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\setlength{\parsep}{2pt}% \def\makelabel##1{\upshape ##1}}% } {\end{list}\end{raggedright}} \newenvironment{specHead}[2]% {\vspace{20pt}\hrule\vspace{10pt}% \phantomsection\label{#1}\markright{#2}% \pdfbookmark[2]{#2}{#1}% \hspace{-0.75in}{\bfseries\fontsize{16pt}{18pt}\selectfont#2}% }{} \def\TheFullDate{2017-01-15 (revised: 15 January 2017)} \def\TheID{\makeatother } \def\TheDate{2017-01-15} \title{Impact of Debt Financing on Financial Leverage Risk of Firms: A Comparative Study between Listed MNCs and Domestic Companies of Bangladesh} \author{}\makeatletter \makeatletter \newcommand*{\cleartoleftpage}{% \clearpage \if@twoside \ifodd\c@page \hbox{}\newpage \if@twocolumn \hbox{}\newpage \fi \fi \fi } \makeatother \makeatletter \thispagestyle{empty} \markright{\@title}\markboth{\@title}{\@author} \renewcommand\small{\@setfontsize\small{9pt}{11pt}\abovedisplayskip 8.5\p@ plus3\p@ minus4\p@ \belowdisplayskip \abovedisplayskip \abovedisplayshortskip \z@ plus2\p@ \belowdisplayshortskip 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\setlength{\parindent}{0pt}\raggedright \textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}} \vskip16pt \textcolor{GJBlue}{\large\bfseries\abstractname\space} }{% \vskip8pt \textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}} \vskip16pt } \usepackage[absolute,overlay]{textpos} \makeatother \usepackage{lineno} \linenumbers \begin{document} \author[1]{Dr. Syed Mohammad Khaled Rahman} \affil[1]{ } \renewcommand\Authands{ and } \date{\small \em Received: 12 December 2016 Accepted: 31 December 2016 Published: 15 January 2017} \maketitle \begin{abstract} Financial risk of leverage or capital gearing lies in the possibilities of loss of equity earnings and threat to insolvency. The main objective of the study was to explore the impact of debt financing on financial leverage risk of DSE-listed MNCs & domestic companies of Bangladesh over a 20-year period (1996-2015). After analyzing domestic companies and MNCs, it is seen that leverage ratios are positively related with financial leverage risk (FLR). For domestic companies, 1% increase of 2nd difference of TD/SE and TD/TA results in 0.005 and 0.001 increase in 2nd difference of FLR (CV) respectively and vice-versa. For MNCs, 1% increase of 2nd difference of TD/SE and TD/TA results in 0.009 and 0.065 increase in FLR (CV) [2nd difference] respectively and vice-versa. After test of null hypothesis, 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. \end{abstract} \keywords{financial, leverage, risk, ratios.} \begin{textblock*}{18cm}(1cm,1cm) % {block width} (coords) \textcolor{GJBlue}{\LARGE Global Journals \LaTeX\ JournalKaleidoscope\texttrademark} \end{textblock*} \begin{textblock*}{18cm}(1.4cm,1.5cm) % {block width} (coords) \textcolor{GJBlue}{\footnotesize \\ Artificial Intelligence formulated this projection for compatibility purposes from the original article published at Global Journals. However, this technology is currently in beta. \emph{Therefore, kindly ignore odd layouts, missed formulae, text, tables, or figures.}} \end{textblock*} \let\tabcellsep& \section[{Introduction}]{Introduction}\par 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. \section[{II.}]{II.}\par Statement of the Problem \hyperref[b1]{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. \hyperref[b2]{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:\par 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. \section[{III.}]{III.}\par Literature Review \hyperref[b3]{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 \hyperref[b4]{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." {\ref 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. \hyperref[b2]{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. \hyperref[b7]{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.\par 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. \hyperref[b9]{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. \hyperref[b10]{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.\par 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. \hyperref[b13]{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.\par IV. \section[{Objective of the Study}]{Objective of the Study}\par 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.\par V.