# Introduction inancial leverage involves changes in shareholders' income in response to changes in operating profits, resulting from financing a company's assets with debt or preferred stock. If a company is financed with debt or is 'leveraged,' however, its shareholder earnings will become more sensitive to changes in operating profit. Nevertheless, financial leveraging makes companies equally susceptible to greater decreases in stockholder earnings if operating profits drop. Financial leverage increases the chance or probability of insolvency. Due to insolvency a levered firm can legally be forced into liquidation for non-payment of interest charges. Leverage has both benefits and costs and it is not an unmixed blessing. As a company increases debt and preferred equities, interest payments increase, reducing EPS if return on investment does not cover cost of debt. As a result, risk to stockholder return is increased and they demand a higher expected return for assuming this additional risk, which in turn, raises a company's costs. # II. Statement of the Problem Modigliani and Miller (1963) argued that the capital structure of a firm should compose entirely of Author: e-mail: kr15sust@gmail.com debt due to tax deductions on interest payments. However, in theory, the Modigliani-Miller (MM) model is valid but, in practice, bankruptcy costs exist and these costs are directly proportional to the debt level of the firm. Hence, an increase in debt level causes an increase in bankruptcy costs which affect the financial performance of a firm. Therefore an optimal capital structure can only be attained if the tax sheltering benefits provided by increase of debt level is equal to the bankruptcy costs. In this case, managers of the firms should be able to identify when this optimal capital structure is attained and try to maintain it at the same level. This is the only way that the financing costs and the weighted average cost of capital are minimized which leads to increase of firm value and corporate performance. Schall and Haley (1991) stated that some of the complications found in practice provide advantages to debt financing whereas other factors favor equity financing. They found three types of complications-firstly capital markets are imperfect. There are information asymmetries and transaction costs which imply that there may be situations where debt or preferred stock financing may be unusually costly relative to common stock and vice versa. Secondly there are legal fees, investment banking commissions and other expenses associated with issuing securities. Issuing equity is usually more expensive than issuing preferred stock and issuing debt is less expensive than to issue preferred stock. Thirdly use of debt financing often results in serious disruption of the firm's business activity as top management spends time in negotiations with lenders while lower management starts thinking about alternative jobs. It is described as follows: Customers for the firm's products and services began to search for other suppliers. The firm may be forced to delay or forego profitable investments due to lack of finance. There are also legal and other expenses associated with the legal proceedings in bankruptcy situations. At some point the expected costs of default become large enough to offset the advantages of debt. Firms with large amount of outstanding debt may have other problems. Lenders are reluctant to lend additional money to firms that are highly levered and they may either not lend money or charge a very high interest rate to compensate for their exposure to risk. The general opinion is that, beyond some point, additional leverage is undesirable. # III. Literature Review Allen (1983) states that financial risk is the risk which arises solely from the company's financial structure. The 'gearing up' or increasing the proportion of fixed interest securities is regarded as increasing the company's financial risk. According to Gitman (2007), "Financial risk can be