The data indicates that there is a positive correlation between MPS and the independent variables BV (correlation coefficient=0.716), RONW (correlation coefficient 0.256), SIZE (correlation coefficient.503). The BV and SIZE are showing significant relationship with dependent variable MPS at 5 per cent probability level, while RONW is not showing significant relationship with dependent variable MPS. It indicates that the independent variables BV and SIZE are showing an impact on dependent variable MPS. 1765 P a g e

Table -7: ANOVA, Model Summary & Co-efficient for each variable (DPS, EPS, DY, DP, P/E) with dependent Variable (MPS) of Steel industry Model Un-standardized Coefficients Standardized Coefficients T Sig. Constant 85.969 22.165 3.879.000 Dividend Price Share (DPS) 30.061 10.417.770 2.886.006 Earnings Per Share (EPS) 1.384 2.144.172.645.522 Dividend Yield (DY) -17.174 8.120 -.156-2.115.040 Dividend Paid (DP) -1.809.868 -.289-2.083.043 Price to Earnings Ratio (P/E) 2.140.678.453 3.155.003 R Square Value 0.815 F Value 50.419 F- Sig 0.000 b. Predictors: (Constant), Price to Earnings Ratio (P/E), Dividend Price Share (DPS), Dividend Yield (DY), Dividend Paid (DP), Earnings per Share (EPS). From the data in table-7, it is found that the R-Square, which is the coefficient of determination of the variables is 0.815. The R- square, which is also a measure of the overall fitness of the model indicates that this is capable of explaining about 81.5 per cent of the variability of the share prices of the selected companies. On the other hand, it is about 18.5 per cent of the variation in MPS of the select companies are effected by other factors not captured by this model. This result is complimented by the adjusted R- square of 83.5 per cent, which is the essence of the proportion of total variance that is explained by the model. Similarly, findings from the Fisher's ratio indicates that the F is about 50.419 and a p-value that is less than to 0.05 (P-value =0.000), which clearly suggests that simultaneously the explanatory variables are significantly associated with the dependent variable. That shows, they strongly determine the behavior of the market values of share prices. The empirical data shown in table-7 also explains that there is a significant positive relationship between DPS and MPS of the listed select companies in Bombay Stock Exchange. This is evident in the t-statistics value of 2.886 with a P-value of 0.006, which is significant at 5 per cent level of significance. This outcome basically implies that with all other variables held constant, an increase or a change in DPS of companies, say 1 per cent, will on the average bring about a 77 per cent increase in the MPS. That is an increase in the DPS of selected companies will also lead to a positive improvement in the MPS. From this, it is ev ident that the DPS of the selected companies have a significant positive impact on the MPS. Hence, we accept the H 4. Moreover, an insignificant positive relationship between EPS and MPS can be observed which is evident from the t-statistics value 0.645 and the P-Value 0.522. This implies that an increase in EPS will invariably bring about no significant increase in the MPS. Hence, we reject H 5. Another empirical finding from the regression analysis shows a negative relationship between DY and MPS. This is evident from the t-statistics value of -2.115 and the P-Value is 0.04. This result explains that with the influence of other variables held constant, as DY changes, say 1 per cent on an average, MPS changes by -15.6 per cent in the opposite direction. This result further indicates that the DY is a significant determinant of MPS for the sample listed companies in BSE. Hence, we accept H 6. Finally, other variables, like DP and P/E also have a significant impact on the MPS with P-values of 0.043 and 0.003 respectively. Hence, we accept H 7 and H 8. However, this indicates that the firms' earnings have no explanatory power towards stock price movement during the period of study. Table-8: Correlation Co-efficient for each variable (DPS, DP, DY, EPS, P/E) with dependent variable (MPS) of Steel Industry Variables MPS DPS EPS DY DP P/E MPS 1.838 **.852 **.044 -.095.107 DPS.838 ** 1.973 ** -.120.188 -.197 EPS.838 ** 1.973 ** -.120.188 -.197 DY -.095.188.216.013 1 -.298 * DP.044 -.120 -.044 1.013.860 ** P/E.107 -.197 -.143.860 ** -.298 * 1 1766 P a g e

