INDIAN JOURNAL OF FINANCE AND BANKING 13(2) (2023), 14-22 14 FINANCE AND BANKING IJFB VOL 13 NO 2 (2023) P-ISSN 2574-6081 E-ISSN 2574-609X Available online at https://www.cribfb.com Journal homepage: https://www.cribfb.com/journal/index.php/ijfb Published by CRIBFB, USA EQUITY PRICE DETERMINANTS OF INDIA'S NIFTY NEXT 50 INDEX FIRMS' Amit Hedau (a)1 Sarbesh Mishra (b) (a) Assistant Professor, School of Business Management, National Institute of Construction, Management and Research, Hyderabad, India; E-mail: amithedau21@gmail.com (b) Professor & Dean, National Institute of Construction, Management and Research, Hyderabad, India; E-mail: sarbeshmishra@nicmar.ac.in A R T I C L E I N F O Article History: Received: 1st October 2023 Revised: 2nd December 2023 Accepted: 15th December 2023 Published: 17th December 2023 Keywords: Equity Price, Determinants, Regression, EVA, India JEL Classification Codes: C53, C55, G12, G17, O16 A B S T R A C T An investor must perform research on a stock before investing in it. It becomes critical for a financially savvy investor. As a result, stock prices have long been a source of attraction. Researchers have worked hard to identify the elements influencing stock prices and returns. This paper is an attempt to identify the factors predicting the market price of Equity in India. The secondary data about 2017 to 2022 of NIFTY's Next 50 index companies is analyzed using OLS regression. The findings of the regression are ratified through a qualitative approach by the semi-structured open-ended survey and interviewing experts. The obtained responses are transcribed and coded. Matrix coding has been performed using a qualitative tool such as NVivo to understand the pattern of codes. The study finds that dividend rate, book value, and return on net worth are statistically significant and positively influence the market price of sample firms. Debt to equity ratio has a negative impact on market price. Economic value added (EVA) was found to be a new variable that significantly impacted the market price of shares. The study findings are helpful to the market participants to make wise and knowledge-based investment decisions. The study's findings will also add to the existing body of knowledge regarding stock valuation. © 2023 by the authors. Licensee CRIBFB, USA. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). INTRODUCTION An informed investment decision considers an asset's present value and the factors that will affect its future worth. The foundation of wise investment is the idea that an investor should never overpay for an asset. Due to the wide range of variables that affect their pricing, the valuation of financial assets like Equity and bonds is more difficult because their value resides outside the eyes of the investor. One group of investors thinks valuing Equity is a precise science with no room for human mistakes. The other portion contends that since analysts must rely on convenient assumptions to support their findings, equities valuation is more of an art than a science. Finding the variables that affect an equity's market price is essential to its valuation. These variables could include things like market sentiment, firm and industry-specific circumstances, financial and economic variables, and investor behavior. In order to find the elements that explain the market price of Equity, researchers and academics employ statistical approaches to the financial data of businesses. However, more study is necessary before additional literature on the subject may be added. Second, using previously untried data analysis approaches or discovering novel explanatory variables may be possible by conducting another survey. The present study uses internal financial characteristics to forecast the market price of Equity in line with the earlier findings. Multiple regression is used to test for cause and effect. The NIFTY Next 50 (NN50) index companies are examined using historical financial data from January 2017 to December 2022. According to the findings, the dividend rate (DR), return on net worth (RONW), book value (BV), and economic value added (EVA) are statistically significant and positively influence the market price of the sample companies. In contrast, the debt-to-equity ratio slightly negatively impacts the market price of Equity, and earnings per share (EPS) are found to be statistically insignificant. The statistical results of data analysis were typically reported as the study's conclusion in earlier research. The current paper validates the factors using a qualitative technique that includes expert interviews and a semi-structured open- ended survey. The received responses are coded and transcribed. A qualitative tool like NVivo has been used to execute 1Corresponding Author: ORCID ID: 0000-0002-5571-601X © 2023 by the authors. Hosting by CRIBFB. Peer review under responsibility of CRIBFB, USA. https://doi.org/10.46281/ijfb.v13i2.2148 To cite this article: Hedau, A., & Mishra, S. (2023). EQUITY