defined as the chance that the firm will be unable to cover its financial obligations. Level is driven by the predictability of the firm's operating cash flows and its fixed cost financial obligations." Brigham and Houston (2001) stated that financial risk is the additional risk placed on the common stockholders as a result of the decision to finance with debt. If a firm uses debt or financial leverage, this concentrates the business risk on common stockholders. Schall and Haley (1991) explained financial leverage as the changes of shareholders income to changes in Earnings Before Interest and Taxes and is formed by debt or preferred stock financing with fixed interest and dividend payments. According to trading on equity, financial leverage enhances EPS which increases market price of common stock. However, the use of higher debt can lead to financial difficulties. Peirson and Bird (1981), noted that financial risk is that part of a company's risk that is introduced as a result of debt financing. The used of borrowed fund by a company exposes its ordinary shareholders to the possibility of increased variability in their earnings stream and the firm to the increased possibility of bankruptcy. This results from the contractual nature of the interest payments and principal repayments on the borrowed funds. Thus a firm's financial risk is directly related to the proportion of debt. Hussan (2016) has investigated on impact of leverage on risk of the companies. He explored that the leverage enhances the financial risk of the firm which indicates recovery of loss in terms of loan is very difficult to the firm because in general there are limited sources of alternative funding and business insurance policy is not popular in Bangladesh. It also found that high interest rate and unethical political influence negatively manipulate the profitability of the firm. Akbari and Mohammadi (2013) have investigated the effects of leverages ratio on systematic risk based on the CAPM in Tehran Stock Market. The aim of the study was to determine if there is any significant relationship between leverages ratio as independent variables and beta as dependent variables. The results of the study revealed that there is not significant relationship between the variables. Bhatt and Sultan (2012) in their study found that the leverage risk factor performs consistently across various categories of firms and its impact is more pronounced during the recent financial crisis. Effects of leverage risk are robust to heterogeneity of the firms in the sample. The contribution of leverage risk to asset pricing has been quite strong. The results indicate that leverage based risk factor can explain a substantial portion of the cross-section of stock returns. Gunarathna (2016) in his study examined how financial leverage affects financial risk based on the data collected over ten years ranging from 2006 to 2015 regarding 15 companies listed in the Colombo Stock Exchange. The findings revealed that financial leverage positively correlate with financial risk. The findings imply that firms having a higher financial risk can avoid their risk by altering the capital structure. Ufo (2015) has conducted a study to examine the relationship between leverage and manufacturing firms' financial distress in Ethiopia from 1999-2005. The result showed that leverage has negative and significant influence on financial distress. Minimize the bank loans through equity financing, improving cash collection and reducing bad debt expenses are remedy for maintaining short term cash problem. IV. # Objective of the Study The main objective of the study was to explore the impact of debt financing on financial leverage risk of firms. Specific objectives are: a. To find out the three financial leverage ratios of sample firms. b. To explore the financial leverage risk of sample firms based on coefficient of variation (CV) and mean absolute deviation. c. To analyze the significance of regression coefficients of leverage ratios