Portfolio Analysis on the Earnings, Debt, liquidity and Profitability of Five Industries in Malaysia Stock Market

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1 Portfolio Analysis on the Earnings, Debt, liquidity and Profitability of Five Industries in Malaysia Stock Market Cheng Fan Fah, Annuar Nasir & Cheng Seow Voon Accounting and Finance, University Putra Malaysia, Accounting and Management Department, TAR University College, Penang Abstract This paper aims at determining the relationship between earnings per share, debt to equity ratio, current ratio and return on asset with the share prices in five sectors in a mature developing market. A sample of public listed companies in Bursa Malaysia was chosen for 12 years period covered in a range from year 2001 to The results showed that the relationship between accounting earning, measured using earnings per share, with abnormal returns of stock is significant. However, the cumulative abnormal returns have weak relationship with debt, liquidity and return on asset at portfolio level. It suggests that investor can predict the values of share price based on the changes in earning per share mainly and weakly on other financial ratios. The financial ratios cannot be fully used as a tool to forecast the volatility of share price in various sector as compare to earnings per share. The plantation sectors seen to have significant coefficients for all the variables, this may be due to the sensitivity of plantation stocks to external factors, like interest rate, economic growth, etc. Whereas the property and service sectors only indicate the earnings are significantly affect share prices, this may be due to the fact that property and service sector are more localize in Malaysia context. The portfolio grouping methodology enhances the significant of the results and it strengthens the analysis by indicating the significant of the coefficients and the robustness of the relationships. Keywords: Earnings response coefficients, industry sectors, debt equity ratios, liquidity ratios, return on assets Introduction Accounting earnings are the amount of money that a company earned during the accounting period basically quarterly or yearly, as reported based on popper accounting standards like Generally Accepted Accounting Principles (GAAP). The information about the accounting earnings is very important especially to investors because its help to measure a company s profitability and also company s performance. In order to invest in a particular company, the investors should consider the earnings quality, not only earnings quantity in evaluating the company s accounting earnings. As the previous studies, this study also has variables which indicate anything that varies or changes from one instance to another; variables can exhibit differences in value, usually in magnitude or strength, or in a direction. The dependent variable is a process outcome or a variable that is predicted and/or explained by other variables, while independent variable is a variable that is expected to 1

2 influence the dependent variable in some way. In this study, accounting earnings, liquidity ratio and debt/equity ratio are the independent variables at the same time as the share price is the dependent variable. Earnings per share are one of the extensively used measures that are calculated from data associated with its stock market performance. Definitely, the earnings per share have to be reported in the statement of comprehensive income of publicly traded firms. The ratio indicates how much the firm has earned per share of stock outstanding despite of how much the individual stockholder has paid per share for the rights over that annual earnings and also how many assets a firm used to generate those earnings. Financial ratios are variables derived from data disclosed in financial statements, and are widely used by investors to evaluate corporate financial performance. Financial ratios play a significant role in terms of financial analysis in order to describe the state of health of an ongoing firm for a given point in time as well as to make conclusion about changes in a particular firm s structure over time. The most important ratios that will be analyzed in this study are profitability ratio (return on assets), liquidity ratio (current ratio) and debt to equity ratio. Other than that, financial ratios tend to be most meaningful when the ratios are used to compare amongst organizations within the same field of industry. Profitability ratio used to compare components of income with total assets. This ratio used to measure management s overall effectiveness as shown by the returns generated on sales and investment as compared to its expenses and any cost incurred during the period. Return on Assets (ROA) can be referred as an indicator that how profitable of the firm is relative to its total assets. ROA is to measure how efficient the management in using firm s assets in order to generate earnings. Liquidity ratio such as current ratio designates the firm s ability to meet its short-term debt obligation using assets that are most readily converted into cash and then the liquidity measures are believed to be of prime interest to short-term lenders such as merchandise suppliers and bank. According to Morris and Shin (2016), the liquidity ratios can be defined as the realizable cash on the statement of financial position to short-term liabilities. In sequence, realizable cash can be defined