\par methodology of the study \section[{Results and Discussion}]{Results and Discussion}\par a) Analyzing Impact of Leverage on Financial Risk By FLR Models\par 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 = \section[{f. Overall Fitness of the Models}]{f. Overall Fitness of the Models}\par 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. \section[{b) Test of Hypothesis}]{b) Test of Hypothesis}\par 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. \section[{VII. Recommendations and Conclusion}]{VII. Recommendations and Conclusion}\par 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. \section[{Acknowledgement}]{Acknowledgement}\par 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. \begin{figure}[htbp] \noindent\textbf{} \par \begin{longtable}{P{0.7012282497441146\textwidth}P{0.14877175025588535\textwidth}} Sample Size \& Sample Items: The sample in this\tabcellsep \\ study consists of 14 companies (7 from each\tabcellsep \\ population) listed in Dhaka Stock Exchange (DSE).\tabcellsep \\ Two companies are selected from Pharmaceuticals \&\tabcellsep \\ Chemicals industry and one company is selected from\tabcellsep \\ domestic companies. F statistic and coefficient of\tabcellsep \\ determination or r 2 value was used to measure overall\tabcellsep \\ goodness to fit of the models. Normality test has been\tabcellsep \\ done by Kolmogorov-Smirnov, Shapiro-Wilk and chi-\tabcellsep \\ square test. Data stationary has been judged by\tabcellsep \\ Augmented Dickey Fuller (ADF) test. Variance Inflation\tabcellsep \\ Factor (VIF) has been used to test multicollinearity\tabcellsep \\ among 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.\tabcellsep 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.\tabcellsep \end{longtable} \par {\small\itshape [Note: 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]} \caption{\label{tab_0}}\end{figure} \begin{figure}[htbp] \noindent\textbf{1} \par \begin{longtable}{P{0.35674356699201415\textwidth}P{0.05732031943212067\textwidth}P{0.024134871339840283\textwidth}P{0.35146406388642415\textwidth}P{0.029414374445430345\textwidth}P{0.030922803904170362\textwidth}} 2nd difference of variables\tabcellsep \multicolumn{2}{l}{Unstandardized Coefficients B Std. Error}\tabcellsep Standardized Coefficients Beta\tabcellsep t\tabcellsep Sig.\\ (Constant)\tabcellsep -.001\tabcellsep .030\tabcellsep \tabcellsep -.043\tabcellsep .967\\ TD/TA\tabcellsep .558\tabcellsep .800\tabcellsep .164\tabcellsep .698\tabcellsep .503\\ TD/SE\tabcellsep .119\tabcellsep .046\tabcellsep .600\tabcellsep 2.605\tabcellsep .029**\\ NPM\tabcellsep -.043\tabcellsep .011\tabcellsep -.721\tabcellsep -3.887\tabcellsep .004***\\ SG\tabcellsep -.002\tabcellsep .002\tabcellsep -.196\tabcellsep -.887\tabcellsep .398\\ FS\tabcellsep .218\tabcellsep .391\tabcellsep .133\tabcellsep .557\tabcellsep .591\\ EPS\tabcellsep .009\tabcellsep .015\tabcellsep .126\tabcellsep .563\tabcellsep .587\\ EBIT\tabcellsep .0003\tabcellsep .001\tabcellsep -.136\tabcellsep -.647\tabcellsep .534\\ \multicolumn{5}{l}{Note: Data processed on SPSS **Significant at 5\%, ***Significant at 1\%}\tabcellsep \\ The equation of the fitted model is:\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ \multicolumn{3}{l}{dd\textunderscore FLR(CV) = -0.001 + 0.558*dd\textunderscore TD/TA +}\tabcellsep \tabcellsep \tabcellsep \\ \multicolumn{3}{l}{0.119*dd\textunderscore TD/SE -0.043*dd\textunderscore NPM -0.002 *dd\textunderscore SG + 0.218*dd\textunderscore FS + 0.009*dd\textunderscore EPS -0.0003*dd\textunderscore EBIT (dd\textunderscore variable = 2 nd difference of variable)}\tabcellsep \multicolumn{3}{l}{b. Model D2 (FLR-MAD) The equation of fitted model is: dd\textunderscore FLR(MAD) = 0.007 -0.029*dd\textunderscore NPM +}\\ \tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{0.315*dd\textunderscore TD/TA + 0.004*dd\textunderscore TD/SE -0.001*dd\textunderscore SG +}\\ \tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{0.449*dd\textunderscore FS + 0.024*dd\textunderscore EPS -0.0004*dd\textunderscore EBIT}\\ \tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{(dd\textunderscore variable = 2 nd difference of variable)}\\ \tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{Coefficients of model D2 (FLR-MAD) is as follows:}\end{longtable} \par \caption{\label{tab_1}Table 1 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{2} \par \begin{longtable}{P{0.24894459102902375\textwidth}P{0.16820580474934035\textwidth}P{0.07176781002638521\textwidth}P{0.06728232189973615\textwidth}P{0.11662269129287597\textwidth}P{0.08746701846965699\textwidth}P{0.08970976253298153\textwidth}} 2nd difference of variables\tabcellsep \multicolumn{2}{l}{Unstandardized Coefficients B Std. Error}\tabcellsep \multicolumn{2}{l}{Standardized Coefficients Beta}\tabcellsep t\tabcellsep Sig.