PRICE DETERMINANTS OF INDIA’S NIFTY NEXT 50 INDEX FIRMS’. Indian Journal of Finance and Banking, 13(2), 14-22. https://doi.org/10.46281/ijfb.v13i2.2148 https://orcid.org/0000-0002-5571-601X http://creativecommons.org/licenses/by/4.0/) http://creativecommons.org/licenses/by/4.0/) https://doi.org/10.46281/ijfb.v13i2.2148 https://orcid.org/0000-0003-2969-7756 Hedau & Mishra, Indian Journal of Finance and Banking 13(2) (2023), 14-22 15 matrix coding to comprehend the pattern of codes. Market participants, including fund managers, equities analysts, portfolio managers, retail investors, and high-net- worth people, can benefit from the study's conclusions. Investors are wary about the market's volatility and the security of their investments as the broad Indian market indices NIFTY and Sensex linger close to all-time high levels; equity market investors may find the study's findings helpful in making sane investment decisions. The study's findings will also add to the existing body of knowledge regarding stock valuation. A comprehensive literature review identified the research gap in conducting the present research work. The research methodology is selected based on the earlier empirical work. The statistical data analysis is presented in the findings section, followed by a discussion of the present findings and their implications. Five sections make up the remainder of the paper. A survey of the literature is covered in Section Two to determine the research gap. The research methodology is covered in Section 3, and the results of the data analysis are covered in Section 4. The study's results and their consequences are discussed in section five. Conclusions, limitations, and the future scope of the research are discussed in Section 6. LITERATURE REVIEW A theory suggests a wide range of valuation models that can be classified into three categories. The first category includes discounted cash flow valuation, where the expected future cash flows' present value is estimated using the required rate of return, popularly known as the discount rate. In the second category, a relative valuation is done based on sales, cash flow, book value, or earnings of comparable assets. The third category talks about contingent claim valuation, where option pricing is used to determine the present value of the underlying asset. However, these three models are based on a few assumptions. Like in discounted cash flow models, the analyst disagrees with the discount rate. The discount rate differs due to the risk appetite of every investor; therefore, a universally accepted discount rate is difficult to find. Predicting future cash inflows regarding dividends is another challenge when using discounted cash flow models for equity valuation. In the comparable approach model, the non-availability of equal assets is the limiting factor. Sometimes, similar variables are influenced by temporary market conditions or non-fundamental factors. The option pricing model, the third type of valuation model, is based on a set of assumptions, and the outcome of these valuation models is subject to the input information used by the user. Further, this model is less if the underlying asset is noted in the derivative segment. The study conducted by Trejo Pech, White, and Noguera (2015); Choiriyah et al. (2020), Budi and Davianti (2022), and Saputra (2022) observed enough evidence to suggest that financial variables have critical roles in predicting the market price of Equity. The study conducted by Collins (1957) is one of the pioneering studies in the field of determinants of equity prices. The study was conducted on US market data to scientifically deal with the problem of mix determinants and to depict a clear answer to determining whether stocks at a given point in time are too high or too low. The study found that dividends, net profit, operating earnings, and book value were the prominent factors affecting share prices in the US. Zahir and Khanna (1982) is the first study to predict the equity price in India using multiple regression based on data from two years, i.e., 1976–1978. The dividend per share, book value, and yield were found to be significant determinants of the share price, whereas the influence of earnings per share was weak. Zahir and Khanna (1982) analyzed 101 companies as one set, whereas Balkrishnan (1984) conducted a sector-specific study on India's engineering and cotton textile sectors. He observed dividend per share and book value as significant determinants in both industries but yield as a substantial factor in the cotton textile sector only. Srivastava (1984) observed the dividend rate as substantial in his study of 327 Indian companies. Nirmala et al. (2011) studied the auto, healthcare, and PSU sectors of the Indian market with data pertaining to 2000–2009. The regression result confirms that dividend, PE ratio, and leverage are significant determinants of the market price in all three sectors, whereas profitability is a sector-specific