The underlying goal of most firms is to achieve maximization of shareholders' wealth. If corporate financial managers indeed seek to pursue this goal, they need to know something about the significant affects of decisions pertaining to financial policies on the systematic risk. An appreciation in the company's stock price is considered to be a common measure of wealth creation. The decisions regarding financial policies significantly affect the systematic risk which in turn influences the stock price, hence, resulting in wealth creation or depletion. Systematic risk affects the stock price in the sense that, since, systematic risk measures the volatility of a corporation's common stock relative to the market as a whole; it provides a numerical estimate for predicting the anticipated future returns on common stock which, in turn, cause movements in stock prices.
In earlier studies, various financial policy variables have been found to exert significant effect on the β of common stock. Some of the researchers focused on the relationship of β with a single variable, that is, Gonedes (1973) found that companies' accounting income is significantly associated with the systematic risk; Hamada (1972) identified firm's capital structure to have a significant effect on the common stock β; and Lev (1974) discovered an empirical relationship between firm's operating leverage and the market β. Whereas, other studies have centered on establishing links between β and a number of variables related to various financial policy and accounting dimensions. That is, Castagna and Matolcsy (1978) supported debt-to-equity ratio, debt-to-total assets ratio, EBIT-to-total assets ratio, return on shareholders' funds, EPS growth, trading volume, liquid ratio, current ratio, dividend payout ratio, and interest coverage to bear significant correlations with the systematic risk. Similarly, Ben-Zion and Shalit (1975) found dividend record, firm size, and leverage to be the significant determinants of firm's β. In addition, Melicher (1974) supported total assets; net plant-to-total capital ratio; rate of return on common equity; stock traded-to-stock outstanding ratio; and dividend payout ratio, whereas, Lev and Kunitzky (1974) supported sales smoothing; dividend smoothing; capital expenditures smoothing; earning smoothing; and average dividend payout ratio to bear significant multivariate connections with the β of common stocks.
The purpose of this research study was to analyze the impact of leverage, liquidity, profitability, and dividend payout decisions on the systematic risk of common stocks of firms included in the KSE-100 Index. Research into the examination of the impact of fore-mentioned managerial decision variables on the systematic risk of common stocks could be useful to financial managers and investors in many areas.
The theoretical framework for β as a measure of common stock risk is laid down by the capital asset pricing model (CAPM) of Sharpe (1964) and others. The CAPM, based on certain assumptions, puts forth that the expected rate of return on ith security, denoted by 'Ri', is a linear function of a risk-free borrowing and lending rate, RFR, and the expected return on a market factor, Rm.
However, beta, as proposed by the capital asset pricing model, can never be observed directly because it is truly a theoretical based result. Instead, beta estimates gained from the historical return data have to be relied upon. Sharpe (1963) has provided the "market model" which is used to estimate the systematic risk by running a time series regression.
Beta gained from the "market model" requires that β was stationary during the estimation period. Unfortunately, beta stability is not supported on the individual security level; however, stationarity on the portfolio level has been confirmed by Blume (1971).
Problem statement
The issue of determinants of systematic risk has gained a considerable attention in the corporate finance literature. A large body of evidence, i.e. different studies by Castagna and Matolcsy (1978), Thompson (1976), Ben-Zion and Shalit (1975), Bildersee (1975), Melicher and Rush (1974), Melicher (1974), Rosenberg and McKibben (1973), Breen and Lerner (1973), and Logue and Merville (1972), suggest that various financial and accounting variables direct differential betas among common stocks. A research into the effects of financial variables on systematic risk would make the financial managers understand about how changes in the financial decisions influence the systematic risk of their corporations, which in turn, affects the shareholders' wealth. It is useful for investors also, as they would be able to predict β on the basis of financial policy information, hence, constructing efficient portfolios. The purpose of this study was to examine the impact of liquidity, leverage, profitability, and dividend payout ratios on the systematic risk of common stocks of firms listed on KSE-100 Index (Benchmark). The research work was aimed at answering the following questions:
What kind of impact does a firm's leverage exert on its systematic risk?
What kind of impact does a firm's liquidity have on its systematic risk?
What kind of effect does a firm's profitability reveal on its systematic risk?
What kind of influence does a firm's dividend payout ratio exert on its systematic risk?
Hypotheses
The study was based upon four hypotheses, formulated as under;
H1: Leverage had a positive impact on the systematic risk of common stocks.
H2: Liquidity had a negative impact on the systematic risk of common stocks.
H3: Profitability had a negative impact on the systematic risk of common stocks.
H4: Dividend payout ratio had a negative impact on the systematic risk of common stocks.
