RELATIONSHIPS BETWEEN STOCK RETURNS AND FINANCIAL RATIOS: EVIDENCES FROM THE INDIAN STOCK MARKET (BSE)

Submitted by:

Anant Kumar (FE)

Piyush Gupta (AE)

Sumanjay Dutta (FE)

Pankaj Rai (FE)

Madras School of Economics

Under the adept aegis of:

Dr.Sowmya Dhanaraj

Introduction

Over the past few decades there has been a considerable interest in financial ratios and their ability to predict stock returns; financial ratios are widely acknowledged as being accurate in determining the investment potential of a company. In addition to this, they allow for insight into liquidity, liabilities as well as the extent to which a company uses its assets to generate returns. A wide range of literature has evolved over the past few decades, which try to determine the ratios which are most helpful in determining the stock returns. Early studies by Gordon (1959), Bower & Bower (1969) and Zahir (1992) found that stock returns were indeed affected by a number of firm specific variables such as earnings, dividends, risk leverage, size, etc. In light of this, the present study will analyze the effect of some of these variables on stock returns by studying the top 20 companies (by market capitalization) listed at the Bombay Stock Exchange (BSE).

Objectives of the study:

One of the most important motivations behind studying financial markets is the returns that are to be gained from stocks. The financial markets comprise of numerous players who respond to stimuli of various kinds. This hyperactivity is what gives the markets their volatility. Nevertheless, researchers, analysts, investors and traders have tried to study stock returns in the light of the macroeconomic variables, balance sheets, financial ratios, etc.

The forces that move a stock fall into three main categories – Fundamental and Technical. Technical analysis assumes that prices discount all the information available in the market and chart patterns suffice to provide information on the prospects of a stock. Popularized by Charles Dow in the late 19th century, it is extensively used tool by day traders across the globe. Fundamental analysis, on the other hand, focuses on firm level balance sheet and ratio analysis as well as macroeconomic analysis to judge the investment potential of a financial asset. Financial ratio, macroeconomic policy variables, conditions prevailing in the global markets fall into this category. The present study aims to determine the relationship between stock returns and key financial ratios that are used in everyday equity analysis. A good amount of literature is available on similar themes, but none of the studies have focused on the Indian stock market with a view to encompass the major sectors of the economy. This includes the coal industry, the power sector, the pharmaceutical sector, banking sector, etc.

In financial markets, there exists some relationship between equity valuation and financial health, which in turn are indicated by financial ratios.

Literature Review

A considerable amount literature and empirical research has been directed at the financial sector, in particular the behaviour of stock markets. in this respect, the past Century has been dominated by research relating to behavioural finance resulting in notable and seminal material published by the likes of Fama and French (1988) and Campbell and Shiller (1988). As a result of the deluge of research relating to the field, a number of distinct thought of schools have emerged, such as those which prescribe to the fact that stock movements and markets can be predicted to those who argue that stock markets are dynamic and complex, the movements of which are unpredictable and risky (Cambell and Ammer 1993, Cambell and shiller 1988, Papanastasopoulos et al., 2011, Rosenberg et al. 1985). stock market movements have fascinated observers and the emergence of behavioural finance as a discipline in its own right is a testament to the former.

Whilst the movement of stock markets are widely investigated and studied by researchers, the subject area holds a considerable interest to investors given that they face the direct risk in speculating on stock markets. So, to this extent, investors have a vested interest in observing the stock market and the increasing volatility associated with stock markets has resulted in investors seeking more reliable and precise ways of better explaining stock returns (Papanastasopoulos et al. 2011, tsoukalas 2005). Shafana et al. (2013) add to this and suggest that financial markets have serve to establish themselves as cornerstone of a number of economies therefore the behaviour stocks and returns has garnered interest from a number of quarters, extending beyond investors such as financial regulators, policy makers and government and stock market regulators in particular (shafana et al., 2013). in light of this, the subsequent section of the literature review will examine the latter phenomenon in greater detail and focus on the use of financial ratios in understanding stock returns.

Financial ratios are widely agreed to be effective in aiding potential investors in determining the financial health of a firm, the extent to which it effectively utilizes its assets as well as its ability to meet any debt obligations. That said however, the use of financial ratios is not confined to the latter as it is acknowledged by a number of authors that financial ratios can also be applied to stock markets as a tool capable of predicting returns (Lewellen 2004). Kheradyar et al. (2011) also note that financial ratios are especially effective in predicting stock returns given that they pose a lower level of risk when compared to other speculative variables and the observation and historical returns and movements (Bower and Bower 1969, Zahir 1992, Shafana et al. 2013).