determinant in the auto sector only. Sharma (2011) analyzed sixteen years of data on Indian companies, starting from 1993–94 to 2008–09. He found that earnings per share (EPS), dividend per share (DPS), and book value (BV) have significant impacts on the market price of Equity. Bhatt and Sumangala (2013) found book value and earnings per share as significant market price predictors in their study conducted on the top 50 companies in India based on market capitalization for data related to 2006–2011. Jadhav and Badade (2012) conducted a sector-specific study on India's banking, IT, and healthcare sectors. They found price-to-earnings ratio (PE) and dividend yield (DY) as significant determinants of the market price of equity shares in all three sectors. EPS is expected in the banking and healthcare sectors; DY is common in the healthcare and IT sectors, and BV is common in India's banking and IT sectors. Srinivasan (2012) conducted a study on six sectors in India: manufacturing, pharmaceuticals, energy, IT & ITES, infrastructure, and banking. The panel data about 2006–2011 was analyzed using the fixed effects model and the random effects model. He found the EPS and PE ratios are significant in the manufacturing, pharmaceutical, energy, infrastructure, and commercial banking sectors but insignificant in the IT & ITES sectors. Similarly, book value is essential in the pharmaceutical, energy, IT & ITES, and infrastructure sectors, whereas it is insignificant in the manufacturing and banking sectors. Tandon and Malhotra (2013) applied linear regression to study data from 2007 to 2012 for 95 sample companies in India. They reported that BV, EPS, and PE ratios have a significant positive association with the market price of the share, while dividend yield has a significant negative influence on the market price of the share. The sector-specific results should be reported in their study. Nautiyal and Kavidayal (2018) studied the actively traded top 30 companies of the NIFTY 50 index to predict the market price using fundamental ratios. They found EPS is poorly connected with market price, whereas economic value Hedau & Mishra, Indian Journal of Finance and Banking 13(2) (2023), 14-22 16 added (EVA) and dividend per share are moderately predictive of the market price of Equity. EVA was used as an independent variable for the first time in an Indian context. Goyal and Gupta (2019) used earnings per share, dividend pay-out ratio, PE Ratio, net margin, return on equity, and return on assets to identify the factors influencing stock prices of 30 BSE-listed businesses. The data was analysed using a panel data most miniature square regression model. The findings show that earnings per share, net margin, and net income considerably impact a company's stock price. The comprehensive literature survey observed that:  Earlier studies considered a few sectors—NIFTY 50, BSE 100, and BSE 500 index companies—as sample sizes. The Nifty Next 50 (NN50) index companies have not been explicitly studied.  Most of the studies conclude with statistical findings. The present study will test the validity of regression findings using a semi-structured questionnaire survey and expert interview followed by matrix generation using Nvivo.  For the first time, economic value added (EVA) is used as an independent variable by Nautiyal and Kavidayal (2018). Further study is required to support EVA as a new determinant of the market price of Equity in India.  Scopus and Google Scholar, the reliable databases, show Goyal and Gupta (2019) and Kaur and Gupta (2021) as the recent studies on a similar topic in India. The topic requires further research as a consensus on the determinants has yet to be achieved. MATERIALS AND METHODS Population and Sample Selection Listed companies on the National Stock Exchange (NSE) form the population for the present study. The Indian stock market is represented by a well-diversified, broader benchmark market index called NIFTY50. It represents the weighted average of the 50 largest Indian companies listed on the NSE. The index constituents are not fixed. They are reviewed on a semi- annual basis in the months of June and December every year. Based on market capitalization and free float market capitalization of individual stocks, rebalancing and reconstitution are done in the NIFTY index. NIFTY NEXT 50 (NN50) is another set of 50 companies, representing the next rung of liquid stock after NIFTY 50 companies, with the possibility of forming part of the NIFTY 50 index in the future. The NIFTY 50 constituent companies are considered safer investment options than the other stocks listed on the NSE. The average rolling returns of NIFTY 50 and NIFTY NEXT 50 are compared in Table 1. Table 1. Comparative Returns of NIFTY Next 50 and NIFTY 50 Period NIFTY Next 50 NIFTY 50 1 Year 26.5% 20.3 % 3 Year 18.2% 15.7 % 5 Year 16.8% 14.0% 10 Year 15.9% 13.0% 15 Year 16.9% 14.5% Source: ETMoney, 2023 At this juncture, investors are curious to know