and make a comparison between MNCs and domestic companies. V. methodology of the study # Results and Discussion a) Analyzing Impact of Leverage on Financial Risk By FLR Models In analyzing effect of leverage on financial risk, 2 ratios of FLR (CV and MAD) are considered explained or dependent variables and 3 financial leverage ratios are used as explanatory or independent variables. As EBIT and EPS are directly related with FLR so these variables are considered as independent variables. Debt financing depends on sales growth because higher sales growth ultimately results in higher internal financing which reduces the necessity of debt financing and vice-versa. The same matter also applies to net profit margin. Financial structure depends on firm size also because cost of borrowed fund depends on assets of the firm. So, sales growth, net profit margin and firm size are used as explanatory or independent variables in the model. The model is as follows: Second difference of leverage ratios are positively related with 2 nd difference of FLR (MAD). If 2 nd difference of TD/SE and TD/TA is changed by 1 or 100% then 2 nd difference of FLR (MAD) would change by 0.004 and 0.315 respectively or in other words, 1% increase of 2 nd difference of TD/SE and TD/TA results in 0.00004 and 0.0031 increases in 2 nd difference of FLR (MAD) and vice-versa. FLR (Financial Leverage Risk) = ? 0 + ? 1 TD/TA + ? 2 TD/SE + ? 3 TD/CE t + ? 4 SG + ? 5 FS t + ? 6 EBIT + ? 7 EPS+ ? 8 NPM + ? i,t Where: ? 0 = Constant term, ? 1 to ? 8 = Coefficients of variables, ? i,t = # f. Overall Fitness of the Models In table A10 it is seen that p-value of F statistic is less than 0.05 in model D1, M1, M2 and it is less than 0.10 in model D2. So, it can be said that there is a statistically significant relationship between the variables at the 95.0% confidence level in models D1, M1, M2 and at 90% confidence level in model D2. Independent variables of Model D1 explain 72.77% variability in dependent variables. The R-Squared statistic indicates that the models D2 (FLR-MAD) as fitted explains 67.02% of the variability in explained variables. Independent variables of models M1 and M2 explain more than 90% variability in dependent variables. # b) Test of Hypothesis Null hypothesis is as follows: Financial leverage does not significantly influence firm's financial risk This hypothesis is tested by analyzing the coefficients of financial leverage ratios of two FLR models discussed above. Acceptance or rejection of null hypothesis depends on p value of coefficients. The following table shows hypothesis test of domestic companies and MNCs. From the table it is seen that domestic companies' debt-equity ratio has significant impact on FLR (CV) whereas MNCs' debt ratio has significant impact on both the measures of FLR at 95% confidence level. MNCs' FLRs are more sensitive to changes in leverage ratios than domestic companies as leverage coefficients of MNCs are higher than domestic companies in both the models. # VII. Recommendations and Conclusion It is expected that the process of liability management will become far more sophisticated in the coming decade as companies increasingly recognize the connections between balance-sheet decisions and firm performance. In fact, the more the debts rise, the higher the risk of financial distress will be. The financial manager has to take into consideration the effect on the capital structure when any financing decision is evaluated. Once a financial need arises from the planning activity, the financial manager should simulate what impact a debt or equity issue may have on the overall company. # Acknowledgement My first and foremost thanks as well as all praise go to almighty Allah, who has given me the opportunity to be educated through acquiring knowledge. The present study is supported and organized by my research supervisor Dr. Md. Meherul Islam Khan, Professor, Department of Finance, Rajshahi University, Rajshahi. Deep sense of gratitude and profound respect are