Subsequent studies have now revealed that financial ratios are effective in predicting returns and this is further corroborated by growing evidences in its favour.(Fama and French 1992, 1995, and 1998; Kothari and Shanken 1997, Pontiff and Schall 1998, Lewellen 1994).. Historically, the Price to Earnings (P/E) ratio and the price to book value of equity (P/B) ratio have been used by investors for equity valuation.

DATA AND METHODOLOGY:

Data Description:

At the onset of the study, it is important to introduce the variables, both dependent and independent. In this study, we aim to study the relationship between stock returns and financial ratios. Stock returns for the top 20 companies listed at the Bombay Stock Exchange (by market capitalization) have been calculated on a YoY basis (from the beginning of the FY 2012 till the FY 2017). Every asset has some value, either intrinsic or derived (as in the case of derivatives). Proper valuation of equities plays an important role in financial markets. Some analysts use discounted cash flow (DCF) models to value shares, while others use financial ratios. Technical analysts on the other hand believe that prices are mostly driven by investor psychology. Literature suggests that despite the randomness that prevails in the financial markets, there exists some relationship between equity valuation and financial health, which in turn are indicated by financial ratios.

Stock Returns: Stock Returns are defined as the gains or losses made on equity holdings over a period of time. This period may vary over minutes, hours, days, months or years. In our case since we have computed stock returns over different accounting years the formula for stock returns becomes:

Stock Returns= (Price in the final year-Price in the initial year ) +Dividends/Price in the initial year

The above graph shows the variability of stock returns of the 20 companies under consideration over a period of 5 years.

Price to Sales ratio is defined as a valuation ratio that compares a company’s stock price and its revenues. It can be calculated either by dividing the company’s market capitalization by its sales over a 12-month period or on a per-share basis by dividing the share price by sales per shares. A low ratio indicates possible undervaluation while a high ratio represents overvaluation. This ratio is particularly useful when comparing firms in the same sector.

Bhandari (1988) opines that Debt Equity Ratio has a bearing on stock returns, controlling for firm size and Beta. Debt Equity ratio is a financial indicator that measures the relative proportion of debt (both short term and long term) to shareholder’s equity. A high D/E ratio means that the firm has been aggressive in financing its growth through debt. While a high D/E ratio in itself is not a bad thing, persistently high levels of D/E ratio is considered to be an indicator of the imminent burden that is to come.

Liquidity Ratio measures a company’s ability to pay debt obligations and its margin of safety. Liquidity ratios are most useful when they are used in comparative form. This analysis may be done internally or externally. In general, a high debt equity ratio indicates that a country is more liquid and has a better coverage of outstanding debt. Current Ratio is a liquidity ratio that measures a company’s ability to pay short term and long term obligations. To gauge this ability, this ratio considers the current total assets of a company relative to its current total liabilities. A value of ; 1 indicates that the firm is not in good financial health while a value ; 1 indicates that the firm is in a better position to pay off its debt.

Current Ratio is negatively related to stock returns according to this scatterplot, which is contrary to what theory suggests.

Dividend per Share (DPS) is the sum of declared dividends issued by a company for every share outstanding. It is calculated by dividing the total dividends paid out by a company (including interim dividends) by number of outstanding ordinary shares issued. DPS is important in the sense that a primary goal before the company is to return value to its investors. This turn depends on its profits, and hence on dividend payments. Thus DPS becomes an important tool for equity valuation.

Earnings per Share (EPS) is the portion of a company’s profit allocated to each outstanding share of common stock. Earnings per Share serve as an indicator of a company’s profitability. It is calculated as: Net Income – Dividends on preferred stock/ Average outstanding shares The EPS is an important fundamental because it break downs a company’s profit on a per share basis. The number of outstanding shares could change, and the total earnings of a company might not be a real measure of profitability for investors

The scatterplot suggests a weak positive relationship between stock returns and earnings per share.

Book Value per Share (BVPS) is used to determine the level of safety associated with each individual share after all debts are paid accordingly. It is measured as:

Book Value per Share = Total Shareholder Equity – Preferred Equity/Total Outstanding Shares

The BVPS indicates the dollar value remaining for common shareholders after all assets are liquidated and all debtors are paid. A high BVPS is good for equity holders since they have a better chance to be indemnified should the company dissolve.

There is a weak positive relationship between stock returns and book value/share

Return on Assets (ROA) is an indicator of how profitable a company is relative of its total assets. ROA gives an idea as to how efficient a company’s management is in utilising its assets. It is calculated as: Net Income/ Total Assets. It effectively means that a high ROA should have a positive effect on stock returns.

Market Capitalization refers to the total dollar market value of a company’s outstanding. It is calculated by multiplying price per share with the total outstanding shares. This figure is used to determine a company’s size. Using market capitalization is important because size is a basic determinant of characteristics which interest us, including risk.