\\ (Constant)\tabcellsep .007\tabcellsep .023\tabcellsep \tabcellsep \tabcellsep .289\tabcellsep .779\\ TD/TA\tabcellsep .315\tabcellsep .629\tabcellsep \tabcellsep .130\tabcellsep .500\tabcellsep .629\\ TD/SE\tabcellsep .004\tabcellsep .036\tabcellsep \tabcellsep .027\tabcellsep .106\tabcellsep .918\\ NPM\tabcellsep -.029\tabcellsep .009\tabcellsep \tabcellsep -.691\tabcellsep -3.384\tabcellsep .008***\\ SG\tabcellsep -.001\tabcellsep .002\tabcellsep \tabcellsep -.164\tabcellsep -.675\tabcellsep .516\\ FS\tabcellsep .449\tabcellsep .308\tabcellsep \tabcellsep .384\tabcellsep 1.459\tabcellsep .178\\ EPS\tabcellsep .024\tabcellsep .012\tabcellsep \tabcellsep .489\tabcellsep 1.979\tabcellsep .079*\\ EBIT\tabcellsep .0004\tabcellsep .000\tabcellsep \tabcellsep -.206\tabcellsep -.889\tabcellsep .397\\ \multicolumn{4}{l}{Note: Data processed on SPSS *Significant at 10\%,}\tabcellsep \multicolumn{2}{l}{***Significant at 1\%}\end{longtable} \par \caption{\label{tab_2}Table 2 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{3} \par \begin{longtable}{P{0.3562874251497006\textwidth}P{0.05598802395209581\textwidth}P{0.11270316509837466\textwidth}P{0.026903336184773308\textwidth}P{0.23631308810949528\textwidth}P{0.029811804961505563\textwidth}P{0.03199315654405475\textwidth}} \multicolumn{2}{l}{2nd difference of variables}\tabcellsep \multicolumn{2}{l}{Unstandardized Coefficients B Std. Error}\tabcellsep Standardized Coefficients Beta\tabcellsep t\tabcellsep Sig.\\ \multicolumn{2}{l}{(Constant)}\tabcellsep -.013\tabcellsep .035\tabcellsep \tabcellsep -.365\tabcellsep .723\\ \tabcellsep TD/TA\tabcellsep 6.549\tabcellsep 2.358\tabcellsep .711\tabcellsep 2.777\tabcellsep .021**\\ \tabcellsep TD/SE\tabcellsep .900\tabcellsep .426\tabcellsep .413\tabcellsep 2.111\tabcellsep .064*\\ \tabcellsep 1/NPM\tabcellsep 6.041\tabcellsep 2.959\tabcellsep .639\tabcellsep 2.042\tabcellsep .072*\\ \tabcellsep 1/EPS\tabcellsep 3.896\tabcellsep 3.285\tabcellsep .360\tabcellsep 1.186\tabcellsep .266\\ \tabcellsep EBIT\tabcellsep .001\tabcellsep .000\tabcellsep .560\tabcellsep 2.323\tabcellsep .045**\\ \tabcellsep SG\tabcellsep .010\tabcellsep .003\tabcellsep .577\tabcellsep 3.015\tabcellsep .015**\\ \tabcellsep FS\tabcellsep -.218\tabcellsep 1.041\tabcellsep -.043\tabcellsep -.210\tabcellsep .839\\ \multicolumn{6}{l}{Note: Data processed on SPSS **Significant at 5\%, *Significant at 10\%}\\ \multicolumn{3}{l}{The equation of the fitted model is:}\tabcellsep \tabcellsep \multicolumn{3}{l}{TD/TA is changed by 1 or 100\%, then FLR (CV) [2 nd}\\ \multicolumn{4}{l}{dd\textunderscore FLR (CV) = -0.013 + 6.041*dd\textunderscore (1/NPM) +}\tabcellsep \multicolumn{3}{l}{difference] would change by 0.9 and 6.54 respectively}\\ 3.896*dd\textunderscore (1/EPS)\tabcellsep +\tabcellsep 6.549*dd\textunderscore TD/TA\tabcellsep +\tabcellsep \multicolumn{3}{l}{or in other words, 1\% increase of 2 nd difference of TD/SE}\\ \multicolumn{4}{l}{0.90*dd\textunderscore TD/SE + 0.001*dd\textunderscore EBIT + 0.01*dd\textunderscore SG -}\tabcellsep \multicolumn{3}{l}{and TD/TA results in 0.009 and 0.065 increase in FLR}\\ 0.218*dd\textunderscore FS\tabcellsep \multicolumn{3}{l}{(dd\textunderscore variable = 2 nd difference of}\tabcellsep \multicolumn{3}{l}{(CV) [2 nd difference] respectively and vice-versa.}\\ variable)\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ \multicolumn{4}{l}{Leverage ratios are positively related with}\tabcellsep \tabcellsep \\ \multicolumn{4}{l}{financial leverage risk. If 2 nd difference of TD/SE and}\tabcellsep \tabcellsep \\ \multicolumn{2}{l}{d. Model M2 (FLR-MAD)}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\ \tabcellsep \tabcellsep \multicolumn{4}{l}{Coefficients of model M2 (FLR-MAD) is as follows:}\end{longtable} \par \caption{\label{tab_3}Table 3 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{4} \par \begin{longtable}{P{0.563523890784983\textwidth}P{0.056569965870307166\textwidth}P{0.026109215017064844\textwidth}P{0.14360068259385664\textwidth}P{0.029735494880546075\textwidth}P{0.03046075085324232\textwidth}} 2 nd difference of variables\tabcellsep \multicolumn{2}{l}{Unstandardized Coefficients B Std. Error}\tabcellsep Standardized Coefficients Beta\tabcellsep t\tabcellsep Sig.