the factors that drive the market price of NN50 companies. Using the biased sampling method, the authors have selected the companies forming part of the NN50 as a sample for the present study. Based on the availability of historical financial data, 41 companies (41/50 = 82 percent) are part of the final sample. Nine companies were eliminated due to the non-availability of consistent data pertaining to the period of research. The study analyzed the data related to January 2017 to December 2022. Sources of Data and Data Collection The present study employs qualitative and quantitative methods of data analysis. Therefore, the authors have used both primary and secondary data. The secondary data relates to the historical market price of the sample stocks and the variables determining the market price. Market price determinants are identified from the literature survey. Historical financial information about market prices and their determinants is collected from the PROWESS database maintained by the Center for Monitoring the Indian Economy (CMIE). The secondary data analysis is validated using primary data collected through a qualitative approach using a semi- structured open-ended survey and interviewing experts from the domain area. The primary data is collected from finance professionals, stock analysts, and academicians from a reputed institute with more than ten years of experience in their field. Methods of Data Analysis As mentioned in the literature section, it is justified to use forecasting techniques such as linear, non-linear, or hybrid models to overcome the limitations of the valuation model. The forecasting technique may be simple OLS regression, panel data methods, time series modeling, or machine learning algorithms. The level of complexity and information asymmetry across the world's stock exchanges means that no single model can be applied uniformly to the entire market (Rangi & Aithal, 2021). The regression technique can process large amounts of panel data spread across multiple years. Therefore, the author has selected OLS regression followed by semi-structured interviews with industry experts to test the validity of the regression findings. The data analysis is carried out in two stages. In the first stage, preliminary statistical techniques describe the data. The cause-and-effect relationship is explored using OLS regression to identify the determinants of market Hedau & Mishra, Indian Journal of Finance and Banking 13(2) (2023), 14-22 17 price for the sample companies. The data analysis using the technique of regression is done in the following four steps:  Model building  Model assumptions (multicollinearity, independence of residuals, normal distribution of residuals, and outlier influence)  Model adequacy (F ratio)  Model validation (by splitting the data set into two sets: training and testing) The statistical package for social sciences (SPSS) is used to analyze the secondary data. The primary data collected through the questionnaire survey and interviews are transcribed and coded in the second phase. Matrix coding has been performed using a qualitative tool such as NVivo to understand the pattern of codes. Regression Model, Dependent and Independent Variables The dependent variable is regressed against a set of independent variables in the regression model. In the present study, the market price of equity shares of sample companies is taken as a dependent variable. Market price information is available daily, weekly, monthly, quarterly, and annual. The average of the year's high and low prices is considered by Gill et al. (2012) and Tandon and Malhotra (2013). Sehgal and Pandey (2010) applied year-end closing prices, whereas Sukhija (2014) considered annual values in their respective studies. The more significant the difference between the two time periods used to calculate the average, the higher the range (the difference between high and low price), which results in higher variability. The author is convinced by the recent study by Kaur and Gupta (2021), who applied quarterly values in their study. Accordingly, we have taken the average of quarterly values of market price and independent variables in the present study. A brief explanation of dependent and independent variables observed in earlier studies is given in Table 2. Table 2. Description of Dependent and Independent Variables Sr. No Variable Description Literature reference Indian International Dependent Variable 1 Market price of Equity Quarterly closing market price Kaur and Gupta (2021) -- Independent Variables 2 Dividend Rate (Div. Rate) The rate of dividend declared by the company Zahir and Khanna (1982), Balkrishnan (1984), Srivastava (1984), Nirmala et al. (2011), Jadhav and Badade (2012), Chawla and Srinivasan (1987) Collins (1957), Karathanassis and Philippas (1988), Adebisi and Lawal (2015), Uddin (2009) 3 Book Value (BV) Net asset value per share is calculated using the following formula: Equity Capital + Reserve No. of Outstanding