The correlation matrix in table-8 shows the relationship between MPS and DPS, EPS, DY, DP, P/E at 1 per cent and 5 per cent level of confidence. The Pearson s correlation analysis explains that there is a positive correlation between MPS and the independent variables DPS (correlation coefficient = 0.838), EPS (correlation coefficient = 0.838), DP (correlation coefficient = 0.044), P/E (correlation coefficient =.017) and found a negative correlation between MPS and the independent variables DY (correlation coefficient = -0.095) and MPS. The DPS and EPS are having a significant relationship with dependent variable MPS at 5 per cent probability level, whereas DP, DY and P/E are having an insignificant relationship with dependent variable MPS. It indicates that out of the five variables only two independent variables, i.e., DPS, EPS and DY are showing an impact on dependent variable MPS. Table-9: ANOVA, Model Summary & Regression for each variable (BV, SIZE, and RONW) with dependent variable (MPS) of Cement and Steel Industry Model Un-standardized Coefficients Std. Coefficients T Sig. Constant -12.005 121.274 -.099.921 Book Value(BV) 2.289.141.870 16.229.000 Size of the Firm(SIZE) -4.809 18.292 -.015 -.263.793 Return on Net Worth (RONW) 3.296 2.294.075 1.437.154 R Square Value.764 F Value 103.796 F- Sig.000 b. Predictors: (Constant), Return on Net Worth (RONW), Book Value (BV), Size of the Firm (SIZE). Table-9 shows the R-square as 0.764 or 76.4 per cent of the variation in the dependent variable are explained by the independent variable, while the rest were affected by the other variables. The adjusted R-square, a more conservative way of looking at the coefficient of determination is also more than 75 per cent. This indicates that the book value, size and RONW are the major determining factors of MPS. Findings from the Fisher's ratio as shown in table indicates that the 'F' is about 103.796 and a P-value that is less than 0.05 (P-value =0.00), which reliably advocates that the explanatory variables are significantly associated with the dependent variable. It implies that they strongly determine the behavior of the market values of the share prices. The regression analysis in table-9 shows that the book value has a strong and positive relationship with market price. The 't' calculated book value is 16.229 with P-value 0.00 is statistically significant. The beta value represents that for 1 per cent increase in book value, the market price of the companies will increase by.87 per cent, which is significant. Hence, H1 is accepted because BV has a significant relationship with MPS. In addition, the return on net worth has a positive relationship with MPS. The 't' calculated of RONW is 1.437 with P-value > 0.05, which is statistically insignificant. Hence, we reject H 2. On the other hand, the size has a negative relationship with MPS and P>0.05, which is statistically insignificant concluding that we reject H 3. It can be concluded from the results of regression analysis that the increase in book value will invariably bring a significant increase in MPS, whereas RONW and Size shows no impact. Therefore, the weight of the evidence during the period of study suggests that we accept H 1 and reject H2, H3. Table-10: Correlation Co-efficient for each variable (BV, SIZE, and RONW) with dependent variable (MPS) of Cement and Steel Industry Variables Market Price of the Book Value Return on Net Size share Worth Market Price of the share 1.871 **.147.341 ** Book Value.871 ** 1.088.381 ** Return on Net Worth.147.088 1.318 ** Size.341 **.381 **.318 ** 1 The correlation matrix in table-10 shows the relationship between market price of share and book value, return on net worth, size of the firm at 1 per cent and 5 per cent level of confidence. The data explains that there is a positive correlation between MPS and the independent variables BV (correlation coefficient = 0.871), RONW (correlation coefficient 0.147), SIZE (correlation coefficient.341). The BV and SIZE are showing a significant relationship with dependent variable MPS at 5 per cent probability level, whereas, RONW is not showing significant relationship with dependent variable MPS. It indicates that the independent variables BV and SIZE are showing an impact on dependent variable MPS. 1767 P a g e