\\ (Constant)\tabcellsep -.007\tabcellsep .035\tabcellsep \tabcellsep -.206\tabcellsep .842\\ TD/TA\tabcellsep 5.612\tabcellsep 2.347\tabcellsep .596\tabcellsep 2.391\tabcellsep .040**\\ TD/SE\tabcellsep .428\tabcellsep .424\tabcellsep .192\tabcellsep 1.010\tabcellsep .339\\ 1/NPM\tabcellsep 8.988\tabcellsep 2.946\tabcellsep .928\tabcellsep 3.051\tabcellsep .014**\\ 1/EPS\tabcellsep 2.741\tabcellsep 3.270\tabcellsep .247\tabcellsep .838\tabcellsep .424\\ EBIT\tabcellsep .000\tabcellsep .000\tabcellsep .298\tabcellsep 1.270\tabcellsep .236\\ SG\tabcellsep .007\tabcellsep .003\tabcellsep .433\tabcellsep 2.323\tabcellsep .045**\\ FS\tabcellsep -1.658\tabcellsep 1.037\tabcellsep -.318\tabcellsep -1.600\tabcellsep .144\\ \multicolumn{4}{l}{Note: Data processed on SPSS **Significant at 5\%}\tabcellsep \tabcellsep \\ \multicolumn{2}{l}{The equation of the fitted model is:}\tabcellsep \tabcellsep \multicolumn{3}{l}{increase in FLR (MAD) [2 nd difference] respectively and}\\ \multicolumn{3}{l}{dd\textunderscore FLR (MAD) = -0.007 + 8.988*dd\textunderscore (1/NPM) +}\tabcellsep vice-versa.\tabcellsep \tabcellsep \\ \multicolumn{3}{l}{2.741*dd\textunderscore (1/EPS) 0.428*dd\textunderscore TD/SE + 0.000*dd\textunderscore EBIT + 0.007*dd\textunderscore SG -+ 5.612*dd\textunderscore TD/TA +}\tabcellsep \multicolumn{3}{l}{ii. Fitness of models (Model diagnostics)}\\ 1.658*dd\textunderscore FS\tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{a. Test of Stationarity of Data}\\ \multicolumn{3}{l}{Leverage ratios (2 nd difference) are positively}\tabcellsep \tabcellsep \tabcellsep \\ \multicolumn{3}{l}{related with FLR (MAD) [2 nd difference]. The debt ratio}\tabcellsep \tabcellsep \tabcellsep \\ \multicolumn{3}{l}{has significant impact on FLR (MAD). If 2 nd difference of}\tabcellsep \tabcellsep \tabcellsep \\ \multicolumn{3}{l}{TD/SE and TD/TA is changed by 1 or 100\% then FLR}\tabcellsep \tabcellsep \tabcellsep \\ \multicolumn{3}{l}{(MAD) [2 nd difference] would change by 0.428 and 5.61}\tabcellsep \tabcellsep \tabcellsep \\ \multicolumn{3}{l}{respectively or in other words, 1\% increase of TD/SE}\tabcellsep \tabcellsep \tabcellsep \\ \multicolumn{3}{l}{and TD/TA (2 nd difference) results in 0.004 and 0.056}\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par \caption{\label{tab_4}Table 4 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{5} \par \begin{longtable}{P{0.37988826815642457\textwidth}P{0.0664804469273743\textwidth}P{0.10763500931098696\textwidth}P{0.08072625698324022\textwidth}P{0.061731843575419\textwidth}P{0.15353817504655493\textwidth}} Leverage \& FLR\tabcellsep Difference\tabcellsep Coefficient\tabcellsep t statistic\tabcellsep p value\tabcellsep Decision regarding H 0 hypothesis\\ \tabcellsep \tabcellsep \multicolumn{2}{l}{Domestic Companies}\tabcellsep \tabcellsep \\ FLR (CV) \& TD/TA\tabcellsep 2 nd\tabcellsep \tabcellsep 0.697\tabcellsep .503\tabcellsep Accepted\\ FLR (CV) \& TD/SE\tabcellsep 2 nd\tabcellsep 0.119\tabcellsep 2.604\tabcellsep .028\tabcellsep Rejected\\ FLR (MAD) \& TD/TA\tabcellsep 2 nd\tabcellsep 0.314\tabcellsep 0.500\tabcellsep .628\tabcellsep Accepted\\ FLR (MAD) \& TD/SE\tabcellsep 2 nd\tabcellsep 0.003\tabcellsep 0.106\tabcellsep .917\tabcellsep Accepted\\ \tabcellsep \tabcellsep MNCs\tabcellsep \tabcellsep \tabcellsep \\ FLR (CV) \& TD/TA\tabcellsep 2 nd\tabcellsep 6.549\tabcellsep 2.777\tabcellsep .021\tabcellsep Rejected\\ FLR (CV) \& TD/SE\tabcellsep 2 nd\tabcellsep 0.900\tabcellsep 2.111\tabcellsep .063\tabcellsep Accepted\\ FLR (MAD) \& TD/TA\tabcellsep 2 nd\tabcellsep 5.613\tabcellsep 2.391\tabcellsep .040\tabcellsep Rejected\\ FLR (MAD) \& TD/SE\tabcellsep 2 nd\tabcellsep 0.428\tabcellsep 1.010\tabcellsep .338\tabcellsep Accepted\\ \multicolumn{6}{l}{Source: Outcome of Regression Models Note: Computation done on SPSS \& Gretl software}\end{longtable} \par \caption{\label{tab_5}Table 5 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{A1} \par \begin{longtable}{P{0.06710526315789474\textwidth}P{0.11482456140350877\textwidth}P{0.0887280701754386\textwidth}P{0.1312280701754386\textwidth}P{0.08052631578947368\textwidth}P{0.11482456140350877\textwidth}P{0.085\textwidth}P{0.08350877192982456\textwidth}P{0.08425438596491228\textwidth}} \tabcellsep \tabcellsep Domestic Co.\tabcellsep \tabcellsep \tabcellsep \tabcellsep MNCs\tabcellsep \tabcellsep \\ Year\tabcellsep Mean EBIT\tabcellsep Mean EPS\tabcellsep FLR\tabcellsep FLR\tabcellsep Mean EBIT\tabcellsep Mean EPS\tabcellsep FLR\tabcellsep FLR\\ \tabcellsep (million Tk.)