as liquid assets as well as other assets to which a haircut to be applied. In addition, based on the International Accounting Standards (IFRS, 2006), it designates the fact that liquidity refers to the available cash in the near future, after taking into account the financial obligations equivalent to that period. This is important for any organization to meet their short-term obligations in order to survive in the industry that they involved. Besides, the firm s financial health can be measured by analyzing the liquidity of the firm. Basically, the higher value of liquidity ratio indicates that the larger margin of safety that the firm possesses to cover short-term obligations. However, if the value is too high, it shows that the firm is not using its asset efficiently. Liquidity ratio is important for bankruptcy analyst and mortgage originators because they frequently use this ratio to determine whether a firm will be able to continue as a going concern or not. A company can finance its assets either with equity or debt. Financing through debt involves risk because debt legally obligates the company to pay interest and to pay the principal as promised. Equity financing does not obligate the company to pay anything. However, most companies are financed by some combination of debt and equity. The right capital structure will depend on tax policy where high corporate rates favor rate will lead to higher personal tax rates favor equity. In addition, the capital structure also depends on bankruptcy costs and overall corporate risk. Specifically, if the companies are concerned about the possibilities of bankruptcy, thus the long-term leverage of the companies may be important. If the value of debt to equity ratio is high, it indicates that the firm uses 2

3 long-term debts to finance its assets instead of equity. For investors point of view, the higher the value of debt/equity ratio, the more risky it is and then it will lead to higher return. Regularly, analysts look at the debt to equity ratio to determine the company s ability to generate new funds from the capital market. However, a company with considerable debt is commonly thought to have little new financing capacity due to the overall financing capacity of a company that requires as much to do with the quality of the new product as the organization wishes to pursue as with its financial structure. Yet, when a company has a very high debt to equity ratio, it will tend to make the company s future financing difficult because of the given threat of bankruptcy and attendant costs. Therefore, this study attempt to link all these variables with share price using an up dated portfolio grouping methods. By using the portfolio grouping, this study hope to obtain stronger significant results than most other previous study that show only weakly association or not at all between these variables and share price reaction. With the new findings, we hope it will add knowledge to the price formation of shares. 2.0 Literature Review Beaver (1989:90) suggests that a conceptual relationship can be developed between accounting earnings and share prices by introducing three critical links: (a) a link between share price and future dividends, (b) a link between future dividends and future earnings, and (c) a link between future earnings and current earnings. Beaver, Clarke and Wright (1979) did further research and tested the hypothesis that a positive ordinal association exists between unsystematic returns and magnitude of earnings forecast errors. Subsequently, some prior studies by Easton et. al. (1989), Deschow (1994), Ball et. al. (1996) has extended these areas of study name as earnings response study with difference methodology and the added difference variables to check the strength and robustness of the results. Easton et. al. (1989) measures the response of stock prices to accounting earnings announcements by using random coefficient regression model. The theory of this study is that earnings response coefficients (ERC) are positively associated with revision coefficients (coefficients relating current earnings to future earnings) and negatively associated with expected rates of return. The result obtained is complying with the theory which indicates that there is a positive association between the ERC and revision coefficient, a positive association between the ERC and firm size, and a negative association between the ERC and systematic risk. Dechow (1994) studies the examinations of the circumstances under which accruals are predicted to improve the ability of earnings in turn to measure firm performance, as reflected in stock returns. An extensive range of users used the earnings as a summary measure of firm performance. The accounting principles which are revenue recognition and matching principle are important in order to guide the production of earnings. Since the firm has performed all or substantial portions of services to be provided and cash receipt is reasonably certain, so the revenues will be recognized due to the revenue recognition principle. A demand for a summary measure of firm performance can be created by the existence of symmetries information between the managers and outside parties contracting with the firm. Thus, the management can be appraised by using that measure, and also as a source of information to the creditors and investors on the firm s cash generating ability. Ball, et. al. (1996) study the differences in the demand for accounting income in different institutional contexts cause its properties to vary internationally. The model of incorporation of economic income in accounting income has been exercised. This study theorizes that the code-law countries accounting incomes are less timely in incorporating current-period changes in market value and more smoothed than common-law countries and also the common-law accounting income is 3