Outline of the study
This research study is comprised of five chapters. Chapter 1 presents a brief introduction and introduces the theoretical framework; chapter 2 provides and discusses the literature review; chapter 3 explains the sample and methodology in detail; chapter 4 presents results and findings of the study; and chapter 5 provides discussions, implementations, and conclusion of the study alongside the suggested directions for future research.
CHAPTER 2: LITERATURE REVIEW
Portfolio and capital market theories have directed to the development of a basic concept of systematic risk, which is also know as the β or the un-diversifiable risk or the market risk. It measures the sensitivity or volatility of a corporation's common stock relative to the overall market (e.g. KSE-100 Index). Since, a market's β essentially equals to 1, a firm having β>1 implies that its common stock price will be more volatile than the market, if a firm has β<1, its stock price is said to be less sensitive than the market, and a firm's β=1 implies an average riskiness of its common stock. In other words, β provides a quantitative estimate regarding the riskiness of a company's common stock relative to the average riskiness of the market portfolio, that is, for instance, if a stock's beta is 1.5, it is theoretically considered to be 50% more risky than the market as a whole.
The risk measure for a given portfolio of assets was first developed by Markowitz (1952, 1959). In his portfolio model, the variance of the portfolio's rate of return was demonstrated to be a significant determinant of the portfolio's risk under a logical set of assumptions. The model further illustrated that, as N increased in a given portfolio, the riskiness of the portfolio as measured by its variance, became dependent on the average covariance of a stock with the other stocks in the portfolio rather than the individual variance of the given stock. That is to say, if a common stock bears a high variance but exhibits a low covariance with other stocks in the portfolio, then the given stock would not be a risky stock to have because the addition of this stock into portfolio will reduce the portfolio's variance (riskiness of portfolio). Thus, for understanding risk, the concept of covariance holds utter importance.
Covariance measures the extent to which rates of return of two stocks move together relative to their individual mean values over time. Two stocks are said to have a positive covariance if realized returns for both the stocks are either greater or lower than their mean returns during a specified time period. Whereas, on the other hand, covariance between two stocks tends to be negative if one stock has realized returns greater than its mean return but the other stock has realized returns less than its mean return for a given time interval.
The evolution of systematic risk as a reliable risk measure and the measurement of systematic risk is related to the work on basic portfolio model (market model or diagonal model) and the capital asset pricing model by Sharpe (1963, 1964) and others which, basically, is an extension of the work done by Markowitz (1952, 1959). The market model defined the riskiness of a given portfolio, in terms of the average β's of the stocks comprising the portfolio rather than the portfolio's variance. The model further illustrated that the riskiness of portfolio as measured by average β's of stocks comprising the portfolio, is dependent upon the individual β of the stock rather than its individual variance. In addition to that, common stock risk was also classified into two components: the systematic risk and the unsystematic risk.
Systematic risk is considered to be an unavoidable risk because it can not be eliminated by an investor during the holding period of a particular common stock. On the other hand, unsystematic risk is treated as an avoidable risk because it can be driven to zero through increasing N in a portfolio, i.e. through diversification. It is important to note that the concept of risk as reflected by the β is not different from the concept of risk as measured by the covariance, that is, mathematically; β equals to the covariance of returns on common stock with the average returns on a market portfolio. The β also relates the covariance of returns on stock with the market return to the variance of the market in order to standardize the given risk measure. As a result of this standardization, the market β essentially equals to 1, indicating the average riskiness.
The capital asset pricing model, determinant of the equilibrium prices for all stocks in the market, provided further theoretical support for the β to be a meaningful risk determinant. In fact, in context of the capital asset pricing model, the systematic risk coefficient, β, is the only variable that determines the differential returns among common stocks. Apart from that, the model also exerted that there exists a linear relationship between the systematic risk and the stock return, that is, the higher the risk the greater the stock return, when other things are held equal.
A number of research studies have supported the empirical validity of β as a reliable risk measure which is related to testing of two assumptions as proposed by the theory: i) the stability of β, and ii) the hypothesized linear relationship between expected return and β. In a study, Blume (1971) found that β's are constant over time, especially at the portfolio level. He also found that β's estimated from historical returns are unbiased, and that they have a tendency to regress toward the unity (β of the market) over longer intervals of time. In another study, Jensen (1969) also supported the stationarity of β's at the portfolio level. Sharpe and Cooper (1972) discovered that return was positively related with the risk; however, the relationship was not fully linear. Black, Jensen, and Scholes (1972) found a positive linear relationship between portfolio β and monthly excess return. Grundy and Malkiel (1996) confirmed that β is a very reliable risk determinant when the markets are declining. Reilly and Wright (2004) found the return and risk relationship to be significant and in accordance with the theory. Finally, Pettengill, Dundaram, and Matthur (1995) comprehended that empirical studies generally use realized returns instead of expected returns as proposed by the theory. Therefore, they adjusted for negative market excess returns and discovered a steady and significant relationship between stock return and β.