Shares Zahir and Khanna (1982), Balkrishnan (1984), Sharma (2011), Srinivasan (2012), Tandon and Malhotra (2013) Collins (1957), Almumani (2014), Al- Omar, and Al-Mutairi, (2008) 4 Leverage (DE) Total Debt Total Equity Nirmala et al. (2011) Midani (1991), Irfan et al. (2002) 5 Earnings Per Share (EPS) Profit After Tax (PAT) No. of shares outstanding Sharma (2011), Srinivasan (2012), Tandon and Malhotra (2013) Almumani (2014), Uddin (2009), Somoye et al., (2009), Al-Omar, and Al-Mutairi, (2008) 6 Economic Value Added (EVA) Net Operating Profit after Tax (NOPAT) – (WACC × Capital Invested) Nautiyal and Kavidayal (2018) -- 7 Financial performance (RNOW) Return on Net Worth (RNOW) is calculated as Net Income Shareholders Equity -- Adebisi and Lawal (2015) Source: Author’s Compilation. – indicate variable not yet studied Based on the above, the proposed regression equation is: Adj. Closing Priceij = β0 + β1Div Rate + β2DE + β3 EVA + β4EPS + β5 BV + β6 RONW + eij Where β0 is the regression constant, and eij is the error term RESULTS The data are first described through the basic analysis. For all the variables except dividend, a total of 205 observations are evaluated (41 firms' x 5 years beginning in 2017-2022). Since the dividend rate's quarterly values are not available, the present study considers the final dividend with 165 (33 companies x 5 years) observations over the five years of 33 (sample size of 41 – 8 non-dividend paying) companies. Table 3 presents the descriptive statistics. The large variety that can be seen between the research variables' minimum and highest values. When comparing the values of standard deviation, the Debt- to-Equity Ratio is the data set with the least amount of scattering, while EVA has the highest value of standard deviation. The minimum EPS, BV, and Return on Net Worth (RNOW) values are negative. Hedau & Mishra, Indian Journal of Finance and Banking 13(2) (2023), 14-22 18 Table 3. Descriptive Statistics Div. Rate DE AdjCloPrice EPS BV EVA RONW N Valid 165 575 575 575 575 575 575 Mean 235.5284 .8169 1185.0580 44.8664 196.5675 737396.5612 26.4536 Std. Error of Mean 18.42548 .07352 48.15608 5.09587 9.51253 18006.42591 1.24527 Median 100.0000 .1400 747.3000 11.7800 99.9800 624864.0000 25.1700 Mode 0.00 0.00 54.95a -36.45a 17.54a 95029.29a 27.99 Std. Deviation 439.90234 1.75534 1154.74212 122.19466 228.10250 438.92495 29.59975 Skewness 4.157 3.621 1.226 5.331 2.063 1.571 .638 Std. Error of Skewness .102 .102 .102 .102 .102 .102 .103 Kurtosis 20.628 14.533 .700 28.725 4.547 3.089 28.101 Std. Error of Kurtosis .204 .204 .203 .203 .203 .203 .205 Range 3525.00 12.87 4833.90 841.76 1272.91 2205976.21 526.74 Minimum 0.00 0.00 52.95 -77.54 -188.30 86445.00 -210.46 Maximum 3525.00 12.87 4886.85 764.22 1084.61 2292421.21 316.28 Source: SPSS Data Analysis Output To detect multicollinearity, a correlation matrix was constructed to infer the extent of correlation among the variables studied. The variables are moderately correlated between them. The values of the correlation coefficient are reported in Table 4. The highest (lowest) correlation coefficient value is +.77 (-.135), which revealed a moderate correlation between the variables. Table 4. Correlation Matrix AdjCloPrice Div. Rate DE RONW EPS BV EVA AdjCloPrice 1.000 Div. Rate .314 1.000 DE -.135 -.152 1.000 RONW .155 .412 .157 1.000 EPS .492 -.415 -.368 -.412 1.000 BV .645 -.220 -.242 -.240 .771 1.000 EVA .242 .253 .263 .420 -.140 -.157 1.000 Source: SPSS Data Analysis Output Table The regression assumptions (Multicollinearity, Independence of residuals, normal distribution of residual, and outlier influence) are tested before proposing the final regression model. The multicollinearity among the research variables is not a problem as the VIF values of significant variables are less than 5, and the Tolerance score is above 0.2, as reported in Table 7. The Durbin-Watson (DW) test value is 1.911 (close to the standard value of 2), as reported in Table 5, confirming that the residuals are uncorrelated and the independent error assumption is satisfied. The normality of residual values is checked with a graphical method using a histogram and normal probability plot, as reported in Figure 1 and Figure 2, respectively. The Cook's distance is under 1, indicating individual cases do not influence the regression model. Figure 1. Histogram of Adj. Closing Price Figure 2. P-P Plot of Stand. Residual Source: SPSS Data Analysis Output Table 5. Regression Model R R Square Adjusted R Square Std. Error of the Estimate Durbin- Watson 0.812 .659 .656 681.10615 1.911 Source: SPSS Data Analysis Output Table Hedau & Mishra, Indian Journal of Finance and Banking 13(2) (2023), 14-22 19 The regression model and ANOVA are reported in Table 5 and Table 6, respectively. It is found that the Dividend Rate, Debt-to-Equity ratio, Earning per Share, Economic value added (EVA), Book Value, and Return on Net Worth explain a significant amount of the variance in the value market price