\tabcellsep (Tk.)\tabcellsep (CV)\tabcellsep (MAD)\tabcellsep (million Tk.)\tabcellsep (Tk.)\tabcellsep (CV)\tabcellsep (MAD)\\ 1996\tabcellsep 137.14\tabcellsep 6.38\tabcellsep 0.883\tabcellsep \tabcellsep 191.35\tabcellsep 7.74\tabcellsep 0.461\tabcellsep 0.525\\ 1997\tabcellsep 160.53\tabcellsep 5.31\tabcellsep 0.858\tabcellsep 0.932\tabcellsep 231.63\tabcellsep 7.60\tabcellsep 0.486\tabcellsep 0.621\\ 1998\tabcellsep 184.88\tabcellsep 6.76\tabcellsep 0.834\tabcellsep 0.916\tabcellsep 267.74\tabcellsep 7.84\tabcellsep 0.306\tabcellsep 0.352\\ 1999\tabcellsep 210.26\tabcellsep 7.83\tabcellsep 0.864\tabcellsep 0.842\tabcellsep 217.66\tabcellsep 7.29\tabcellsep 0.422\tabcellsep 0.436\\ 2000\tabcellsep 242.49\tabcellsep 8.93\tabcellsep 0.881\tabcellsep 0.858\tabcellsep 314.09\tabcellsep 11.76\tabcellsep 0.428\tabcellsep 0.404\\ 2001\tabcellsep 284.35\tabcellsep 10.93\tabcellsep 0.916\tabcellsep 0.838\tabcellsep 347.80\tabcellsep 9.96\tabcellsep 0.401\tabcellsep 0.552\\ 2002\tabcellsep 282.38\tabcellsep 9.72\tabcellsep 0.883\tabcellsep 0.916\tabcellsep 309.50\tabcellsep 8.29\tabcellsep 0.888\tabcellsep 0.990\\ 2003\tabcellsep 282.14\tabcellsep 8.87\tabcellsep 0.771\tabcellsep 0.879\tabcellsep 319.71\tabcellsep 10.24\tabcellsep 0.626\tabcellsep 0.708\\ 2004\tabcellsep 322.38\tabcellsep 8.74\tabcellsep 0.785\tabcellsep 0.810\tabcellsep 289.22\tabcellsep 8.77\tabcellsep 0.512\tabcellsep 0.609\\ 2005\tabcellsep 417.68\tabcellsep 9.78\tabcellsep 0.741\tabcellsep 0.828\tabcellsep 245.52\tabcellsep 7.72\tabcellsep 0.994\tabcellsep 1.172\\ 2006\tabcellsep 454.42\tabcellsep 10.27\tabcellsep 0.845\tabcellsep 0.780\tabcellsep 378.09\tabcellsep 11.56\tabcellsep 0.977\tabcellsep 1.036\\ 2007\tabcellsep 518.83\tabcellsep 13.00\tabcellsep 0.821\tabcellsep 0.928\tabcellsep 518.03\tabcellsep 14.97\tabcellsep 0.743\tabcellsep 0.766\\ 2008\tabcellsep 621.09\tabcellsep 12.84\tabcellsep 1.050\tabcellsep 1.048\tabcellsep 728.60\tabcellsep 21.21\tabcellsep 0.550\tabcellsep 0.688\\ 2009\tabcellsep 872.04\tabcellsep 15.56\tabcellsep 0.849\tabcellsep 1.036\tabcellsep 993.49\tabcellsep 29.85\tabcellsep 0.398\tabcellsep 0.446\\ 2010\tabcellsep 1093.19\tabcellsep 12.10\tabcellsep 0.592\tabcellsep 0.817\tabcellsep 1480.99\tabcellsep 42.58\tabcellsep 0.582\tabcellsep 0.515\\ 2011\tabcellsep 1330.35\tabcellsep 12.23\tabcellsep 0.645\tabcellsep 0.626\tabcellsep 1306.48\tabcellsep 29.28\tabcellsep 0.382\tabcellsep 0.498\\ 2012\tabcellsep 1598.48\tabcellsep 11.06\tabcellsep 0.761\tabcellsep 0.697\tabcellsep 1643.05\tabcellsep 32.75\tabcellsep 0.406\tabcellsep 0.475\\ 2013\tabcellsep 1819.95\tabcellsep 11.05\tabcellsep 0.804\tabcellsep 0.747\tabcellsep 2128.56\tabcellsep 42.39\tabcellsep 0.388\tabcellsep 0.468\\ 2014\tabcellsep 1908.44\tabcellsep 9.63\tabcellsep 0.714\tabcellsep 0.778\tabcellsep 2376.38\tabcellsep 47.09\tabcellsep 0.425\tabcellsep 0.484\\ 2015\tabcellsep 2224.11\tabcellsep 9.08\tabcellsep 0.583\tabcellsep 0.709\tabcellsep 2674.52\tabcellsep 41.80\tabcellsep 0.437\tabcellsep 0.550\\ G.Mean\tabcellsep 748.26\tabcellsep 10.00\tabcellsep 0.804\tabcellsep 0.645\tabcellsep 848.12\tabcellsep 20.04\tabcellsep 0.524\tabcellsep 0.607\\ \tabcellsep \tabcellsep \tabcellsep \multicolumn{6}{l}{Source: Compiled from Annual Reports of Sample Firms (1996-2015)}\end{longtable} \par \caption{\label{tab_6}Table A1 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{A2} \par \begin{longtable}{P{0.09986945169712794\textwidth}P{0.12206266318537859\textwidth}P{0.13537859007832898\textwidth}P{0.12206266318537859\textwidth}P{0.12206266318537859\textwidth}P{0.1265013054830287\textwidth}P{0.12206266318537859\textwidth}} \tabcellsep \tabcellsep Domestic Co.\tabcellsep \tabcellsep \tabcellsep MNCs\tabcellsep \\ Year\tabcellsep TD/SE\tabcellsep TD/TA\tabcellsep TD/CE\tabcellsep TD/SE\tabcellsep TD/TA\tabcellsep TD/CE\\ 1996\tabcellsep 2.724\tabcellsep 0.430\tabcellsep 2.197\tabcellsep 0.250\tabcellsep 0.120\tabcellsep 0.223\\ 1997\tabcellsep 1.776\tabcellsep 0.303\tabcellsep 1.586\tabcellsep 0.229\tabcellsep 0.121\tabcellsep 0.219\\ 1998\tabcellsep 1.985\tabcellsep 0.332\tabcellsep 1.725\tabcellsep 0.262\tabcellsep 0.129\tabcellsep 0.235\\ 1999\tabcellsep 1.937\tabcellsep 0.345\tabcellsep 1.740\tabcellsep 0.189\tabcellsep 0.096\tabcellsep 0.180\\ 2000\tabcellsep 2.049\tabcellsep 0.367\tabcellsep 1.820\tabcellsep 0.114\tabcellsep 