Similarly, many researchers in the past have focused on forging empirical links between the systematic risk and various financial and accounting variables. The selection of these variables, where, rests on the a priori expectations on one hand, on the other hand, the importance of some of the variables is laid down by the corporate finance literature related to the determinants of risk and return characteristics of firm. That is, the direct valuation models, such as the Gordon Growth model, stress the importance of earnings growth and dividend payout as the determinants of stock price. Likewise, in context of the work done by Modigliani and Miller (1958), leverage ratios can be exploited as a measure of the risk brought by the capital structure. And lastly, Bower and Bower (1969) and Malkiel (1970) have associated earnings growth, dividend payout ratio, and some measure of variability of descriptive variables with the price earnings ratios.
The first significant study that attempted to empirically associate the systematic risk with the accounting and financial variables, was carried out by Beaver, Kettler, and Scholes (1970). They found dividend payout, leverage, earnings variability, and accounting beta to bear significant associations with the systematic risk. The liquidity variable was significant only in 1947-1956 period and dividend payout variable exhibited a very strong association with the systematic risk. The researchers concluded that there exists a high degree of association between accounting variables and the systematic risk.
In another study, Castagna and Matolcsy (1978) analyzed variables related to financial structure, liquidity, profitability, dividend payout, interest coverage, growth and variability in earnings, company size, and marketability of company's stock for having empirical associations with the systematic risk. Debt-to-equity ratio, debt-to-total assets ratio, EBIT-to-total assets ratio, return on shareholders funds, growth in EPS, and trading volume were confirmed to be positively related with the systematic risk, whereas, liquid ratio, current ratio, dividend payout ratio, and EBIT-interest payment ratio were supported for having negative relationship with the systematic risk.
Gonedes (1973) examined company accounting income by transforming the income according to several formulations. In general, significant and consistent association was found to exist between the scaled first difference in income levels and the common stock β. The researcher concluded that there exists a statistically significant and reliable association between the accounting income numbers and systematic risk if the income numbers are transformed.
Other studies have attempted to establish multivariate links between the systematic risk of common stocks and various corporate finance variables. Logue and Merville (1972) regressed systematic risk on short-term liabilities to total assets ratio, long-term liabilities plus preferred stock to total assets ratio, current ratio, annual percentage change in firm's assets, total asset turnover ratio, operating profit margin, return on assets, dividend payout ratio and logarithm of total assets. They found the variables for leverage, profitability, and size to be statistically significant. Apart from that, the researchers reported the existence of problem of multicollinearity.
Hamada (1972) found that firm's capital structure has a significant effect on the systematic risk of common stock. The researcher concluded that leverage can be used to explain 21 to 24 percent value of the common stock β.
Breen and Lerner (1973) examined debt-to-equity ratio, debt-to-equity ratio squared, growth of earnings, stability of growth in earnings, company size, dividend payout ratio, and trading volume. They found many of the variables to be insignificant, and those significant were inconsistent from month to month. However, the researchers acknowledged that signs of the regression coefficients were in the directions as expected.
In another study, Rosenberg and McKibben (1973) found standard deviation of EPS growth measure, latest annual proportional change in EPS, Standard & Poor's quality rating, liquidity, absolute magnitude of dividend per share cuts, leverage, growth measure for total net sales, growth measure of EPS available for common shareholders, gross plant per dollar of total assets, historical beta, share turnover as a percentage of shares outstanding, logarithm of unadjusted share price, and book value of common equity per share-to-price ratio to be statistically significant. The researchers recognized that out of 13 variables, only 4 had the expected signs. Furthermore, the variables exhibited 2.2 percent additional descriptive power than the naive assumption of β=1 for all stocks.
Melicher (1974) found total assets, stock traded-to-stock outstanding, rate of return on common equity, and net plant-to-total capital exerting a significant positive influence, whereas, dividend payout ratio exerting a significant negative influence on the systematic risk. Furthermore, long-term liabilities plus preferred stock-to-common equity was found to exert a significant nonlinear effect on the systematic risk.
In a subsequent study, Melicher and Rush (1974) analyzed the multivariate ability of long-term leverage, total leverage, total assets, dividend payout ratio, EPS growth trend, EPS growth trend R-square, return on equity, operating income-to-operating revenue, net plant-to-total capital, return on common equity, institutional holdings-to-shares outstanding, revenues-to-total revenues, accounting method employed, and bond rating for describing the changes in systematic risk. They found EPS growth, total leverage, net plant-to-total capital, and accounting method employed to be statistically significant. The variables explained only 25 percent of the relative changes in systematic risk.