Methodology:

Since our analysis involves a study of stock returns of 20 companies over a period of 5 years, we chose a panel data regression model. 8 financial ratios were chosen for this purpose. Firstly, we did a simple OLS Regression without dummies. Thereafter, we chose accounting years as the dummies to assess the impact on stock returns of different financial ratios, furthermore we took FY2017 as the benchmark year.We also conducted the Breusch-Pagan-Godfrey test to detect any heteroskedasticity. We also checked for pairwise correlation to detect multicollinearity. Further, we tried to find a best fit model, that is, only the variables that affect stock returns significantly were included. Data was taken from the CMIE database and BSE website.

Empirical Results and Analysis:

The multiple regression equation is as follows:

Rit= ?0+?1(PSit)+?2(MCapit)+?3(BVit)+?4(CRit)+?5(DERit)+?6(DSit)+?7(EPSit)+?8(ROAit)+€itRit= Stock returns

PSit=Price to sales ratio

MCapit=Market capitalization ratio

BVit=Book value per shareCRit=Current ratio

DERit=Debt-Equity ratio

DSit=Dividend per share

EPSit=Earnings per share

ROAit=Return on assets

This was the basic model, without dummy variables, that was tested first.

stockreturns Coef. Std. Err. t P;t 95% Conf. Interval

PSit -.0028074 .01196 -0.23 0.815 -.0265645 .0209498

MCAPit -3.18e-08 3.30e-08 -0.96 0.337 -9.74e-08 3.37e-08

BVit -.0006456 .000248 -2.60 0.011 -.0011383 -.000153

CRit -.107732 .0329724 -3.27 0.002 -.1732275 -.0422365

DEit -.0061319 .0088904 -0.69 0.492 -.0237915 .0115277

DPSit -.0021915 .0030774 -0.71 0.478 -.0083044 .0039214

EPSit .0056715 .0018269 3.10 0.003 .0020427 .0093004

ROAit .0030424 .0049605 0.61 0.541 -.006811 .0128958

Cons .2942836 .0887102 3.32 0.001 .1180718 .4704955

R-squared 0.1914

Adj R-squared 0.1203

Table 1: Regression results before accounting for dummies

From the table, we observe that Price to Sales Ratio, Average market capitalization, Book Value per Share, Current Ratio, Debt Equity Ratio and Dividend per Share are negatively related to stock returns. On the other hand, EPS and Return on Assets are positively related to Stock returns. When checking for significance, we find that only Book Value per Share, Current Ratio and EPS have any serious effect on stock returns. ( Since, the P-value of only these variables are less than 0.05). The explanatory variable s in the current model explains approximately 20% of the variability present in the Stock returns.

Stock Returns Coef. Std. Err. t P;t 95% Conf. Interval

PS -.0011415 .0106094 -0.11 0.915 -.0222289 .0199459

MCAP -3.69e-08 3.00e-08 -1.23 0.222 -9.66e-08 2.27e-08

BVPS -.000709 .0002181 -3.25 0.002 -.0011425 -.0002756

CR -.1056952 .0289241 -3.65 0.000 -.163185 -.0482053

DER -.0057619 .0078129 -0.74 0.463 -.0212908 .009767

DPS -.004956 .0027602 -1.79 0.076 -.0104408 .0005317

EPS .0067147 .001623 4.14 0.000 .0034888 .0099407

ROA .0034748 .0043898 .79 .431 -.0052504 .0121999

d2013 -.1596996 .0745062 -2.14 0.035 -.3077887 -.0116106

d2014 -.1153823 .0723508 -1.59 0.114 -.2591875 .0284228

d2015 .070144 .0716693 0.98 0.330 -.0723064 .2125944

d2016 -.3014626 .071825 -4.20 0.000 -.4442225 -.1587027

_cons .3967417 .0913517 4.34 0.000 .2151702 .5783131

R-squared 0.4073

Adj R-squared 0.3256

Table 2: Regression results after including dummies

Using time dummies we are able to analyze the effect of financial ratios on stock returns for a particular year. The benchmark for this purpose is set to be FY2017.From the table we see Stock Returns have been “significantly” affected by financial ratios in FY 2015 ; FY2016 compared to FY2017.It can also be seen from the table that the Stock Returns in FY2013 ; FY2014 is not statistically different from the FY2017.

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity

chi2(1) 2.10

Prob ; chi2 0.1475

Ho: Constant variance

We used Breusch-Pagan Godfrey test for testing for Heteroskedasticity in our data .Since the p-value is greater than 0.05 we conclude that the variance of disturbance term is Homoskedastic in our data .