extended to the noted director of the Institute of Bangladesh Studies (IBS) as well as other faculty members. I would like to express my sincere appreciation to executives of finance and accounts section of sample multinational and domestic companies who provided necessary information for this study. Special thanks go to authority of Dhaka Stock Exchange Ltd. for providing invaluable secondary data regarding the sample companies. Sample Size & Sample Items: The sample in thisstudy consists of 14 companies (7 from eachpopulation) listed in Dhaka Stock Exchange (DSE).Two companies are selected from Pharmaceuticals &Chemicals industry and one company is selected fromdomestic companies. F statistic and coefficient ofdetermination or r 2 value was used to measure overallgoodness to fit of the models. Normality test has beendone by Kolmogorov-Smirnov, Shapiro-Wilk and chi-square test. Data stationary has been judged byAugmented Dickey Fuller (ADF) test. Variance InflationFactor (VIF) has been used to test multicollinearityamong variables. Autocorrelation has been judged by Durbin-Watson (DW) statistic and Breusch-Godfrey test (also called LM test). Breusch-Pagan test has been used to judge heteroscedasticity in residuals.Population two consists of all DSE listed domestic companies of the same 6 industrial sectors and which continue operations during the study period. Population size is 45.VI.Type of Research: Type of research is explanatory or causal. An attempt was made to identify cause and effect relationship between financial leverage and financial risk. Nature of research is Empirical and research approach is Quantitative. Population: Population one consists of all MNCs listed on DSE which continue operation during the study period. Eight MNCs are found in 6 industrial sectors. Engineering, Food & Allied, Tannery, Cement and Fuel & Power industry in each category. Name of the domestic companies are: Aftab Automobiles Ltd., Agricultural Marketing Company Ltd., Beximco Pharmaceuticals Ltd., Square Pharmaceuticals Ltd., Apex Footwear Ltd., Confidence Cement Ltd., and Padma Oil Company Ltd.Name of the MNCs are: Singer Bangladesh Ltd., British American Tobacco Bangladesh Company Ltd., GlaxoSmithKline Bangladesh Ltd., Reckitt Benckiser (Bangladesh) Ltd., Bata Shoe Company Ltd., Heidelberg Cement Bangladesh Ltd., and Linde Bangladesh Ltd.Techniques of Data Analysis: Mean is used to determine yearly average and grand average. Collected data has been processed by MS Excel, SPSS and Gretl software. Presentation of data is done in two forms; text and tabular. Multiple regressions have been used to explore independent variables' degree of influence and direction of relationship with dependent variable. Ordinary Least Square (OLS) method has been applied to estimate the coefficients of financial risk models of MNCs and 12nd difference of variablesUnstandardized Coefficients B Std. ErrorStandardized Coefficients BetatSig.(Constant)-.001.030-.043.967TD/TA.558.800.164.698.503TD/SE.119.046.6002.605.029**NPM-.043.011-.721-3.887.004***SG-.002.002-.196-.887.398FS.218.391.133.557.591EPS.009.015.126.563.587EBIT.0003.001-.136-.647.534Note: Data processed on SPSS **Significant at 5%, ***Significant at 1%The equation of the fitted model is:dd_FLR(CV) = -0.001 + 0.558*dd_TD/TA +0.119*dd_TD/SE -0.043*dd_NPM -0.002 *dd_SG + 0.218*dd_FS + 0.009*dd_EPS -0.0003*dd_EBIT (dd_variable = 2 nd difference of variable)b. Model D2 (FLR-MAD) The equation of fitted model is: dd_FLR(MAD) = 0.007 -0.029*dd_NPM +0.315*dd_TD/TA + 0.004*dd_TD/SE -0.001*dd_SG +0.449*dd_FS + 0.024*dd_EPS -0.0004*dd_EBIT(dd_variable = 2 nd difference of variable)Coefficients of model D2 (FLR-MAD) is as follows: 22nd difference of variablesUnstandardized Coefficients B Std. ErrorStandardized Coefficients BetatSig.(Constant).007.023.289.779TD/TA.315.629.130.500.629TD/SE.004.036.027.106.918NPM-.029.009-.691-3.384.008***SG-.001.002-.164-.675.516FS.449.308.3841.459.178EPS.024.012.4891.979.079*EBIT.0004.000-.206-.889.397Note: Data processed on SPSS *Significant at 10%,***Significant at 1% 32nd difference of variablesUnstandardized Coefficients B Std. ErrorStandardized Coefficients BetatSig.