Table-11: ANOVA, Model Summary & Co-efficient for each variable (DPS, EPS, DY, DP, P/E) with dependent Variable (MPS) of Cement and Steel industry Model Un-Standardized Coefficients Standardized Coefficients T Sig. Constant 110.258 51.425 2.144.035 Dividend Price Share (DPS) 52.606 10.122.590 5.197.000 Earnings Per Share (EPS) 4.290 1.511.327 2.839.006 Dividend Yield (DY) -67.174 21.942 -.151-3.061.003 Dividend Paid (DP) -2.237 2.155 -.088-1.038.302 Price to Earnings Ratio (P/E) 3.084 1.644.164 1.876.064 R Square Value 0.863 F Value 117.938 F- Sig 0.000 b. Predictors: (Constant), Price to Earnings Ratio (P/E), Dividend Price Share (DPS), Dividend Yield (DY), Dividend Paid (DP), Earnings per Share (EPS) In Table-11, the R-square value is observed as 0.863 or 86.3 percent indicating the variations in the dependent variable that are explained by the independent variables, while the rest were affected by other variables outside the independent var iables. The adjusted R-square, a more conservative way of looking at the coefficient of determination is also more than 80 percent. Hence, it indicates that the independent variables are the major determining factors of MPS. The observations from the analysis of variance in table-11 states that the F value is 117.938 and the P-value stands at 0.00, which is less than 5 per cent level of significance shows the goodness of the model. Hence, all the independent variables selected for the study are determining factors of the market price of the share in the select companies during the period of study. On the other hand, further empirical results shows that there is a significant relationship between DPS and MPS of the select companies. It is also evident in 't' statistics value of 5.197 with a P-value of 0.000 is significant at 5 per cent level. This implies that with all other variables held constant, an increase or a change in DPS, say 1 per cent will bring 59.1 percent in the MPS. That is an increase in DPS will lead to a positive improvement in the MPS. From this, it is evident that the DPS of selected companies have a significant positive impact on the MPS. Hence, we acc ept the H 4. Further, data also shows a significant relationship between EPS and MPS, which is evident in t-statistics value of 5.137 and P-value 0.006. This implies that an increase in EPS will invariably bring about a significant increase in MPS, say 1 percent on the average will bring 32.7 percent increase in MPS. Hence, we accept H 5. Another empirical result from the regression analysis shows a negative relationship between DY and MPS. This is evident in 't'-statistic value of -3.061 and the P-value 0.003. This result explains that as DY changes, say 1 per cent on an average, MPS changes by -1.51 percent in the opposite direction. This result further indicates that DY is a significant determinant of MPS for the listed sample companies in BSE. Hence, we accept H 6. Finally, other variables like DP and P/E have an insignificant impact on MPS. However, this indicates that the DP and P/E have no explanatory power toward stock price movement of select firms during the period of study period. Hence, we reject H 7 and H 8. Table-12: Correlation Co-efficient Analysis for each variable (DPS, DP, DY, EPS, P/E) with dependent variable (MPS) of Cement and Steel Industry Variables MPS DPS EPS DY DP P/E MPS 1.891 **.884 ** -.249 * -.089.017 DPS.891 ** 1.938 ** -.028 -.107 -.110 EPS.884 **.938 ** 1 -.087 -.186 -.153 DY -.249 * -.028 -.087 1.088 -.274 ** DP -.089 -.107 -.186.088 1.828 ** P/E.017 -.110 -.153 -.274 **.828 ** 1 The co-efficient of correlation in table-12 explains the relationship between DPS, EPS, DY, DP, P/E and MPS at 1 per cent and 5 per cent level of significance. The result shows that there is a positive correlation between MPS and the independent variables DPS (correlation coefficient = 0.891), EPS (correlation coefficient = 0.884), P/E (correlation coefficient = 0.017) and found a negative correlation between MPS and the independent variables DY (correlation coefficient = -0.249), DP (correlation coefficient = -0.089). The DPS, EPS and DY are having a significant relationship with dependent variable MPS at 5 per cent probability level, whereas DP and P/E is having an insignificant relationship with dependent variable MPS. It can be concluded from the foregoing analysis that out of the five variables, only three independent variables DPS, EPS and DY are showing an impact on dependent variable MPS. 1768 P a g e

