Review of Economics and Development Studies, Vol. 6 (1) 2020, 153-166 153 Abnormal Returns, Corporate Financial Policies and the Dynamics of Leverage: Empirical Evidence from Non-Financial Sector of Pakistan a Kashif Hamid, b Zahid Hussain, c Muhammad Mudasar Ghafoor a Faculty Member, IBMS,University of Agriculture Faisalabad.Email:kashif.boparai@hotmail.com b Faculty Member, FMS, National Textile University, Faisalabad, Email: zahid@ntu.edu.pk c Assistant Professor, University of the Punjab, Jhelum Campus, Jhelum. Email: mudasar@pugc.edu.pk.com ARTICLE DETAILS ABSTRACT History: Accepted 19 March 2020 Available Online 31 March 2020 The aim of this study is to evaluate impact of corporate financial policies and the dynamics of leverage on financial performance of non-financial sector in Pakistan. In this study we used the data from Fertilizer, Chemical and Cement sector for the period 2008-2017. Abnormal return has been taken as dependent variable and Change in cash to lagged market values, Change in EBIT to lagged market values, Change in dividend to lagged market value, Net Financing to lagged market value, Lagged cash values to lagged market values, Lagged cash values to lagged market values crossed by change in cash to lagged market value, Change in total assets net of cash to lagged market values, Change in interest to lagged market values, Operating leverage, Financial leverage, Total leverage, Leverage ratio, Leverage ratio to change in cash crossed by lagged market values and WACC are taken as explanatory variables. OLS, Fixed effect and Random effect models has been used to express the impact of these variables on return. Hence it is concluded that leverage dynamics are significant contributors in designing the corporate financial policies. Corporate financial policies have significant impact on the financial performance of the non-financial sector of Pakistan. © 2020 The authors. Published by SPCRD Global Publishing. This is an open access article under the Creative Commons Attribution- NonCommercial 4.0 Keywords: Financial Leverage, Total Leverage WACC, Fixed Effect, Random Effect, Robust JEL Classification: B26, D53, O47 DOI: 10.47067/reads.v6i1.193 Corresponding author’s email address: kashif.boparai@hotmail.com 1. Introduction Downfall in industrial sector has become critical issue in the economy of Pakistan in recent years. These downfalls may be due to financial policies incorporated by the firms in a traditional manner. In first instance the core domains of leverage policies and its dynamics are discussed in a brief manner. Leverage is used to magnify the level of returns and the optimal level of leverage is a result of high integrated leverage policies of the firm. Leverage dynamics are changing dynamics and still have loop holes in the policies for strengthen the anatomy of the industrial structure of Pakistan economy. In business context the term leverage means a corporate debt used to finance the assets of a firm. Leverage can increase the firm's risk of bankruptcy as well as assist to enhance the abnormal mailto:kashif.boparai@hotmail.com mailto:zahid@ntu.edu.pk mailto:mudasar@pugc.edu.pk.com mailto:kashif.boparai@hotmail.com Review of Economics and Development Studies, Vol. 6 (1) 2020, 153-166 154 returns of the firm. Leverage is classified into three domains as operating leverage, financial leverage and total leverage. Operating leverage is a ratio of fixed cost to variable cost. If a firm has high fixed costs as compared to variable costs then firm has high operating leverage. Operating leverage can also be interpreted as a change in EBIT to change in sales. If the result is greater than one then it means operating leverage exits in the business. Financial leverage means the amount of debt in capital structure of the firm. Huge amount of financial leverage can increase the risk of default and bankruptcy. The financial leverage is computed as percentage change in earning per share divided by the percentage change in EBIT. If the result of ratio is greater than one, then it shows the presence of financial leverage in the business. Total leverage means the total amount of risk faced by a business firm and it is a combination of operating and financial leverage. Total leverage is calculated as percentage change in the earning per share divided by percentage change in sales. Total leverage is a result of multiplication of operating leverage and financial leverage. Further leverage ratio can affect a corporate risk, financing capacity, cost of capital, strategic decisions investment and finally shareholders wealth. Cai and Zhang (2011) used financial leverage ratio to identify the impact on firm stock price. Modigliani and Miller (1958), leverage ratios can also be affected by the many other elements additionally to the issuance of the security and share repurchase, including earnings accumulation, dividend payment, provision or use of trade credit or payment of existing credit line etc. In second instance a brief introduction of corporate financial policies is discussed as adopted by the firms. Such corporate financial polices include cash policies, earning management, dividend policies, asset management, capital structure and cost of capital