0.067\tabcellsep 0.103\\ 2001\tabcellsep 2.460\tabcellsep 0.398\tabcellsep 2.171\tabcellsep 0.142\tabcellsep 0.073\tabcellsep 0.139\\ 2002\tabcellsep 2.672\tabcellsep 0.417\tabcellsep 2.369\tabcellsep 0.097\tabcellsep 0.048\tabcellsep 0.095\\ 2003\tabcellsep 2.826\tabcellsep 0.440\tabcellsep 2.496\tabcellsep 0.258\tabcellsep 0.108\tabcellsep 0.216\\ 2004\tabcellsep 2.778\tabcellsep 0.408\tabcellsep 2.501\tabcellsep 0.309\tabcellsep 0.121\tabcellsep 0.277\\ 2005\tabcellsep 1.858\tabcellsep 0.380\tabcellsep 1.654\tabcellsep 0.607\tabcellsep 0.146\tabcellsep 0.510\\ 2006\tabcellsep 2.108\tabcellsep 0.344\tabcellsep 1.956\tabcellsep 0.551\tabcellsep 0.133\tabcellsep 0.486\\ 2007\tabcellsep 3.105\tabcellsep 0.350\tabcellsep 3.020\tabcellsep 0.575\tabcellsep 0.121\tabcellsep 0.487\\ 2008\tabcellsep 1.747\tabcellsep 0.324\tabcellsep 1.689\tabcellsep 0.373\tabcellsep 0.104\tabcellsep 0.317\\ 2009\tabcellsep 0.938\tabcellsep 0.272\tabcellsep 0.863\tabcellsep 0.081\tabcellsep 0.040\tabcellsep 0.077\\ 2010\tabcellsep 1.138\tabcellsep 0.241\tabcellsep 1.051\tabcellsep 0.020\tabcellsep 0.012\tabcellsep 0.020\\ 2011\tabcellsep 1.334\tabcellsep 0.281\tabcellsep 1.241\tabcellsep 0.080\tabcellsep 0.039\tabcellsep 0.079\\ 2012\tabcellsep 1.484\tabcellsep 0.288\tabcellsep 1.379\tabcellsep 0.083\tabcellsep 0.044\tabcellsep 0.083\\ 2013\tabcellsep 1.220\tabcellsep 0.282\tabcellsep 1.134\tabcellsep 0.057\tabcellsep 0.030\tabcellsep 0.057\\ 2014\tabcellsep 1.275\tabcellsep 0.292\tabcellsep 1.152\tabcellsep 0.099\tabcellsep 0.044\tabcellsep 0.098\\ 2015\tabcellsep 1.157\tabcellsep 0.308\tabcellsep 0.959\tabcellsep 0.030\tabcellsep 0.014\tabcellsep 0.028\\ G.Mean\tabcellsep 1.929\tabcellsep 0.340\tabcellsep 1.735\tabcellsep 0.220\tabcellsep 0.080\tabcellsep 0.197\end{longtable} \par {\small\itshape [Note: Source: 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) ]} \caption{\label{tab_7}Table A2 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{A3} \par \begin{longtable}{P{0.0265625\textwidth}P{0.13945312499999998\textwidth}P{0.1859375\textwidth}P{0.112890625\textwidth}P{0.13945312499999998\textwidth}P{0.1328125\textwidth}P{0.112890625\textwidth}} \tabcellsep \tabcellsep Domestic Co.\tabcellsep \tabcellsep \tabcellsep MNCs\tabcellsep \\ Year\tabcellsep Net Profit Margin(\%)\tabcellsep Sales Growth(\%)\tabcellsep Firm Size (Ln TA)\tabcellsep Net Profit Margin(\%)\tabcellsep Sales Growth(\%)\tabcellsep Firm Size (Ln TA)\end{longtable} \par \caption{\label{tab_8}Table A3 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{A4} \par \begin{longtable}{P{0.08901760889712697\textwidth}P{0.10871177015755328\textwidth}P{0.10949953660797034\textwidth}P{0.07405004633920297\textwidth}P{0.12840593141797962\textwidth}P{0.030722891566265058\textwidth}P{0.05987025023169601\textwidth}P{0.015755329008341055\textwidth}P{0.14652455977757184\textwidth}P{0.08744207599629286\textwidth}} Name of variable\tabcellsep \multicolumn{3}{l}{Original value ADF Test P value of test statistic statistic}\tabcellsep \multicolumn{4}{l}{First difference ADF Test P value of test statistic statistic}\tabcellsep \multicolumn{2}{l}{Second difference ADF Test P value of test statistic statistics}\\ FLR(CV)\tabcellsep -2.96025\tabcellsep \tabcellsep 0.1688\tabcellsep \multicolumn{2}{l}{-3.90748}\tabcellsep \multicolumn{2}{l}{0.0355}\tabcellsep -5.09552\tabcellsep 0.004827\\ FLR(MAD)\tabcellsep -2.08519\tabcellsep \tabcellsep 0.519\tabcellsep \multicolumn{2}{l}{-2.87426}\tabcellsep \multicolumn{2}{l}{0.1935}\tabcellsep -3.85158\tabcellsep 0.04094\\ TD/TA\tabcellsep -1.90895\tabcellsep \tabcellsep 0.6085\tabcellsep \multicolumn{2}{l}{-5.49959}\tabcellsep \multicolumn{2}{l}{0.00208}\tabcellsep -6.33332\tabcellsep 0.0005998\\ TD/SE\tabcellsep -2.4399\tabcellsep \tabcellsep 0.3495\tabcellsep \multicolumn{2}{l}{-4.41467}\tabcellsep \multicolumn{2}{l}{0.01437}\tabcellsep -4.67495\tabcellsep 0.009896\\ TD/CE\tabcellsep -2.41307\tabcellsep \tabcellsep 0.3612\tabcellsep \multicolumn{2}{l}{-4.23577}\tabcellsep \multicolumn{2}{l}{0.0198}\tabcellsep -4.77285\tabcellsep 0.008341\\ NPM\tabcellsep -1.97475\tabcellsep \tabcellsep 0.5752\tabcellsep \multicolumn{2}{l}{-3.01454}\tabcellsep \multicolumn{2}{l}{0.1567}\tabcellsep -4.25288\tabcellsep 0.02054\\ EBIT\tabcellsep -0.042287\tabcellsep \tabcellsep 0.9916\tabcellsep \multicolumn{2}{l}{-1.56793}\tabcellsep \multicolumn{2}{l}{0.7624}\tabcellsep -2.59757\tabcellsep 0.02852\\ EPS\tabcellsep -1.35651\tabcellsep \tabcellsep 0.8383\tabcellsep \multicolumn{2}{l}{-5.34326}\tabcellsep \multicolumn{2}{l}{0.002745}\tabcellsep -9.35932\tabcellsep 0.0000015\\ FS\tabcellsep -4.18004\tabcellsep \multicolumn{2}{l}{0.02065}\tabcellsep \multicolumn{2}{l}{-4.28288}\tabcellsep \multicolumn{2}{l}{0.0182}\tabcellsep -3.29792\tabcellsep 0.0102\\ SG\tabcellsep -3.9409\tabcellsep \multicolumn{2}{l}{0.03197}\tabcellsep \multicolumn{2}{l}{-5.40624}\tabcellsep \multicolumn{2}{l}{0.002454}\tabcellsep -5.36091\tabcellsep 0.00307\\ \tabcellsep \tabcellsep \multicolumn{8}{l}{Source: Annual Reports of Sample Firms (1996-2015) Note: Data processed on Gretl}\\ \tabcellsep \tabcellsep \tabcellsep \multicolumn{5}{l}{Table A5: Normality Test of Residuals}\tabcellsep \\ \multicolumn{2}{l}{Model No.}\tabcellsep \multicolumn{3}{l}{Kolmogorov-Smirnov}\tabcellsep \multicolumn{2}{l}{Shapiro-Wilk}\tabcellsep \tabcellsep \multicolumn{2}{l}{Chi Square}\\ \tabcellsep \tabcellsep Statistic\tabcellsep df\tabcellsep Sig.\tabcellsep Statistic\tabcellsep df\tabcellsep Sig.\tabcellsep Chi Statistic\tabcellsep P value\\ \multicolumn{2}{l}{D1(FLR-CV)}\tabcellsep .131\tabcellsep 17\tabcellsep .200\tabcellsep .957\tabcellsep 17\tabcellsep .583\tabcellsep 0.475\tabcellsep 0.78845\\ \multicolumn{2}{l}{D2 (FLR-MAD)}\tabcellsep .112\tabcellsep 17\tabcellsep .200\tabcellsep .944\tabcellsep 17\tabcellsep .375\tabcellsep 3.846\tabcellsep 0.14620\\ \multicolumn{2}{l}{M1(FLR-CV)}\tabcellsep 0.100\tabcellsep 17\tabcellsep 0.200\tabcellsep 0.965\tabcellsep 17\tabcellsep .730\tabcellsep 0.812\tabcellsep 0.66615\\ \multicolumn{2}{l}{M2 (FLR-MAD)}\tabcellsep 0.205\tabcellsep 17\tabcellsep 0.055\tabcellsep 0.904\tabcellsep 17\tabcellsep .080\tabcellsep 1.590\tabcellsep 0.45162\end{longtable} \par {\small\itshape [Note: Source: Compiled from Annual Reports (1996-2015) Note: Data processed on SPSS \& Gretl]} \caption{\label{tab_9}Table A4 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{A6} \par \begin{longtable}{P{0.06563706563706563\textwidth}P{0.2176962676962677\textwidth}P{0.09407979407979408\textwidth}P{0.12361647361647361\textwidth}P{0.1072072072072072\textwidth}P{0.12471042471042472\textwidth}P{0.11705276705276704\textwidth}} \tabcellsep \multicolumn{2}{l}{Original value}\tabcellsep \multicolumn{2}{l}{First difference}\tabcellsep \multicolumn{2}{l}{Second difference}\\ Name of variable\tabcellsep ADF Test statistic\tabcellsep P value of test statistic\tabcellsep ADF Test statistic\tabcellsep P value of test statistic\tabcellsep ADF Test statistic\tabcellsep P value of test statistics\\ FLR(CV)\tabcellsep -2.24847\tabcellsep 0.4378\tabcellsep -4.85646\tabcellsep 0.006518\tabcellsep -6.10828\tabcellsep 0.0008648\\ FLR(MAD)\tabcellsep -2.13174\tabcellsep 0.4954\tabcellsep -4.56118\tabcellsep 0.01105\tabcellsep -5.88098\tabcellsep 0.001266\\ TD/TA\tabcellsep -1.7191\tabcellsep 0.7002\tabcellsep -3.24793\tabcellsep 0.1085\tabcellsep -5.70871\tabcellsep 0.001704\\ TD/SE\tabcellsep -1.39829\tabcellsep 0.8255\tabcellsep -2.95371\tabcellsep 0.1719\tabcellsep -5.98867\tabcellsep 0.001056\\ TD/CE\tabcellsep -1.37537\tabcellsep 0.8326\tabcellsep -2.6425\tabcellsep 0.2684\tabcellsep -5.23042\tabcellsep 0.003832\\ NPM\tabcellsep -2.22979\tabcellsep 0.4468\tabcellsep -4.08408\tabcellsep 0.02597\tabcellsep -5.49219\tabcellsep 0.002457\\ EBIT\tabcellsep -0.097632\tabcellsep 0.9902\tabcellsep -4.79511\tabcellsep 0.007271\tabcellsep -6.32426\tabcellsep 0.0006088\\ EPS\tabcellsep -1.69001\tabcellsep 0.7133\tabcellsep -4.42583\tabcellsep 0.01408\tabcellsep -5.7334\tabcellsep 0.001634\\ FS\tabcellsep -1.72944\tabcellsep 0.6954\tabcellsep -6.01077\tabcellsep 0.0008396\tabcellsep -8.80503\tabcellsep 0.0000023\\ SG\tabcellsep -4.14224\tabcellsep 0.02214\tabcellsep -6.32839\tabcellsep 0.0004875\tabcellsep -7.14808\tabcellsep 0.0001\\ \tabcellsep \multicolumn{6}{l}{Source: Compiled from Annual Reports (1996-2015) Note: Data processed on Gretl software}\end{longtable} \par \caption{\label{tab_10}Table A6 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{A7} \par \begin{longtable}{P{0.16024590163934424\textwidth}P{0.2264344262295082\textwidth}P{0.28043032786885247\textwidth}P{0.18288934426229506\textwidth}} \tabcellsep \multicolumn{2}{l}{Model D1(FLR-CV) \& Model D2 (FLR-MAD)}\tabcellsep \\ 2 nd difference\tabcellsep \tabcellsep Measures taken to remove\tabcellsep VIF after removing\\ of Variables\tabcellsep VIF\tabcellsep multicollinearity\tabcellsep multicollinearity\\ NPM\tabcellsep 