of Equity (F(6, 559) = 178.505, p < .01, R2 = .659, R2Adjusted = .656). Table 6. ANOVA Sum of Squares df Mean Square F Sig. Regression 496857336.891 6 82809556.148 178.505 0.000 Residual 256539791.914 553 463905.591 Total 753397128.805 559 Source: SPSS Data Analysis Output The regression coefficient, as reported in Table 7 shows that dividend Rate (β=.264, t(559) = 9.506, p < .01), return on net worth (β=.050, t(559) = 1.833, p < .1), Book Value (β=.719, t(559) = 18.384, p < .01) and Enterprise value (β=.380, t(559) = 14.524, p < .01) are statistically significant and positively influencing the market price of equity shares whereas debt-to-equity ratio (β= -0.167, t(559) = -6.353, p < .01) is negatively influencing the market price of Equity. The significance of RONW, which is not yet studied in the Indian context, and EVA, only studied by Nautiyal and Kavidayal (2018) in the recent past, confirms the changing pattern of significant variables over a period of time. Table 7. Regression Coefficient Unstandardized Coefficients Standardized Coefficients t Sig. Collinearity Statistics B Std. Error Beta Tolerance VIF (Constant) -408.697 71.493 -5.717 .000 Div. Rate .692 .073 .264 9.506 .000 .799 1.252 DE -116.483 18.336 -.167 -6.353 .000 .895 1.117 RONW 1.958 1.068 .050 1.833 .067 .824 1.214 EPS -.174 .367 -.019 -.475 .635 .405 2.471 BV 3.653 .199 .719 18.384 .000 .403 2.482 EVA .001 .000 .380 14.524 .000 .901 1.109 Source: SPSS Data Analysis Output The automatic linear regression modeling generated using SPSS shows 65.6 percent accuracy of the model with an AIC value of 7,487.076, as shown in Figure 3. The model is validated by splitting the sample into testing and training sets. An accuracy level of 87.62 percent is achieved in the validation process. The scatter plot of predicted values of dependent variables is reported in Figure 4. Figure 3. Automatic Linear Modeling. Figure 4. Scatterplot of Dependent Variable Source: SPSS Data Analysis Output The word cloud and matrix coding are reported in Figure 5 and Figure 6, respectively. Based on the expert's opinion, earnings per share and debt to equity ratio are the two most significant determinants of the market price of Equity, followed by book value and return on net worth. Nevertheless, the experts suggest that short-term and long-term business growth rates, industry outlook, business governance, and industry potential are the additional factors that influence the market price of Equity. Hedau & Mishra, Indian Journal of Finance and Banking 13(2) (2023), 14-22 20 Figure 5. Word Cloud for frequency of determinants of Market Price of Equity Div. Rate DE EPS RONW EVA BV Expert 1 1 1 1 1 1 Expert 2 1 1 1 Expert 3 1 1 1 Expert 4 1 1 1 1 1 1 Expert 5 1 1 Expert 6 1 1 Expert 7 1 1 1 Expert 8 1 1 1 1 Expert 9 1 1 1 1 Expert 10 1 1 1 Expert 11 1 Expert 12 1 1 1 1 Expert 13 1 1 1 1 Expert 14 1 1 1 1 Expert 15 1 1 1 1 1 1 Figure 6. Matrix Coding of determinants of the market price of Equity from experts DISCUSSIONS The current article expands on previously published research on the factors that affect equity market price. The present study discovered that the debt-to-equity ratio adversely impacted the stock's market price. Wippern (1966) asserts that a critical factor in attaining the objective of wealth maximisation is the financial structure. Nautiyal and Kavidayal (2018) note the opposing viewpoint in the Indian context and assert that there is no substantial relationship between the stock price and debt in the capital structure; the present investigation concurs with their findings of Nautiyal and Kavidayal (2018). The negative coefficient of -0.175 in the Indian scenario indicates that investors are risk-averse and that upward price movement is constrained by the use of extra debt in the company's capital structure. The negative coefficient of the debt-to-equity ratio is consistent with the earlier findings of Midani (1991). The present study supports the findings of Tandon and Malhotra (2013), Balkrishnan (1984), and Zahir and Khanna (1982) to conclude that book value has a positive and statistically significant impact in predicting the market price of Equity. The company's enormous reserves and surplus, as well as fewer external liabilities, raise the book value of the shares. The present study concludes that the liquidation approach to equity valuation is preferable in India rather than discounting future cash flows due to the longevity of the significance of book value in numerous research from 1981 up to 2023 in the current study. Earnings per share's negative coefficient, which is statistically insignificant (p > 0.05), shows that investors disagree that EPS influences price. A simple book entry made during a share repurchase or bonus issue can change the value of EPS. Investors do not want these discretionary decisions to determine price because they are made at the management's discretion. Nautiyal and Kavidayal (2018) provided evidence of the low predictive capacity of