policies. Cash management includes the sustainability of optimal level of the most liquid item in the current asset. Its change over the span of time indicates the ultimate requirement of the business unit but the excessive amount of cash indicates the precautionary needs. The earning management dilemma indicates the level of earnings after meeting the fixed and variable costs and how various policies regarding to the costing is used to enhance the profitability. Dividend policy may affect the market value behavior over the financial years. Whereas the asset management includes the total size of the firm and its sustainability over the span of time with best performing portfolio of current assets as well as fixed asset and its role in enhancing the returns on asset. Capital structure is the mix of long term debt and equity in the business financing portfolio. The optimal capital structure theory leads to the maximization of earnings before interest and taxes by reducing the weighted average cost of capital. Tangibility, size, profitability, market to book ratios have number of times been reported by different researchers that may affect the corporate financial policies. As Bhatti, Majeed, Rehman and Khan (2010) investigated the impact of leverage on systematic risk and stock returns for the industrial sector of Pakistan. However, Cost of capital is the weighted-average of after tax cost of firm’s long-term debt, common and preferred stock. Cost of capital is the rate of return which could be earned on an investment with the similar risk. It may also be defined from company as well as investor point of view. From company’s point of view, the cost of capital refers to the cost of debt or equity to finance an investment. However from an investor point of view, the cost of capital is the required rate of return that an investment must provide to the business. Cost of capital is used as a bench mark parameter because it is used to evaluate the new projects of a firm, as well as it is the minimum return that investors expect for providing capital to the company. A firm's securities typically include both equity and debt therefore one must calculate the cost of equity and the cost of debt both are necessary to determine a firm's cost of capital. A more important calculation of cost of capital is the (WACC) weighted average cost of capital. The purpose of this study is http://economics.about.com/od/production/ss/The-Costs-Of-Production_4.htm http://www.readyratios.com/reference/debt/financial_leverage.html http://www.accountingcoach.com/blog/what-is-preferred-stock https://www.boundless.com/definition/finance/ https://www.boundless.com/definition/required-return/ https://www.boundless.com/definition/required-return/ https://www.boundless.com/definition/security/ Review of Economics and Development Studies, Vol. 6 (1) 2020, 153-166 155 to investigate the relationship and to model the leverage dynamics and corporate financial policies for the abnormal returns of the firms. In this study we are focusing to investigate the changing dynamics of explanatory variables on the firm’s abnormal returns to optimize the best corporate financial policies for the industrial sector Pakistan. This study will contribute in designing the optimal mix of strategic financial policies to identify the key elements that may magnify the returns level. 2. Literature Review Aivazian, Ge and Qiu (2005) examined the effect of financial leverage on the firm’s investment decisions and concluded that there exist negative association between leverage and h investment decisions. The outcomes support the agency theory for corporate leverage and its disciplinary role for lower growth firms. Dangl and Zechner (2006) investigated that long term debt maturities abolish share holders’ incentives and decrease in leverage leads to poor corporate performance. Whereas short term debt maturity increases shareholders interest due to declines in leverage. However, a short debt maturity increases the transactions costs. They identified the tradeoff between the higher expected transactions costs against the commitment to decrease the leverage when the company is performing poorly. Therefore it motivates towards an optimum maturity structure of the corporate debt. Debt maturity requires the suitable level of financial leverage to reduce the volatility of the cash flows of the firm. They also found that the share holders’ incentives to decrease the debt are non-monotonic in the presence of corporate leverage. If the firm is pressed towards bankruptcy by a determined series of low cash flows, then ownership can start again issuing the debt to refinance the maturity bonds. Abdullah, Aydemir, Gallmeryer and Hollifield (2006) studied the impact of financial leverage on market portfolio of small companies with market risk. In an economy with both constant price of risk and a constant interest rate, financial leverage creates small difference in share return volatility at the level of market but significant deviation at the individual level of the firm. In an economy having more realistic deviation in the price of risk and interest rates, there is a significant variation in the return volatility at firm as well as at market level. In