1.339\tabcellsep \tabcellsep 1.139\\ EPS\tabcellsep 1.802\tabcellsep \tabcellsep 1.668\\ TD/TA\tabcellsep 3.803\tabcellsep \tabcellsep 1.839\\ TD/SE\tabcellsep 218.369\tabcellsep \tabcellsep 1.755\\ EBIT\tabcellsep 1.507\tabcellsep \tabcellsep 1.463\\ TD/CE\tabcellsep 206.825\tabcellsep Variable dropped\tabcellsep \\ SG\tabcellsep 1.628\tabcellsep \tabcellsep 1.611\\ FS\tabcellsep 2.125\tabcellsep \tabcellsep 1.889\\ \tabcellsep \tabcellsep Model M1(FLR-CV) \& Model M2 (FLR-MAD)\tabcellsep \\ EBIT\tabcellsep 31.933\tabcellsep \tabcellsep 5.694\\ SG\tabcellsep 1.894\tabcellsep \tabcellsep 3.591\\ TD/TA\tabcellsep 9.744\tabcellsep \tabcellsep 6.427\\ TD/SE\tabcellsep 74.032\tabcellsep \tabcellsep 3.750\\ FS\tabcellsep 5.141\tabcellsep \tabcellsep 4.095\\ EPS\tabcellsep 63.704\tabcellsep Transformed to reciprocal\tabcellsep 9.012\\ NPM\tabcellsep 23.680\tabcellsep Transformed to reciprocal\tabcellsep 9.585\\ TD/CE\tabcellsep 89.998\tabcellsep Variable dropped\tabcellsep \\ Source:\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par \caption{\label{tab_11}Table A7 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{A8} \par \begin{longtable}{P{0.25717948717948713\textwidth}P{0.1176923076923077\textwidth}P{0.21358974358974359\textwidth}P{0.26153846153846155\textwidth}} Name of the model\tabcellsep No. of observations\tabcellsep LM test statistic\tabcellsep p value of LM test statistic\\ D1(FLR-CV)\tabcellsep 17\tabcellsep 3.558737\tabcellsep 0.828966\\ D2(FLR-MAD)\tabcellsep 17\tabcellsep 4.518053\tabcellsep 0.718542\\ M1(FLR-CV)\tabcellsep 17\tabcellsep 3.377039\tabcellsep 0.848073\\ M2(FLR-MAD)\tabcellsep 17\tabcellsep 4.977143\tabcellsep 0.662753\end{longtable} \par {\small\itshape [Note: Source: Compiled from Annual Reports (1996-2015) Note: Data processed on Gretl software]} \caption{\label{tab_12}Table A8 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{A9} \par \begin{longtable}{P{0.24918154761904762\textwidth}P{0.04300595238095238\textwidth}P{0.09360119047619048\textwidth}P{0.09360119047619048\textwidth}P{0.03415178571428572\textwidth}P{0.08727678571428571\textwidth}P{0.053125\textwidth}P{0.03415178571428572\textwidth}P{0.06956845238095238\textwidth}P{0.09233630952380953\textwidth}} \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Test\tabcellsep P value\\ \multicolumn{2}{l}{Name of the model}\tabcellsep DW Stat\tabcellsep P value of DW\tabcellsep D U\tabcellsep D L\tabcellsep \multicolumn{2}{l}{Decision}\tabcellsep statistic of LM\tabcellsep of LM test\\ D1(FLR-CV)\tabcellsep \tabcellsep 2.5337\tabcellsep 0.8854\tabcellsep 2.5366\tabcellsep 0.4511\tabcellsep \multicolumn{2}{l}{No decision}\tabcellsep 3.2294\tabcellsep 0.11\\ D2(FLR-MAD)\tabcellsep \tabcellsep 2.3306\tabcellsep 0.7924\tabcellsep 2.5366\tabcellsep 0.4511\tabcellsep \multicolumn{2}{l}{No decision}\tabcellsep 2.2971\tabcellsep 0.168\\ M1(FLR-CV)\tabcellsep \tabcellsep 1.9776\tabcellsep .8800\tabcellsep 2.5366\tabcellsep 0.4511\tabcellsep Near 2\tabcellsep \tabcellsep 0.1070\tabcellsep 0.752\\ M2(FLR-MAD)\tabcellsep \tabcellsep 2.2112\tabcellsep .9574\tabcellsep 2.5366\tabcellsep 0.4511\tabcellsep Near 2\tabcellsep \tabcellsep 1.3448\tabcellsep 0.28\\ \multicolumn{9}{l}{Source: Compiled from Annual Reports (1996-2015) Note: Data processed on Gretl software}\\ \tabcellsep \tabcellsep \multicolumn{5}{l}{Table A10: Summary Statistics of the Models}\tabcellsep \\ Model No.\tabcellsep \multicolumn{2}{l}{R square}\tabcellsep \multicolumn{2}{l}{Adj. R square}\tabcellsep \multicolumn{2}{l}{S.E of estimates}\tabcellsep \multicolumn{2}{l}{F statistic}\tabcellsep p value of F\\ D1(FLR-CV)\tabcellsep \multicolumn{2}{l}{0.727780}\tabcellsep \multicolumn{2}{l}{0.516053}\tabcellsep \multicolumn{2}{l}{0.116236}\tabcellsep \multicolumn{2}{l}{3.437354}\tabcellsep 0.044450\\ D2(FLR-MAD)\tabcellsep \multicolumn{2}{l}{0.670269}\tabcellsep \multicolumn{2}{l}{0.413811}\tabcellsep \multicolumn{2}{l}{0.091411}\tabcellsep \multicolumn{2}{l}{2.613568}\tabcellsep 0.090403\\ M1(FLR-CV)\tabcellsep \multicolumn{2}{l}{0.908}\tabcellsep 0.836\tabcellsep \tabcellsep 0.141\tabcellsep \tabcellsep \tabcellsep 12.703\tabcellsep .0005\\ M2(FLR-MAD)\tabcellsep \multicolumn{2}{l}{0.913}\tabcellsep 0.845\tabcellsep \tabcellsep 0.140\tabcellsep \tabcellsep \tabcellsep 13.499\tabcellsep .0004\end{longtable} \par \caption{\label{tab_13}Table A9 :}\end{figure} \footnote{© 20 17 Global Journals Inc. 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