EPS, particularly in the context of the Indian situation. The current study supports Balke and Wohar's (2006) observation that dividend expectations are a key factor in stock price changes. The study advises businesses to adopt a liberal philosophy and confirms the conclusions of Sharma (2011) and Goyal and Gupta (2019). A positive and significant coefficient of economic value added shows that the company and its management must work to increase wealth for their shareholders. The logical connection between book value and EVA is supported in the current study, which shows that the more wealth generated for the shareholders, the more book value increases. A higher net worth for the corporation is likely inferred from the higher book value. A detailed examination of the coefficient of dividend rate, EVA, and return on net worth reveals the shareholders' expectations. As a result of the dividend rate and EVA having statistically significant values, investors can anticipate the company creating value for them and distributing it to them in the form of dividends, making the return on net worth statistically negligible. Hedau & Mishra, Indian Journal of Finance and Banking 13(2) (2023), 14-22 21 In the current study, the stated value of the adjusted R square is 65.6 percent. According to Kotha and Bhawna (2016), changes in macroeconomic variables caused an index value change of 11%. Together, the findings from this study and Kotha and Bhawna (2016) will account for 79% of the change. This will close the research gap that Tandon and Malhotra (2013) identified. CONCLUSIONS Investors in India might use the study's conclusions as a guide when choosing their investments. To enhance pricing performance and protect against volatility, it is advised that the management of the sample companies concentrate on increasing the numerical values of the positive coefficients of the significant variables. For investors in growing economies like India, the subject of the current study is crucial. The author concurs with Nautiyal and Kavidayal's (2018) and Sharma's (2011) assertions that basic analyses of financial factors have a significant predictive value for equities market prices. Therefore, investors must understand the significance of such analyses and consider them when making wise investment selections. According to Almashaqbeh, Islam, and Bakar (2021) investors must analyse psychological and behavioural characteristics before investing in the stock market because stock price movements can create significant swings in portfolio performance. The study does have certain limitations, though. The study's findings depend on how reliable the secondary data used to support them was. Second, the study's small sample size makes it impossible to generalise the results. The analysis is based on previous financial values, which might vary nonlinearly and unpredictably when the business climate shifts. Beyond their financial metrics, the sample firms might investigate applying the knowledge-based theory of the firm that Nickerson and Zenger (2004) put forward to create alternative (non-financial) capacities. The methods for data analysis, the choice of independent variables, and the study length can all be considered study boundaries. The complexity of each sector of the Indian economy may be better understood through large sample sizes or sector-specific studies using more advanced data analysis techniques. Author Contributions: Conceptualization, A.H.; Methodology, A.H.; Software, A.H.; Validation, A.H.; Formal Analysis, A.H.; Investigation, A.H.; Resources, A.H.; Data Curation, A.H.; Writing – Original Draft Preparation, A.H.; Writing – Review & Editing, A.H., and S.M.; Visualization, A.H.; Supervision, A.H.; Project Administration, A.H.; Funding Acquisition, A.H., and S.M. Authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement: Ethical review and approval were waived for this study because the research does not deal with vulnerable groups or sensitive issues. Funding: The authors received no direct funding for this research. Acknowledgments: The author acknowledges the National Institute of Construction Management and Research, Hyderabad, India, for providing access to the database to download the secondary data and software for data analysis. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The secondary data presented in this study are available on request from the corresponding author. The primary data may not be available to maintain the respondent's confidentiality. Conflicts of Interest: The authors declare no conflict of interest. REFERENCES Adebisi, O. S., & Lawal, K. O. (2015). Equity share price determinants: a survey of literature. Arabian Journal of Business and Management Review (OMAN Chapter), 5(3), 1-7. Retrieved from https://platform.almanhal.com/Files/Articles/75678 Almashaqbeh, M., Islam, M. A., & Bakar, R. (2021, May). Factors affecting share prices: A literature revisit. 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