4 more asymmetrically conservative than code-law. The results designate the varying demands that reported income satisfy under different institutional arrangements because of the function of properties of accounting income around the world where the common-law accounting income does indeed exhibit significantly greater timeliness than code-law accounting income, but that this is due entirely to greater sensitivity to economic losses (income conservatism). The results are consistent with the hypothesis created earlier with the evidence given. In 2001, the first ever comprehensive work on the Earnings Response Coefficients (ERC) for a mature developing market was done by Cheng, Ariff, and Shamsher (2001). They address the issue of the effect on contemporaneous accounting earnings announcements on share price in an institutionally mature developing market. They find that, while a significant price-to-earnings relation is evident, the strength, consistency, and the magnitude of the relation are not as pronounced as reported in institutionally more developed markets. In conclusion, stock price change ordinally in a statistically significant manner in response to earnings increases and decreases, and the effect is more pronounced over the long window and not in the short window. Price effect from accounting earnings is much more direct in the emerging market, which is also more speculative and less constrained by differences in firm-related variables. Moreover, investors react to unexpected earnings only. Their study sort of confirms the undisputable facts that earnings affect share prices even in developing markets. So the new question now are 1) Is there any other factors that affect share prices? 2) Is there industry effect on the ERC? The finance sector is the most regulated and is always single out for research. Cheng and Ariff (2007) used factor analysis to generate the banks risks and regressed against the earnings response coefficients equations. The results show that other than earnings, risks factors also affect the share price of Malaysia local banks. Ariff and Cheng (2011) extended to multi countries study report new finding on earnings response coefficients for banking firms on how disclosures on total earnings and disaggregated fee earnings are used by investors to change share prices prior to earnings disclosures. The information relating to total earnings influences share prices significantly in all four banking sectors studied, all of which have sufficiently liberalized capital markets. Australian investors appear to use information on disaggregated non-interest fee income to revise share prices significantly: not so in other markets. The investors in Malaysia and South Korea appear to consider changes in fee income as bad news with negative price impact, anomalous to theory. The Australian investors appear to regard both total and fee incomes as equally important whereas investors in other markets either ignore or consider changes in fee income as bad news for share valuation. Then, Ariff, Cheng and Soh (2013) investigate two issues: Do share prices of banks in European markets respond to unexpected accounting earnings disclosures? Are share prices as well as unexpected earnings changes correlated with bank-relevant risk factors? Results reveal that bank share prices respond to unexpected earnings changes at the time of accounting reports in the same manner as the shares of the more widely-researched non-bank firms. Apart from finding significant earnings response coefficients in eight countries, we find that credit risk, price risk, exchange rate risk, and solvency risk are significantly correlated with share price changes. Third, three bank risk factors are significantly correlated with unexpected earnings changes. These results are obtained after corrections for several statistical and econometric problems so our reported parameters are robust, certainly more so than in earlier studies using ordinary least square regressions. These new findings extend earnings response literature to several banking sectors, and also identify bank's key risk factors. The latest study by Cheng, Annuar and Cheng (2016) determine the share price responds to earnings, cashflow, liquidity, debt to equity and profitability of five major sectors i.e. industry, plantation, property, services, and consumer stocks listed in Malaysia stock market at company levels. 4