(Constant)-.013.035-.365.723TD/TA6.5492.358.7112.777.021**TD/SE.900.426.4132.111.064*1/NPM6.0412.959.6392.042.072*1/EPS3.8963.285.3601.186.266EBIT.001.000.5602.323.045**SG.010.003.5773.015.015**FS-.2181.041-.043-.210.839Note: Data processed on SPSS **Significant at 5%, *Significant at 10%The equation of the fitted model is:TD/TA is changed by 1 or 100%, then FLR (CV) [2 nddd_FLR (CV) = -0.013 + 6.041*dd_(1/NPM) +difference] would change by 0.9 and 6.54 respectively3.896*dd_(1/EPS)+6.549*dd_TD/TA+or in other words, 1% increase of 2 nd difference of TD/SE0.90*dd_TD/SE + 0.001*dd_EBIT + 0.01*dd_SG -and TD/TA results in 0.009 and 0.065 increase in FLR0.218*dd_FS(dd_variable = 2 nd difference of(CV) [2 nd difference] respectively and vice-versa.variable)Leverage ratios are positively related withfinancial leverage risk. If 2 nd difference of TD/SE andd. Model M2 (FLR-MAD)Coefficients of model M2 (FLR-MAD) is as follows: 42 nd difference of variablesUnstandardized Coefficients B Std. ErrorStandardized Coefficients BetatSig.(Constant)-.007.035-.206.842TD/TA5.6122.347.5962.391.040**TD/SE.428.424.1921.010.3391/NPM8.9882.946.9283.051.014**1/EPS2.7413.270.247.838.424EBIT.000.000.2981.270.236SG.007.003.4332.323.045**FS-1.6581.037-.318-1.600.144Note: Data processed on SPSS **Significant at 5%The equation of the fitted model is:increase in FLR (MAD) [2 nd difference] respectively anddd_FLR (MAD) = -0.007 + 8.988*dd_(1/NPM) +vice-versa.2.741*dd_(1/EPS) 0.428*dd_TD/SE + 0.000*dd_EBIT + 0.007*dd_SG -+ 5.612*dd_TD/TA +ii. Fitness of models (Model diagnostics)1.658*dd_FSa. Test of Stationarity of DataLeverage ratios (2 nd difference) are positivelyrelated with FLR (MAD) [2 nd difference]. The debt ratiohas significant impact on FLR (MAD). If 2 nd difference ofTD/SE and TD/TA is changed by 1 or 100% then FLR(MAD) [2 nd difference] would change by 0.428 and 5.61respectively or in other words, 1% increase of TD/SEand TD/TA (2 nd difference) results in 0.004 and 0.056 5Leverage & FLRDifferenceCoefficientt statisticp valueDecision regarding H 0 hypothesisDomestic CompaniesFLR (CV) & TD/TA2 nd0.697.503AcceptedFLR (CV) & TD/SE2 nd0.1192.604.028RejectedFLR (MAD) & TD/TA2 nd0.3140.500.628AcceptedFLR (MAD) & TD/SE2 nd0.0030.106.917AcceptedMNCsFLR (CV) & TD/TA2 nd6.5492.777.021RejectedFLR (CV) & TD/SE2 nd0.9002.111.063AcceptedFLR (MAD) & TD/TA2 nd5.6132.391.040RejectedFLR (MAD) & TD/SE2 nd0.4281.010.338AcceptedSource: Outcome of Regression Models Note: Computation done on SPSS & Gretl software A1Domestic Co.MNCsYearMean EBITMean EPSFLRFLRMean EBITMean EPSFLRFLR(million Tk.)(Tk.)(CV)(MAD)(million Tk.)(Tk.)(CV)(MAD)1996137.146.380.883191.357.740.4610.5251997160.535.310.8580.932231.637.600.4860.6211998184.886.760.8340.916267.747.840.3060.3521999210.267.830.8640.842217.667.290.4220.4362000242.498.930.8810.858314.0911.760.4280.4042001284.3510.930.9160.838347.809.960.4010.5522002282.389.720.8830.916309.508.290.8880.9902003282.148.870.7710.879319.7110.240.6260.7082004322.388.740.7850.810289.228.770.5120.6092005417.689.780.7410.828245.527.720.9941.1722006454.4210.270.8450.780378.0911.560.9771.0362007518.8313.000.8210.928518.0314.970.7430.7662008621.0912.841.0501.048728.6021.210.5500.6882009872.0415.560.8491.036993.4929.850.3980.44620101093.1912.100.5920.8171480.9942.580.5820.51520111330.3512.230.6450.6261306.4829.280.3820.49820121598.4811.060.7610.6971643.0532.750.4060.47520131819.9511.050.8040.7472128.5642.390.3880.46820141908.449.630.7140.7782376.3847.090.4250.48420152224.119.080.5830.7092674.5241.800.4370.550G.Mean748.2610.000.8040.645848.1220.040.5240.607Source: Compiled from Annual Reports of Sample Firms (1996-2015) A2Domestic