such economies, financial leverage has less impact upon the dynamics of returns volatility at market level. Financial leverage increases more stock returns volatility on small size firm. Maia (2010) investigated the relationship between the expected equity returns, capital structure determinants and the financial leverage of the firms. It is concluded that firms having lower leverage leads to a higher discount-rate of beta and firms with high leverage is related to lower cash-flow of beta. Moreover the key determinants of the corporate capital structures are associated with the firm’s sensitivities due to systematic risk and are significantly important for high and low leveraged firms. The study reveals that short-term shocks are comparatively more important for the firms having low leverage and financial risk is more sensitive to the firm’s cash flow. Bhatti, Majeed, Rehman and Khan (2010) investigated the Cotton, Chemical, Engineering, Sugar, Cement , Fuel & Energy, Communication and transport industries sector and concluded that high level of leverage in these industries cause higher level of systematic risk as well as high volatility in the stock prices. Cai and Zhang (2011) documented a negative and significant impact of change in a firm's leverage ratio on the firm’s stock prices. They found that the negative effect is very stronger for that firms which have higher leverage ratio, face more severe financial constraints and have higher likelihood of default. Moreover, firms which have an increase in the leverage ratio tend to have low future investment. These results are constant with debt overhang theory which tells us that an increase in the leverage may leads to the future underinvestment, thus decrease in the value of a firm. Johnson, Chebonenko, Cunha, D’Almeida and Spencer (2011) examined the endogenous choice conditions of debt which persuades a negative relationship between the leverage and the expected stock returns. Review of Economics and Development Studies, Vol. 6 (1) 2020, 153-166 156 Faulkender, Flannery, Hankins and Smith (2012) explored that the cash flow realizations can provide opportunities to the firm to adjust the leverage at the lower marginal cost. They found that a firm’s cash flows attributes are not only affect the leverage target, but also speed of the adjustment towards those targets. Adjustment speeds is driven by an economical concept of adjustment costs. Further they investigate that how market timing and financial constraints affect the adjustments towards a leverage target. Obreja (2013) suggested a new dynamic model for the corporate sector that associates operating leverage to both book-leverage premium and value premium in stock returns. Due to which the book-leverage premium become negative while the value premium becomes positive. Without the operating leverage, the symbols of both premiums are reversed therefore this model has quantitatively many important for cross-section stock returns. Yarram (2013b) identified positive relation between ownership concentration and leverage. The study used data for 465 firms during the period 2004 to 2010. Frank and Goyal (2015) identified the serious defect in the trade-off theory due to contrary relation between leverage and profitability. Study reveals that the theory is not defected but by applying a leverage ratio in which profitability influence both the numerator and denominator. Firms have taken the offsetting actions for predictions. When profitability increases firms issue debt and repurchase equity, and repay debt and issue equity capital when profitability declines. Consistency with varying transactions costs, to fully undo the profitability shocks; therefore such adjustments are not generally sufficient. However on an average the leverage ratio falls as profitability increases. Ball, Gerakos, Linnainmaa and Nikolaev (2016) reported that adjusted cash flows create a profit measure mainly unchanged by the timing of payments and receipts of cash. They identified that prior research do not cover expected returns which may rise in profitability and fall in accruals. Riccetti, Russo and Gallegati (2016) developed a model which has three main financial accelerators: leverage, stock market and network. The leverage effect shows negative shocks over the firm’s output as a result of banking sector which is less willing for loans. Moreover, firms has less willing for the further investment and therefore the credit reduction more reducing the outputs. Due to the stock market effect lower profit over stock returns which reduces the firm’s capitalization on the stock market. The credit network may be transmitting the initial shock. They concluded that if the stock market is the main indicator of economy then variation in the stock market may destruction real economy. The result has relevant implications for the monetary policy. Teng, Si and Hachiya (2016) analyzed the leverage-return dilemma and observe the returns affect due to bank debts by considering the capital structure heterogeneity and dynamic nature. The relative leverage examined that the returns has strong and positive relationship with leverage. The positive relationship may be partially described in the way that the