We conducted a panel regression because the analysis involves a number of companies studied over a period of time. Then we conducted the Breusch/Pagan LM test and saw that we could proceed with the OLS method as well.

stockreturnscompany1,t = Xb + ucompany1 + ecompany1,t

H0: No Pannel Effect or Var(u) = 0

chibar2(01) 0.00

Prob;chibar2 1.0000

Since P-value is greater than 0.05 so we fail to reject the null hypothesis. So we found that there is no evidence of significant difference across companies, therefore we can run a simple OLS regression.

Testing for multicollinearity using Correlation matrix:

stockr~spricet~oavgmkt~p bookva~e curren~o debteq~o divide~e epsreturn~s

stockreturns1.0000

pricetosales0.03631.0000

avgmktcap-0.11810.14581.0000

bookvalues0.0919-0.2924-0.02851.0000

currentratio-0.15350.07850.3704-0.12601.0000

debteqityr0.0140-0.0026-0.09850.2737-0.58011.0000

dividendsh0.0884-0.03790.35850.45310.4349-0.16821.0000

earnings per share0.1769-0.12850.14950.86160.20800.05370.72841.0000

returnonassets0.04090.18150.3074-0.22230.7065-0.59720.41580.14851.0000

Table 3: Correlation Matrix

We have a clear evidence of a high pairwise correlation b/w EPS ; Book value per share, Return on assets ; Current Ratio and EPS ; Dividend per share. But we have no any evidence of perfect multicollinearity.

From table1 we can see that the Market Cap., Price to Sales ratio, Debt Equity Ratio, Dividend per share and return on assets are insignificant to our model. So we can drop these variables for the best fit model. After dropping the insignificant variable we have a clear evidence of the high Adj-R squared .So it is better to drop the insignificant variable in the search of the best fit model.

R-squared 0.1609

Adj R squared 0.1347

Best Fit Model

stockreturnsCoef. Std. Err. t P;t 95% Conf.Interval

currentratio-.0965705.023739 -4.07 0.000 -.1437049-.0494362

eps .0050712 .00116894.340.000.0027504.0073921

bookvalues-.0006389.0001761-3.630.000-.0009886-.0002892

d2015 .1282276.05940022.160.033.0102872.2461681

d2016 -.2249876.0592272 -3.800.000-.3425846-.1073905

_cons .2471618.04872065.070.000.1504259.3438978

Table 4: Best Fit Model results

Thereafter, we found a best fit model comprised of only three ratios as independent variables and 2 years as dummies. In FYs 2015 and 2016, these ratios explain stock returns better, vis-à-vis the other years. Part of this could be attributed to greater market volatility in these years. This sums up the study telling that not all ratios have an effect on stock returns. And only a few key ratios explain the variations in stock returns to some extent. This is pretty much in line with previous studies that show that though very important, financial ratios are not sufficient in explaining price behavior.

Conclusion

Firm specific characteristics are essential to explain the behavior of the stock returns. A number of studies well documented the relationship between stock returns and most popular firm specific factors such as price-to sales ratio, book to market ratio, earnings per share, dividend yield, dividend payout ratio and firm size in developed countries. These documents are mentioned in the previous sections in literature review and results interpretation. earlier research studies found on the behavior of expected stock returns with respect to the firms’ specific factors in both developed and developing countries, there have been a very few of such studies in the Indian stock markets. Therefore, this paper aimed to reinvestigate the behavior of stock returns with respect to the firms’ financial ratios in Bombay Stock Exchange. There were 20 firms and 8 financial ratios selected from each firm. Panel Data regression method showed that only three ratios, Current Ratio, EPS and Book Value per Share have any effect on stock returns in our dataset. One reason for other ratios to be ‘insignificant’ or yield counterintuitive results could be that markets being highly volatile, there are other factors, psychological and economic, that may affect stock returns. Shleifer (2000) talks about repeated phases of ‘irrational exuberance’ that have driven stock market booms. A good amount of literature has come up in the recent years which study the behavioral aspects of financial decision making. For example, in the 2008 crisis, junk bonds were rated AAA by credit rating companies despite the fact that the balance sheets told a different stories. Part of this was attributed to a ‘bandwagon’ effect that ensues once the markets start exhibiting a long term upward trend. Our inferences from the results of this study indicate towards this consensus that the element of randomness is key determinant of returns on assets.

One of the limitations of this analysis are the duration of the data. It will be certainly better when the future research can run a long run regression analysis and especially divide into different stock durations such as bull and bear durations repeatedly for a long run analysis. This would help in comparing between periods when valuations are high compared to when they are low. It is believed that the result will be more convincing and reliable, as they will account for market perception as well. Further, there is also a scope for encompassing the behavioral aspects of the market as well, given the fact that an increasing amount of theoretical literature now accounts for these. Provided the availability of data, a cross-country comparison between emerging markets on this topic could be done once in the future.

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