Co.MNCsYearTD/SETD/TATD/CETD/SETD/TATD/CE19962.7240.4302.1970.2500.1200.22319971.7760.3031.5860.2290.1210.21919981.9850.3321.7250.2620.1290.23519991.9370.3451.7400.1890.0960.18020002.0490.3671.8200.1140.0670.10320012.4600.3982.1710.1420.0730.13920022.6720.4172.3690.0970.0480.09520032.8260.4402.4960.2580.1080.21620042.7780.4082.5010.3090.1210.27720051.8580.3801.6540.6070.1460.51020062.1080.3441.9560.5510.1330.48620073.1050.3503.0200.5750.1210.48720081.7470.3241.6890.3730.1040.31720090.9380.2720.8630.0810.0400.07720101.1380.2411.0510.0200.0120.02020111.3340.2811.2410.0800.0390.07920121.4840.2881.3790.0830.0440.08320131.2200.2821.1340.0570.0300.05720141.2750.2921.1520.0990.0440.09820151.1570.3080.9590.0300.0140.028G.Mean1.9290.3401.7350.2200.0800.197Source: Compiled from Annual Reports of Sample Firms(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015) A3Domestic Co.MNCsYearNet Profit Margin(%)Sales Growth(%)Firm Size (Ln TA)Net Profit Margin(%)Sales Growth(%)Firm Size (Ln TA) A4Name of variableOriginal value ADF Test P value of test statistic statisticFirst difference ADF Test P value of test statistic statisticSecond difference ADF Test P value of test statistic statisticsFLR(CV)-2.960250.1688-3.907480.0355-5.095520.004827FLR(MAD)-2.085190.519-2.874260.1935-3.851580.04094TD/TA-1.908950.6085-5.499590.00208-6.333320.0005998TD/SE-2.43990.3495-4.414670.01437-4.674950.009896TD/CE-2.413070.3612-4.235770.0198-4.772850.008341NPM-1.974750.5752-3.014540.1567-4.252880.02054EBIT-0.0422870.9916-1.567930.7624-2.597570.02852EPS-1.356510.8383-5.343260.002745-9.359320.0000015FS-4.180040.02065-4.282880.0182-3.297920.0102SG-3.94090.03197-5.406240.002454-5.360910.00307Source: Annual Reports of Sample Firms (1996-2015) Note: Data processed on GretlTable A5: Normality Test of ResidualsModel No.Kolmogorov-SmirnovShapiro-WilkChi SquareStatisticdfSig.StatisticdfSig.Chi StatisticP valueD1(FLR-CV).13117.200.95717.5830.4750.78845D2 (FLR-MAD).11217.200.94417.3753.8460.14620M1(FLR-CV)0.100170.2000.96517.7300.8120.66615M2 (FLR-MAD)0.205170.0550.90417.0801.5900.45162Source: Compiled from Annual Reports (1996-2015) Note: Data processed on SPSS & Gretl A6Original valueFirst differenceSecond differenceName of variableADF Test statisticP value of test statisticADF Test statisticP value of test statisticADF Test statisticP value of test statisticsFLR(CV)-2.248470.4378-4.856460.006518-6.108280.0008648FLR(MAD)-2.131740.4954-4.561180.01105-5.880980.001266TD/TA-1.71910.7002-3.247930.1085-5.708710.001704TD/SE-1.398290.8255-2.953710.1719-5.988670.001056TD/CE-1.375370.8326-2.64250.2684-5.230420.003832NPM-2.229790.4468-4.084080.02597-5.492190.002457EBIT-0.0976320.9902-4.795110.007271-6.324260.0006088EPS-1.690010.7133-4.425830.01408-5.73340.001634FS-1.729440.6954-6.010770.0008396-8.805030.0000023SG-4.142240.02214-6.328390.0004875-7.148080.0001Source: Compiled from Annual Reports (1996-2015) Note: Data processed on Gretl software A7Model D1(FLR-CV) & Model D2 (FLR-MAD)2 nd differenceMeasures taken to removeVIF after removingof VariablesVIFmulticollinearitymulticollinearityNPM1.3391.139EPS1.8021.668TD/TA3.8031.839TD/SE218.3691.755EBIT1.5071.463TD/CE206.825Variable droppedSG1.6281.611FS2.1251.889Model M1(FLR-CV) & Model M2 (FLR-MAD)EBIT31.9335.694SG1.8943.591TD/TA9.7446.427TD/SE74.0323.750FS5.1414.095EPS63.704Transformed to reciprocal9.012NPM23.680Transformed to reciprocal9.585TD/CE89.998Variable droppedSource: A8Name of the modelNo. of observationsLM test statisticp value of LM test statisticD1(FLR-CV)173.5587370.828966D2(FLR-MAD)174.5180530.718542M1(FLR-CV)173.3770390.848073M2(FLR-MAD)174.9771430.662753Source: Compiled from Annual Reports (1996-2015) Note: Data processed on Gretl software A9TestP valueName of the modelDW StatP value of DWD UD LDecisionstatistic of LMof LM testD1(FLR-CV)2.53370.88542.53660.4511No decision3.22940.11D2(FLR-MAD)2.33060.79242.53660.4511No decision2.29710.168M1(FLR-CV)1.9776.88002.53660.4511Near 20.10700.752M2(FLR-MAD)2.2112.95742.53660.4511Near 21.34480.28Source: Compiled from Annual Reports (1996-2015) Note: Data processed on Gretl softwareTable A10: Summary Statistics of the ModelsModel No.R squareAdj. 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