relative components may negatively forecast the prospect asset growth or may contain considerable information about the future risk. Nadarajah, Ali, Liu and Haung (2016) examined the effect of corporate governance and the stock liquidity over the firm’s leverage decisions in order to drive for the stock trading system and less severe for Australian governance environment. The non-financial sector data is taken for 1207 firms during the period of 2001-2013. The outcomes indicate that the negative stock relationship exist between the liquidity and leverage. The results support the past research, which the firms having more liquid stocks have significantly less leveraged. The outcome also indicates that the significant negative relationship exists between leverage and corporate governance quality, which shows that the firms having high corporate governance quality significantly decrease leverage. Duan et al, (2018) investigated the impact of leverage effect and uncertainty of economic policy Review of Economics and Development Studies, Vol. 6 (1) 2020, 153-166 157 on the future volatility in the regime switching framework. The results of this study indicate that the HAR-RV having the leverage effect and uncertainty of economic policy with regimes can get higher forecast accuracy than GARCH and RV-type models. Moreover the results of this study show that these factors in regime switching framework can substantially increase the performance of HAR-RV’s forecast. Admati et al, (2018) studied the firms’ incapability to commit to future funding choices has thoughtful results for capital structure dynamics. With debt, shareholders resist leverage reductions pervasively without considering how much these reductions may increase the value of firm. The choices of leverage can have the implication for shareholders value. Shareholder value reduced due to new debt threat of bankruptcy and asymmetric behavior. The asymmetric behavior makes leverage adjustment just through arbitraging process. 2.1 Hypothesis H1: Change in cash to lagged market values has a negative impact on abnormal return and has a significant positive relationship with abnormal return. H2: Change in EBIT to lagged market values has a positive impact on abnormal return and has a significant positive relationship with abnormal return. H3: Change in dividend to lagged market values has a positive impact on abnormal return and also has a significant positive relationship with abnormal return H4: Net Financing to lagged market values has a positive impact on abnormal return and also has a significant negative relationship with abnormal return. H5: Lagged cash values to lagged market values has a positive impact on abnormal return and also has a significant positive relationship with abnormal return. H6: Lagged cash values to lagged market values crossed by change in cash to lagged market value has a positive impact on abnormal return and also has a significant negative relationship with abnormal return. H7: Change in total assets net of cash to lagged market values has positive impact on abnormal return and also has a significant positive relationship with abnormal return. H8: Change in interest to lagged market values has negative impact on abnormal return and also has a significant negative relationship with abnormal return. H9: Operating leverage has positive impact on abnormal return and also has a significant negative relationship with abnormal return. H10: Financial leverage has a positive impact on abnormal return and also has significant negative relationship with abnormal return. H11: Total leverage has a positive impact on abnormal return and also has significant negative relationship with abnormal return. H12: Leverage ratio has a positive impact on abnormal return and also has significant negative relationship with abnormal return. H13: Leverage ratio to change in cash crossed by lagged market values has a positive impact on abnormal return and also has significant negative relationship with abnormal return. H14: WACC has a positive impact on abnormal return and also has significant negative relationship with abnormal return. 3. Data and Methodology In this study we used the data for the Chemical, Cement and Fertilizer sector during the period 2008-2017. These sectors consist of 42 companies which are registered at Karachi Stock Exchange (KSE). The data has been taken from the official website of the State bank of Pakistan. We deployed descriptive statistics, correlation, ordinary least squares model, Fixed and Random Effect Models to analyze the data. Review of Economics and Development Studies, Vol. 6 (1) 2020, 153-166 158 3.1 Model Specifications To explore the impact of leverage dynamics and corporate financial policies on abnormal returns the model is identified in this manner. Model: Effect of Leverage Dynamics and Corporate Financial Policies on Abnormal Return. Abnormal Return = f (Change in cash to lagged market values, Change in EBIT to lagged market values, Change in dividend to lagged market value, Net Financing to lagged market value, Lagged cash values to lagged market values, Lagged cash values to lagged market values crossed by change in cash to lagged market value, change in total assets net