Lev (1974) regressed systematic risk on average variable cost and found that variable cost has a significant negative relationship with the systematic risk. The researcher concluded that the lower the unit variable cost, the higher will be operating leverage, and therefore, the higher the systematic risk of common stock will be.
In another study, Lev and Kunitzky (1974) examined dividend payout ratio, sales, capital expenditures, capital structure, production growth, dividend growth, dividend per share, and various pointers of smoothing in a firm. Sales smoothing, dividend smoothing, capital expenditures smoothing, earning smoothing, and dividend payout ratio were statistically significant. All the regression coefficients behaved in the expected direction except that of the capital structure smoothing.
Ben-Zion and Shalit (1975) regressed systematic risk on the variables of company's size, leverage, and dividend record. The variables of dividend record and company size exhibited a highly significant negative relationship, whereas, the leverage variable revealed a highly significant positive relationship with the systematic risk.
Bildersee (1975) examined earnings available for common stockholders-common equity ratio, debt-common equity ratio, current ratio, common equity, average annual growth in assets, sales-common equity ratio, cash flow-liabilities plus preferred stock ratio, and accounting beta. He found debt-common equity ratio (leverage dimension), sales-common equity ratio, standard deviation of the earnings-price ratio, and accounting beta to be statistically significant.
Pettit and Westerfield (1972) analyzed dividend payment, leverage, firm size, liquidity, and earning per share growth. They found an extraordinarily strong negative association between the dividend payout ratio and systematic risk. Furthermore, the examination of effects of leverage, dividend payment, firm size, and EPS growth on the systematic risk revealed that dividend payout ratio and total assets exerted a significant and consistent negative influence on the systematic risk of common stock.
Thompson (1976) analyzed 43 variables that were derived from the financial, accounting, and market data. He found many of the variables to bear highly significant associations with the systematic risk, with the variables: the model, earnings multiple β, dividend payout ratio, and earnings multiple variance exhibiting strongest associations. The signs of the coefficients were in the anticipated direction for over 85 percent of the variables.
CHAPTER 3: RESEARCH METHODS
The research study was comprised of one dependent variable and five independent variables related to four distinct dimensions of financial policy. The dependent variable was systematic risk, measured as the β of common stock. The first independent variable chosen was the ratio of total long-term liabilities to total assets related to the leverage dimension and was defined as long-term debt plus the par value of preferred stock divided by total assets. Its anticipated direction of impact on the systematic risk was positive as introduction of debt into firm's capital structure results in an increased financial risk which in turn increases the chances of default or liquidation. Therefore, it could be stated that more debt leads to more vulnerable earnings on common stock and the systematic risk rises. In addition to that, Logue and Merville (1972) found it to exert a significant positive influence on the systematic risk. As a result, hypothesis was formulated as;
H1: Leverage had a positive impact on the systematic risk of common stocks.
The second independent variable was the current ratio pertaining to liquidity dimension and was defined as total current assets divided by total current liabilities. It was expected to have a negative impact on the systematic risk because of the judgment that if a firm is more liquid, it is perceived to be less susceptible to market variations. Furthermore, in different studies, Castagna and Matolcsy (1978), Bildersee (1975), and Beaver, Kettler, and Scholes (1970) found it to bear a significant negative correlation with the β. Consequently, the hypothesis was formed as;
H2: Liquidity had a negative impact on the systematic risk of common stocks.
The third and fourth independent variables, total asset turnover ratio and operating profit margin, measured the profitability dimension. Total asset turnover ratio was defined as sales divided by total assets and operating profit margin was defined as earnings before interest and tax divided by sales. The expected direction of the impact of these variables on systematic risk was based upon two interpretations. That is, on one hand, a positive relationship between profitability and β could be expected because of the logic that in case of higher profitability, investors would expect higher returns and stock's β will be high; whereas, on the other hand, a negative relationship between β and profitability could be expected based on the judgment that higher profitability indicates a lower probability of the organization failure which, in turn, acts to reduce the business risk and therefore systematic risk will be low. However, Logue and Merville (1972), in a study, found that these profitability measures exerted a significant negative effect on the systematic risk. Hypothesis was designed as;
H3: Profitability had a negative impact on the systematic risk of common stocks.
And lastly the fifth independent variable was dividend payout ratio which measured the dividend payments dimension, defined as dividend per share divided by earning per share. It was expected to have a negative impact on the systematic risk based on the logic that from investors' perspective, the flow of returns from dividend payments could be perceived as more certain than flow of returns from capital gains in the later future. Apart from that, Thompson (1976), Lev and Kunitzky (1974), Melicher (1974), and Pettit and Westerfield (1972) found dividend payout ratio to exert a significant negative effect on common stock β. Hypothesis was formed as;
H4: Dividend payout ratio had a negative impact on the systematic risk of common stocks.