5 This paper extends all these study using portfolio grouping methods in hope of obtaining a significant and strong results Methodology and Research Design The standard events study method is applied to identify the direction and magnitude of the stock price revaluation effect of the changes on accounting earnings, liquidity ratio, profitability ratio and debt/equity ratio. Sharper s (1963) market model was used as a standard equilibrium model to estimate abnormal returns (AR): AR n = R n [ α 1 + β 1 R mt] (1) With R n = Ln (P it/p i t-1) and R mt = L n (I t / I t-1). Where, in addition to the terms already defined, Ln is the natural logarithm and I refer to the market composite index. The market parameters α1 and β1 are estimated by ordinary least square regression over trading periods, -60 months to -3 months (estimation period) relative to the announcement month. The returns were adjusted for thin trading bias using Fowler-Rocker s method. The resulting risk-adjusted abnormal returns of each observation is added and averaged across all the observation as to obtain the AARt as the simple arithmetic average. Next the average returns over t = 1,.., T is cumulated as: CAR= (T=1) ^T AARt*100 (2) The cumulating is done over a price reaction window consistent with other studies in percentage and tested for statistical significance. Analysis of Unexpected Accounting Earnings Unexpected earnings are computed using the naive expectation model (Cheng et. al. 2001, Cheng et. al. 2016) that assumes the next period s expectation is simply the current period s earnings. The accounting earnings are defined as follows: EPS = (EASH - PREFDIV - MINOR)/NoEQ (3) Where, EASH : earnings attributable to shareholder, PREDIV : preferred dividends, NoEQ : number of shares measured as average outstanding, MINOR : minority interest Unexpected earnings (UEs) are computed using the naïve model: UEit = [Eit Ei(t-1)] / [E i (i-1)] (4) Firm specific variables. Given that the price of stock price is determined not solely by accounting earnings but also by other sources of information about ratios, this study looks at the relation between accounting earnings and other information to control the effect of left-out variables in the return-to-earnings association. Four variables are identified, which are growth in revenue (Swaminathan and Weintrop 1991), liquidity ratio, profitability ratio and debt-equity ratio (Dhaliwal, Lee and farger 1991, Ball Kothari and Watts 1993). This study test the relationship between accounting earnings, liquidity ratio, profitability ratio and debt/equity ratio and share price in property sector by using the following ratios: 5

6 Liquidity Ratio: Current Ratio=(Current Assets)/(Current Liabilities) Profitability Ratio: Return on Assets (ROA)=(Net Income)/(Net Assets) Debt/equity Ratio: Debt to Equity Ratio=(Long Term Debt)/(Shareholder Equity) All these ratios can be added in to the following formula: CAR = a 1 + a 2 ΔEPS + a 3 ΔCR + a 4 ΔROA + a 5 ΔDE (5) Where, CAR: cumulative abnormal returns in percentage over a specified window, ΔEPS: changes in earnings per share, ΔCR: changes in current ratio (current assets divided by current liabilities), ΔROA: changes in return on assets (net income divided by total assets), ΔDE: changes in debt-equity ratio (sum of short-term loans and long-term divided by shareholders fund) loans Grouping Stocks into Portfolios Grouping the data according to unexpected earnings or abnormal returns helps to reduce the effect of the disturbance term which is large in individual stocks. This leads to an increase in the estimated correlation at group level. Grouping is one approach that has been used to reduce the errorsin-variables problem: (Cheng et. al. (2001). The portfolios were formed first by ranking all stocks according to the magnitude of unexpected earnings (UEit)/or cumulative abnormal returns (CARit) starting from year 2001 to To form 20 portfolios, the four percent of the stocks with the highest rank on the ranking variables (earnings or returns) will be placed in the first portfolio. The next highest four percent in the second portfolio, and so forth until the twenty fifth portfolio contains those four percent of the observation with the lowest unexpected earnings/or abnormal returns. The mean UEit and CARit will be selected as the portfolio unexpected earnings and abnormal returns respectively. This procedure will result in 20 observations at portfolio level for 2002 to 2013 on which a regression will be estimated. This process will be repeated for any other type of portfolio formation over the test period. The above procedure extends one aspect of the Ball and Brown (1968). Thus this design facilitates testing the hypothesis that a positive ordinal association exists between abnormal returns and unexpected earnings, not just a sign relation exists. 3.1 Hypotheses The major hypothesis of this study is that a direct relation in sign as well as magnitude exists between risk-adjusted abnormal returns, which represent changes in adjusted share price in property sector and changes in accounting earnings, liquidity ratio, profitability ratio and debt/equity ratio. The null will be accepted if there is no significant relation between changes in share price in all sector and changes in accounting earnings, liquidity ratio, profitability ratio and debt/equity ratio. If there is a significant relation between changes in share price in the property sector and changes in accounting earnings, liquidity ratio, profitability ratio and debt/equity ratio, we expect the null to be rejected in favor the findings in the changes in earnings per share, liquidity ratio, profitability ratio and debt/equity ratio. 3.2 Data 6