FINDINGS AND CONCLUSIONS In case of the select companies of Cement industry, the explanatory variables like book value and size are the determining factors of market price of the equity share, while from the select variables, only dividend payout ratio and dividend yield are observed to be determining factors. On the other hand, in case of select companies of Steel industry, only book value emerged as a determining factor of the market price. The other variables, like dividend per share, dividend yield, dividend payout ratio and price to earnings ratio are all influencing the market price, while earnings of the company has nothing to do with the market price of the equity share. Therefore, for both the Cement and Steel industries together, the book value seems to be a determinant factor of market price of the share. On the other hand, the DPS, EPS, DY are observed to be influential factors of market price of the share. SUGGESTIONS As market price of the share is significantly associated with the book value of both Cement and Steel industries, it is suggested to the investors to make a note of BV while taking investment decisions. A steady dividend policy with a consistent book value would help a company to be in a lucrative path as dividend per share and yield are also playing an important role. Hence, the present study concludes that the analysis on the financial determinants of equity share prove to be helpful to the investors before they take investment decisions. As the selected explanatory variables possess strong influence towards the stock price movements, it is useful for both investors and the company to predict while investing in shares. REFERENCES 1. AL-Shubiri, F. N. (2011). Analyze the Determinants of Market Stock Price Movements: An Empirical Study of Jordanian Commercial Banks. International Journal of Business and Management, 5(10), 137-147. 2. Balkrishan. (1984). Determinants of Equity Prices in India. Management Accountant, 19(12), 728-730. 3. Bhatt, P., & Sumangala, J. K. (2012). Impact of Earnings per share on Market Value of an Equity share: An Empirical study in Indian Capital Market. Journal of Finance, Accounting and Management, 3(2), 1-14. 4. Bodnar, G. M., & Gentry, W. M. (1993). Exchange Rate Exposure and Industry Characteristics: Evidence from Canada, Japan, and the USA. Journal of International Money and Finance, 12, 29-45. 5. Chang, H. L., Chen, Y. S., Su, C. W., & Chang, Y. W. (2008). The relationship between Stock Price and EPS: Evidence based on Taiwan panel data. Economic Bulletin, 3(30), 1-12. 6. Jorion, P. (1991). The Pricing of Exchange Rate Risk in the Stock Market. Journal of Financial and Quantitative Analysis, 26, 363-376. 7. Malkar, B., & Gupta, R. (2002, December). Determinants of Share Price - A System Approach: The Modified Model. Finance India, 16(4), 1409-1418. 8. Malhotra, M., & Prakash, N (2001). Determinants of Market Price of A-Group and B-Group Shares. The ICFAI Journal of Applied Finance, 7(3), 45-53. 9. Mehta, S. K., & Turan, M. S. (2005). Determinants of Stock Prices in India: An Empirical Study. The Journal of Indian Management and Strategy, 10(4), 37-43. 10. Midani, A. (1991). Determinants of Kuwaiti Stock Prices: An Empirical Investigation of Industrial Services and Food Company Shares. Retrieved April 9, 2012. 11. Nirmala, P. S., Sanju, P. S., & Ramachandran, M. ( 2011). Determinants of Share Prices in India. Journal of Emerging Trends in Economics and Management Sciences, 2(2), 124-130. 12. Nisa, M. U., & Nishat, M. (2012). The Determinants of Stock Prices in Pakistan. Asian Economics and Financial Review, 1(4), 276-291. 13. Taulbee, N. (2005). Influences on the Stock Market: An Examination of the Effect of Economic Variables on the S&P 500. The Park Place Economist, IX, 91-100. 14. Retrieved from http://www.bwgriffin.com/gsu/courses/edur8132/notes/notes8h_regressionstandardizedcoefficients.pdf 15. Retrieved from http://www.proprofs.com/quiz-school/story.php?title=operations-final-exam-prep ***** 1769 P a g e