3.1 Method of Data Collection
Companies' listings history and the list of KSE-100 Index companies, for the period 2004-2008, were obtained from Karachi Stock Exchange website, Daily Times Newspaper website, and the Search.com. Each company's closing stock prices on the last day of each month were collected for the 5-year period according to companies' respective accounts closure months, that is, from July 2003- June 2008 for companies having June 30 as their accounts closure dates; and from January 2004- December 2008 for companies having December 31 as their accounts closure dates. The stock price data was collected from the websites of Karachi Stock Exchange, ZHV Securities, and KHI Stocks. Similarly, closing KSE-100 Index values on the last day of each month were collected for the corresponding five-year periods from the Karachi Stock Exchange website. Companies' stock prices and KSE-100 Index values were skipped for three months (September 2008- November 2008) due to crash of the Karachi Stock Market in that period. The relevant financial policy data for each sample firm, for the period 2004-2008, was taken from the BALANCE SHEET ANALYSIS OF JOINT STOCK COMPANIES LISTED ON THE KARACHI STOCK EXCHANGE file which was retrieved from the website of State Bank of Pakistan. The file contains annual balance sheet data for all the firms listed on the Karachi Stock Exchange for the period 2003-2008.
3.2 Sampling Technique
This study was based upon a convenient sample of firms that were included in the KSE-100 Index (Benchmark). Firms were selected on the criteria: a) each company must have had remained included in the KSE-100 Index for the period 2004-2008; b) companies must have had June 30 or December 31 as their fiscal year ends; and c) financial companies were excluded from sample.
3.3 Sample size
Out of 100 companies, 48 non financial companies were found to meet the sampling criteria mentioned above. Out of those 48 companies, 6 companies were found to have statistically insignificant β-estimates and consequently eliminated from further analysis. Ultimately, the final sample was reduced to 42 non-financial companies which are listed, along with their estimated systematic risk, in Table A in the appendix.
3.4 Research Model developed
In order to gain systematic risk estimates for sample companies, each company's monthly stock returns were regressed on monthly KSE-100 Index returns over a period of 5 years corresponding to firm's fiscal year end. The model used is as followed;
Rit = αi + βi Rmt + Єi (1)
'Rit' is the monthly stock returns for company 'i' during period t; 'Rmt' is the monthly return for the KSE-100 index during period t; αi is constant term; βi is the systematic risk of company 'i'; and Єi is the random error. The monthly stock returns ('Rit') were defined as monthly percentage changes in the company's stock price, whereas monthly KSE-100 returns ('Rmt') were defined as monthly percentage changes in the KSE-100 Index. The computations of both 'Rit' and 'Rmt' were done as per the following formulas;
Closing price at closing of 'month t' - Closing price at closing of 'month t-1'
Rit = Ã- 100
Closing price at closing of 'month t-1'
Closing index at closing of 'month t' - Closing index at closing of 'month t-1'
Rmt = Ã- 100
Closing index at closing of 'month t-1'
Regression assumptions for systematic risk estimates of each sample firm were tested through normality tests and residual plots. On average, the assumptions of homoscedasticity, linearity, and independence of error term were not seriously violated. However, in some of the cases the normality assumption was violated. The normality was enhanced by deleting extreme and/or distant mild multivariate outliers or influencers identified through boxplots. This resulted in an improvement in the level of explanation too. However, in some of the cases, deletion of multivariate outliers did not improve the coefficient of determination, therefore, level of explanation was enhanced by regressing company's monthly stock returns on both the monthly returns on market portfolio and one-order lag of the dependent variable (company's monthly stock returns). For 42 companies having statistically significant β-estimates, returns on the market portfolio (KSE-100 Index) explained 30.64 percent of the variations in their stock returns, on average. Furthermore, the systematic risk estimates were found to regress toward the mean of the overall market as was discovered by Blume (1971).