7 The data set relating to the period 2002 to 2013 came from the monthly closing prices and earnings information by using data stream and capital IQ. Data stream software is used to get data about the share price, while capital IQ used to get data about the companies earnings. Other than that, this study also refers to the latest accounting standards which are Malaysian Accounting Standards Board (MASB), Financial Accounting Standards Board (FASB), International Accounting Standards Board (IASB) and International Financial Reporting Standards (IFRS). The sample consists of listed and traded companies over the test period and covers all sectors in the market. The companies are subjected to the following selection criteria: the companies are Malaysian-domiciled and not foreign companies; the annual reports containing accounting statements are publicly available; and the selected observation does not have any other confounding information released during the test period. Monthly closing prices of the selected stocks traded during January 2002 to December 2013 were extracted. Various companies were selected for this analysis and consisting of at least 150 plus earnings announcement per sectors were analyzed. 4.0 Results Table 4.1 shows the regression result between CAR, ΔEPS, ΔD/E and ΔCR at Portfolio Level. The regression results are from linear ordinary least square regression at 20 portfolio level. The dependent variables are represented by CAR. The independent variables are represented by changes in earning per share and financial ratio by their respective annual standard deviation. The sample consists of 20 portfolio formed from observations for return window. Table 4.1: Regression Results between CAR, EPS, Debt to Equity Ratio, Current Ratio and Return on Asset at Portfolio Level. CARit = β0 + β1δeps + β2δd/e + β3δcr+ eit Service Model Consumer Industry Plantation Property Model Model Model Model Constant (-4.02***) (-0.991) (0.064) (1.2) (-0.605) ΔEPS (4.41***) (2.326**) 3.265** (1.62*) (1.570*) ΔDE (0.18) (2.306**) (0.009) (0.29) (0.330) ΔCR (-0.18) (0.311) (0.239) (2.19*) (0.041) ΔROA (0.38) (0.890) (4.025***) (7.59***) (1.198) Adj-R² F-test 6.48** 4.514** * *** 2.689* Note: * = significant at 0.1 level, ** = sig. at 0.01 level, *** = sig. at level Service sector 7

8 At the portfolio level, the R-squared value in portfolio analysis is 46 percent for regression service model. This is significantly higher than the results obtained in Cheng et.al. (2001) and Cheng et.al. (2016). If the changes in earning per share were the only factor inducing price changes, the correlation would be perfect and the value would be closer to one. The magnitude of the coefficients increases tremendously due to portfolio formation. This confirms the diversification of the errors in the earnings variables Consumer sector Table 4.1, regression (Consumer Model) result shows that the coefficient for EPS is with t-statistics of and significant at 0.01 levels. Coefficient of D/E ratio and CR ratio are (t= **) and (t=-0.321), respectively, the CR ratio is not significant at any acceptable level. In conclusion, it is suggested that changes in stock prices are determined by the sign and the magnitude of the unexpected earnings changes and the D/E ratios only but not by other financial ratios on consumer sector. In portfolio tests, earning per share are definitely more superior in valuing share prices in the long term. However, financial ratio is less significant than earning per share in explaining abnormal return Industry sector Table 4.1 Industry Model shows the regression Results between CAR, EPS, Debt to Equity Ratio, Current Ratio and Return on Asset at Portfolio Level. The regression results are from linear ordinary least square regression at 20 portfolios. The dependent variable is represented by cumulative abnormal return. The independent variables are represented by four other variables which are change in earning per share, change in debt to equity ratio, change in current ratio as well as change in return on asset. The sample consists of 20 portfolios formed from 172 observations for return window. From the regression Industry Model which is using multiple regressions show that there are no significant variables, except EPS and ROA. Again, these findings confirm that the role of earnings in share valuation. The R-squared for regression is percent, meaning that, the earnings per share and return on asset explained percent of the abnormal return 4.4. Plantation sector Table 4.1 plantation model shows the regression results at a portfolio level. Grouping the observations using the dependent variable which is the cumulative abnormal return led to portfolios. The lowest four percent of the cumulative abnormal returns formed the first portfolio, and the next subsequent group formed the next portfolio and so on. After dividing from 175 observations, there are 20 groups of portfolio that has been formed The regression plantation Model shows the results of multiple regressions between CAR with ΔEPS, ΔD/E, ΔCR and ΔROA in portfolio level. The coefficient for Δ EPS is 0.42 with t-statistics of 1.62 which is weakly significantly different from zero. The coefficient for Δ D/E is with the value of t-statistics are 0.29 and again the value of t-statistics is not significantly different from zero. The coefficient for Δ CR and ΔROA are 0.39 and 0.45 with the t-statistics are 2.19 and 7.59 respectively. Both of the values are also weakly significant. 8

9 4.5. Property sector Table 4.1 property model shows the results of regression at the portfolio level. Regression of the property model which is multiple regressions of EPS, CR, ROA and DE shows that, except for EPS, all the coefficients are not significantly different from zero at any level of acceptance. The coefficient for EPS is (t=1.570), which is weakly significant. The R-squared value for regression is 26.2 percent which is the lowest compared to the other R-squared values, obtain the these regression. The regression results lead to the acceptance of null hypotheses excluding hypothesis regarding the accounting earnings since there is a significant relation between stock price changes and accounting earnings changes. Otherwise, the other null hypothesis regarding current ratio, return on assets and debt to equity ratio are rejected as there are no significant relation between stock price changes and the explanatory variables changes. 5.0 Summary of the results This section explains the findings that had been obtained during the analysis period. Even though only accounting earnings or earnings per share (EPS) seems to be significantly affecting the abnormal returns for portfolio level compared to the other variables, but it the other three variables some time presented with significant results when simple regression are perform. Table 5: Summary of Simple and Multiple Regression Results for the five sectors at Portfolio Service Consumer Industry Plantation Property ΔEPS *** *** *** *** *** ΔDE ** ΔCR ** ΔROA *** ** Adj-R² F-test 6.48** 4.514** 3.44** *** 2.689* Note: * = significant at 0.1 level, ** = sig. at 0.01 level, *** = sig. at level. Table 5.0 shows the summary significant coefficients. The plantation sectors seen to have significant coefficients for all the variables, this may be due to the sensitivity of plantation stocks to external factors, like interest rate, economic growth, etc. Whereas the property and service sectors only indicate the earnings are significantly affect share prices, this may be due to the fact that property and service sector are more localize in Malaysia context. These two sectors are only affected by the local demand and supply. In fact Malaysia property and service sectored are highly regulated by the local government. The plantation sector shows the highest adj-r square of more than 70%, and then property sector adjusted R-square only at 26%. The high adjusted R- square in these regressions as compare to general individual company sample regression in most other studies and in particularly low adjusted R-square in Cheng et. al. (2001) and Cheng et. al (2016) is that this paper adopted the portfolio grouping methodology. The 9

10 portfolio grouping methodology enhances the significant of the results and it strengthens the analysis by indicating the significant of the coefficients and the robustness of the relationship. Acknowledgement: the authors wish to thanks a group of assistance researchers in University Putra Malaysia for their contribution to this paper. There are too many of them that the authors will not name them one by one. However, specially thanks to Halimatussa. All errors belong to the authors. References Ariff, M., & Cheng, F. F. (2011). Accounting earnings response coefficient: An extension to banking shares in Asia Pacific countries. Advances in accounting, 27(2), Ariff, M., Cheng, F. F., & Ni, S. W. (2013). Earnings response coefficients of OECD banks: Tests extended to include bank risk factors. Advances in Accounting, 29(1), Ball, Ray, S. P. Kothari, and Ashok Robin. (2000) "The effect of international institutional factors on properties of accounting earnings." Journal of accounting and economics 29.1 (2000): Beaver, W.H. (1989). Financial Reporting, An Accounting Revolution. (2 nd edition.). Eaglewood Cliffs, NJ, Prentice-Hall. Cheng, F. F., & Ariff, M. (2007). Abnormal Returns of Bank Stocks and Their Factor-Analyzed Determinants. Journal of Accounting, Business & Management, Cheng, F. F., Ariff, M., & Shamsher, M. (2001). Accounting earnings and share revaluation: Further exploration. Capital Market Review, 9(2), Cheng, F. F., Shamsher, M. and Annuar Md Nassir, (2013). Banks Earnings, Risks and Returns in the US. Pertanika Journal of Social Sciences and Humanities. Vol. 21 (S) Oct , Cheng, F. F., Wong Wai Choon and Annuar Nasir (2016) Effects of interest rate, exchange rate, inflation and share price on REIT market in Malaysia and Singapore. 5 th International Conference on Business and Information, Nagoya, Japan, 2-5 th July 2015, Cheng, F. F., Annuar and Cheng (2016). Relationship Between Accounting Earnings, Cash flow, liquidity, Debt to equity, Profitability Ratios and Share Prices in Five Major Sectors in Malaysian Stock Market. Submitted paper to World Finance Conference July, 26-28, 2017 SARDINIA, ITALY. Dechow, Patricia M. (1994) "Accounting earnings and cash flows as measures of firm performance: The role of accounting accruals." Journal of accounting and economics 18.1 (1994): Doukas, John A., Jie Michael Guo, and Bilei Zhou.(2011) " Hot debt markets and capital structure." European Financial Management 17.1 (2011): Easton, Peter D., and Mark E. Zmijewski.(1989) "Cross-sectional variation in the stock market response to accounting earnings announcements." Journal of Accounting and economics (1989): Morris, Stephen, and Hyun Song Shin. (2016) "Illiquidity component of credit risk." Princeton University William S. Dietrich II Economic Theory Center Research Paper 081_2016 (2016). Saleem, Qasim, Ramiz Ur Rahman, and Naheed Sultana. (2013) "Leverage (Financial and Operating) Impact on profitability of oil and gas sector of SAARC Countries." American Based Research Journal 1 (2013): 3. Sharpe, W.F. (1964). Capital Asset Prices, A Theory of Market Equilibrium Under Conditions of Risk. Journal of Finance, Vol. XLX, (3),