The dependent variable, β, and independent variables, average leverage, average current ratio, average total asset turnover ratio, average operating profit margin, and average dividend payout ratio, were tested for normality through Kolmogorov-Smirnov and Shapiro-Wilk tests. Subsequently, a simple linear correlation analysis was conducted to identify the significant correlations between the dependent variable and independent variables. The averages of the fore-mentioned independent variables were computed on the annual company data over the period 2004-2008. The formulae used are as under;
Average leverage = Σ senior debt plus par value of preferred stock ÷ Σ total assets
Average current ratio = Σ total current assets ÷ Σ total current liabilities
Average total asset turnover ratio = Σ net sales ÷ Σ total assets
Average operating profit margin = Σ earnings before interest and tax ÷ Σ net sales
Average dividend payout ratio = Σ cash dividends paid to common shareholders
÷
Σ earnings available for common shareholders
In order to examine the impact of financial variables on the systematic risk of common stocks, multiple linear regression technique was employed. That is, systematic risk estimates were regressed on leverage, liquidity, profitability, and dividend payout variables for the 42 firms. The model gained the following form;
βi = b0 + b1LEV + b2CR + b3ATR + b4OPM + b5DPR + ui (2)
where 'βi' represented the systematic risk, for each sample firm, estimated from applying the model in the form of equation (1). 'LEV' represented average leverage; 'CR' represented the average current ratio; 'ATR' represented the average total asset turnover ratio; 'OPM' represented the average operating profit margin; and 'DPR' represented the average dividend payout ratio.
3.5 Statistical Technique
In context of the statistical techniques employed by past research studies, multiple linear regression technique was chosen for this study. That is, previously, studies conducted by Beaver, Kettler, and Scholes (1970) and Castagna and Matolcsy (1978) used correlation analysis to empirically associate the systematic risk with several accounting and financial variables. Whereas, Melicher (1974), Logue and Merville (1972), Breen and Lerner (1973), Rosenberg and McKibben (1973), Ben-Zion and Shalit (1975), Bildersee (1975), Pettit and Westerfield (1972), Lev and Kunitzky (1974), and Thompson (1976) employed multiple regression approach to forge multivariate connections between systematic risk and firms' financial and non-financial variables.
CHAPTER 4: RESULTS
4.1 Findings and Interpretation of the results
Table 1 presents the results for normality tests for dependent and independent variables in the study. The dependent variable, systematic risk, was found to be statistically normally distributed; whereas independent variables, leverage, current ratio, total asset turnover ratio, operating profit margin, and dividend payout ratio, were not normally distributed, statistically.
Table 1
NORMALITY TESTS
β
LEV
CR
ATR
OPM
DPR
.200
.577
.000
.000
.200
.000
.001
.000
.001
.000
.001
.006
where β= systematic risk, LEV= average leverage, CR= average current ratio, ATR= average total asset turnover ratio, OPM= average operating profit margin, and DPR= average dividend payout ratio
Sig. values for Kolmogorov-Smirnov test appear in top row and that of Shapiro-Wilk's in bottom row at degrees of freedom= 42.
The results for simple linear correlations between the systematic risk and five financial variables are reported in Table 2. The table contains correlation coefficients for the five financial variables along with their sig.-values. The sig. value= .039 for correlation coefficient of total asset turnover ratio suggested that systematic risk had a significant negative correlation with the total asset turnover ratio, at the 5 percent level. The correlation coefficient of dividend payout ratio had sig. value= .010 which indicated that dividend payout ratio had a significant negative correlation with the systematic risk, at the 1 percent level. The correlation coefficients for leverage, current ratio, and operating profit margin were not significant at the desired levels as their sig. values were greater than .05 suggesting that these variables had no correlations with the systematic risk.
Table 2
CORRELATION COEFFICIENTS BETWEEN SYSTEMATIC RISK AND FINANCIAL VARIABLES
LEV
CR
ATR
OPM
DPR
Period
(2004-2008)
.085
(.296)
-.138
(.192)
-.275
(.039)
.066
(.339)
-.360
(.010)
where LEV= average leverage, CR= average current ratio, ATR= average total asset turnover ratio, OPM= average operating profit margin, and DPR= average dividend payout ratio
Correlation coefficients appear in top row with sig.-values in parentheses.
Initially, multiple linear regression was run in the form of equation (2). The regression F value was found to be insignificant at the 5 percent level indicating toward the wrong inclusion of some unimportant variable(s) in the regression. An examination of t-values for regression coefficients revealed that operating profit margin had the smallest t-value amongst others, that is, its t-value was .703 whereas t-values for other variables were greater than 1 in absolute terms. In addition to that, regression coefficient for operating profit margin had the VIF value of 1.905, marginally close to the threshold of VIF= 2. This suggested that the problem of multicollinearity could be caused by the variable. These reasons provided the basis to exclude operating profit margin from the regression.
Second regression was run, regressing systematic risk on leverage, current ratio, total asset turnover ratio, and dividend payout ratio, results of which are reported in Table 3. The regression F value was significant at the 5 percent level. Point of interest was the adjusted coefficient of determination, which increased to .157 from .145 in the first regression. The unadjusted coefficient of determination (.239) implied that the independent variables in the model explained 23.9 percent of variations in the systematic risk. Leverage had a momentous regression coefficient of -.485 but its attached sig. value= .151 showed that the variable was not significant at the 5 percent level. Current ratio had a small regression coefficient of -.074. The variable was not significant because its sig. value= .130 was greater than .05. Total asset turnover ratio showed a tiny regression coefficient of -.072 and its attached sig. value= .046 confirmed that the variable was significant at the 5 percent level. And finally, dividend payout ratio had a substantial regression coefficient of -.357 and its attached sig. value= .025 indicating that the variable was significant at the 5 percent level. The signs attached to all the regression coefficients were found to behave as anticipated, except that of the leverage variable, that is, the direction of impact was theoretically supportive. The sign attached to the regression coefficient of leverage variable was negative.
Table 3
REGRESSION RESULTS WHERE SYSTEMATIC RISK IS THE DEPENDENT VARIABLE
Constant
LEV
CR
ATR
DPR
Adjusted
Regression F value
1.124*
(.000)
-.485
(.151)
-.074
(.130)
-.072*
(.046)
-.357*
(.025)
.239
.157
2.907*
(.035)
where LEV= average leverage, CR= average current ratio, ATR= average total asset turnover ratio, and DPR= average dividend payout ratio
Regression coefficients appear in the top row with sig. values in parentheses.
*Significant at the 5 percent level.
Regression's residual plot, partial regression plots, and residual's normality test are given in the appendix. The residual plot (Figure 1) looked fine, that is, residual values were randomly scattered around the zero-line depicting no specific pattern in distribution, which suggested that the assumptions of linearity and homoscedasticity were not seriously violated. Residual's normality test (Table B) confirmed the multivariate normality of residual and a Durbin-Watson= 1.934 suggested the acceptance of assumption of independence of error term. The partial regression plots for leverage (Figure 2); current ratio (Figure 3); total asset turnover ratio (Figure 4); and dividend payout ratio (Figure 5) exhibited no specific patterns in their respective distributions indicating that the inclusion of these variables in their original form, in the regression model, was correct.
4.2 Hypotheses Assessment Summary
Table 4
HYPOTHESES ASSESSMENT SUMMARY
HYPOTHESIS
PROPOSED RESULT
REGRESSION COEFFICIENT
SIG. VALUE
ACTUAL RESULT
H1: Leverage had a positive impact on the systematic risk.
To accept the hypothesis.
-.485
.151
Hypothesis rejected.
H2: Liquidity had a negative impact on the systematic risk.
To accept the hypothesis.
-.074
.130
Hypothesis rejected.
H3: Profitability had a negative impact on the systematic risk.
To accept the hypothesis.
-.072
.046
Hypothesis accepted.
H4: Dividend payout ratio had a negative impact on the systematic risk.
To accept the hypothesis.
-.357
.025
Hypothesis accepted.
where β= systematic risk, LEV= leverage dimension, CR= liquidity dimension, ATR= profitability dimension, and DPR= dividend payments dimension.
Table 4 summarizes the hypotheses assessment of the study along with the provision of regression coefficients for relative financial policy measures and their attached sig. values. The sig, value= .151 attached to the regression coefficient of leverage variable (LEV) suggested that the regression coefficient could be assumed as not significantly different from zero, which, in turn, caused to reject the hypothesis that leverage had a positive influence on systematic risk of common stocks of KSE-100 Index firms. Similarly, the sig. value= .130 attached to the regression coefficient of liquidity variable (CR) led toward the rejection of the hypothesis that liquidity effected the systematic risk of common stock of KSE-100 Index companies in a negative manner. On the other hand, the sig. value= .046 (less than .05) attached to the regression coefficient of profitability variable (ATR) implied that the coefficient was statistically significant. The existence of statistical significance of profitability variable when accompanied by the negative sign carried by its regression coefficient provided the support to entertain the hypothesis that profitability had a negative impact on the systematic risk of common stocks of KSE-100 Index companies. This suggested that profitability is perceived to be a converse proxy for business risk by the investors. And finally, the sig. value= .025 attached to the regression coefficient of dividend payout ratio (DPR) and the negative sign attached to its coefficient implied that the hypothesis that dividend payout ratio exerted a negative impact on the systematic risk of common stocks of companies included in the KSE-100 Index be accepted, that is, the higher the ratio of dividend payments to net earnings, the lower the systematic risk of common stocks will be when other things are held steady.
CHAPTER 5: DISCUSSIONS, CONCLUSION, IMPLICATIONS, AND FUTURE RESEARCH
5.1 Discussions
The results and findings of this study are theoretically justifiable and are in line with the findings of the past research work, that is, total asset turnover ratio exerting a negative impact on systematic risk of common stocks is based on the theoretical support that higher profitability translates into lower probability of firm failure which in turn results in a lower systematic risk. In other words, the logic behind the inverse relationship between profitability and systematic risk is arguable in the sense that investors view higher profitability in terms of an inverse proxy for business risk. In addition to that, the finding is similar to the finding of the study conducted by Logue and Merville (1972). The theoretical justification for dividend payout ratio exerting a negative influence on systematic risk of common stocks is based on the logic that investors prefer dividend yields on capital gains in the sense that flow of returns from dividends are expected to incur in the near future whereas the flow of returns from capital gains are expected to incur in the later future. In other words, flow of returns from dividends, in investors mind, are more certain when compared to the flow of returns from capital gains. Apart from that, the finding was identical to the findings of different studies carried by Matolcsy (1978), Thompson (1976), Lev and Kunitzky (1974), Melicher (1974), Breen and Lerner (1973), Pettit and Westerfield (1972), and Beaver, Kettler, and Scholes (1970). Apart from that, although the study could not succeed in its effort to find a significant link between systematic risk and current ratio (liquidity), the direction of the impact found, however, was in the anticipated direction. That is, the negative sign attached to the regression coefficient of current ratio is justified on the basis of the judgment that if a firm is more liquid, it is perceived to be less vulnerable to market variations, other things held constant. And finally, the finding of negative and statistically insignificant effect of leverage on systematic risk calls for a complete divergence between the finding and the theoretical support for the given relationship and the corporate finance literature. That is, a positive relationship was expected between the firm's leverage and its systematic risk based on the logic that more debt leads to an increased financial risk which in turn increases the chances of default and bankruptcy. In other words, as debt is introduced into the firm's capital structure, common stock earnings become more susceptible leading to a higher value of systematic risk. In addition to that, different studies by Logue and Merville (1972), Beaver et al. (1970), Castagna and Matolcsy (1978), Bildersee (1975), Ben-Zion and Shalit (1975), and Rosenberg and McKibben (1973) have confirmed the existence of a significant positive relationship between the firm's leverage and its systematic risk.
The failure to find statistical significance for current ratio (liquidity dimension) and leverage variable could be due to various reasons. One possible reason could be the specification of wrong functional form between the systematic risk and financial variables: leverage, liquidity, profitability, and dividend payout measures. However, the partial regression plots did not suggest that any kind of mathematical transformation be applied to one or more of the independent variables. Another possible reason for failure could be the unavoidable measurement errors present in the financial variables. These errors occur because 1) firms use different accounting methods for appraising same event, that is, for instance, use of Straight Line Method or Declining Balance Method or SYD for charging depreciation on fixed assets, and 2) the use of stock's realized return instead of the expected return as proposed by the capital asset pricing model. In addition to that, a possible reason for failure regarding statistical significance for leverage variable could be the chosen ratio (long-term debt plus preferred stock-to-total assets ratio), that is, the given measure was not as suitable as assumed earlier for representing firm's leverage dimension due to the reason that out of 42 sample companies, 13 had zero long-term debt and preferred stock on their balance sheets over the five-year period.
5.2 Implications and Recommendations
The useful contributions and implications of the study can be explored in many ways. First, it would give the financial managers a better perspective of how the systematic risk of their firms is affected by the changes made in the corporate financial policies. Second, better forecasts of systematic risk can be made by the investors based on the information regarding financial policies. Third, a "targeted systematic risk" could be set as a part of the long range financial planning and be pursued through appropriate changes in the financial policies over time. Lastly, mathematical planning models in addition to other constraints for optimization purpose, relevant to conditions, can be integrated with a systematic risk-constraint too.
5.3 Future Research
The future research could carry on along numerous lines. First, the contrary finding of a negative relationship between the systematic risk of common stocks and leverage, with respect to prior research, suggested the use and analysis of a different leverage ratio to represent the leverage dimension of KSE-100 Index firms. And second, multiple coefficient of determination (.239) of regression model was not very high, which suggested that additional determinants of systematic risk be analyzed.
5.4 Conclusion
The study established an empirical link between systematic risk and leverage, liquidity, profitability and dividend payout variables. Both similarities and dissimilarities were indicated by the results in context with the previous research findings. Significant negative impact of total asset turnover ratio and dividend payout ratio were confirmed on the systematic risk of KSE-100 Index firms. All the regression coefficients were in the anticipated direction, except that of the leverage variable. The study suggested further research with respect to several areas; first, to use and analyze a different leverage ratio to investigate the negative influence of leverage on systematic risk, and second, to analyze additional determinants of systematic risk for improving the overall level of explanation.