of cash to lagged market values, Change in interest to lagged market values, Operating leverage, Financial leverage, Total leverage, Leverage ratio, Leverage ratio to change in cash crossed by lagged market values and WACC) Regression Equation for Effect of Leverage Dynamics and Corporate Financial Policies on Abnormal Return: AR i,t = αo + α1 ΔC /Mt-1 + α2 ΔEBIT /Mt-1+ α3 ΔD /Mt-1 + α4 NF /Mt-1 + α5 Ct-1 /Mt-1 + α6 [Ct-1 /Mt-1]×[ ΔC/ Mt-1] + α7 ΔNA /Mt-1 α8 ΔI /Mt-1 + α9 DOL + α10 DFL + α11 DTL + α12 LR + α13 LR /[ ΔC × Mt-1] + α14 WACC + ὲ (1) Whereas, AR = Abnormal Return, ΔC /Mt-1 = Change in cash to lagged market values, ΔEBIT /Mt-1 = Change in EBIT to lagged market values, ΔD /Mt-1 = Change in dividend to lagged market values, NF /Mt-1 = Net Financing to lagged market values, Ct-1 /Mt-1 = Lagged cash values to lagged market values, [Ct-1 /Mt-1] × [ΔC/ Mt-1] = Lagged cash values to lagged market values crossed by change in cash to lagged market values, ΔNA /Mt-1 = Change in total assets net of cash to lagged market values, ΔI /Mt-1 = Change in interest to lagged market values, DOL = Operating Leverage, DFL = Financial Leverage, DTL = Total Leverage, LR = Leverage Ratio LR / [ΔC × Mt-1] = Leverage ratio to change in cash crossed by lagged market values, WACC = Weighted average cost of capital Simple linear model in a static level is expressed in the manner below: h Cross section regression will produce a biased estimate of beta coefficient if there exist correlation between and xit. Therefore it is necessary to identify whether the unobserved individual effects are random or fixed. There exist two basic methods for this model. The fixed effects method take to be a group specific constant regarding to the regression model. On the other hand random effects method specifies that is a group specific disturbance. 3.1 Fixed Effects Model Fixed effects model indicates a constant slope but differ in intercepts in comparison to cross- sectional firms. Fixed effect model controls the potential correlation between the regressors and Review of Economics and Development Studies, Vol. 6 (1) 2020, 153-166 159 unobservable individual effects. However the fixed effects method takes to be a group specific constant term in regression model. Fixed effect model is narrated as below: In the case of the presence of fixed effects, β and can be estimated consistently and efficiently by the Within Groups estimators (WG) attained from OLS equation. Ȳ = ∑ : ̅ =∑ and = ∑ Then, (3) Subtracting this equation from equation to get: ̅ (4) ̅ Pooled ordinary least square can be used to the transformed model to estimate β in a natural phenomenon and (WG) can also be used to eliminate any time-invariant variable in this model. It is because that is taken as fixed constant, and the estimator of β is known as (βwg). [∑ ∑ ̅ ̅ ] [∑ ∑ ̅ ̅ ] (5) The fixed effect estimators are given by: ̂ = ̅ β ̂ (6) One of the biggest advantages of the FE model is that the error terms may be related with the individual effects of the model. On the other hand if the group effects are unrelated with group means of the regressors, it is better to apply a thriftier parameterization of the panel model. 3.2 Random Effect Model The random effects model is a regression equation with a random constant term. A specific effect is visualized as an outcome of a random variable. In simple terms, a static random effects model may be explained as below. β ` (7) (8) Where is independent and identical distribution such that: ( ( -E ( = { } -E ( ) = { } ) = 0 (9) Review of Economics and Development Studies, Vol. 6 (1) 2020, 153-166 160 The appropriate GLS estimator of β shows that the random estimator, given by is consistent. 4. Results and discussion 4.1 Descriptive Statistics Table 1 indicates that the returns are positive i.e. 0.005 in chemical sector along with 1.319 standard deviation. Whereas Table 2 indicates that returns are -0.0022 along with 1.22234 standard deviation. More over Table 3 indicates also negative abnormal returns which are -0.0106 in fertilizer sector along with 1.2234 standard deviation. So the return behavior is different in each sector. It can be visualized in the figure 1. This figure indicates that cement sector is more growing sector in Pakistan and providing high abnormal returns that chemical and fertilizer sector. However each sector has now streamlined the abnormal returns in positive domain. Figure 1: Average Abnormal Returns Table 1 Descriptive Statistics of Chemical Sector AR CCMt CEBItMt CDMt NFMt CtMt CtMtCCMt CACMt CIMt OL FL TL LR LRCCMt WACC Mean 0.005 0.135 0.097 0.081 835991.625 79634.6647 17047.49194 0.02752 0.38509 -199.06216 -18.463 -3578.0595 0.172 -16.289 254396.87 Standard Error 0.090 0.056 0.065 0.080 187291.567 21546.0215 20786.34266 0.01190 0.31671 200.90633 13.679 3572.2219 0.015 14.781 36812.807 Median -0.085 0.000 0.000 0.000 30106.0943 653.20 0 0.00049 0.00000 0.38442 0.445 0 0.068 0.000 32089.832 Mode -2.050 0.000 0.000 0.000 0 0 0 0.00000 0.00000 0.00000 0.000 0 0.000 0.000 0 Standard Deviation 1.319 0.822 0.955 1.181 2758977.07 317392.717 306201.948 0.17527 4.66544 2959.53509 201.506 52622.115 0.224 217.742 542286.51 Sample Variance 1.738 0.676 0.911 1.396 7.61195E+1 1.00738E+1 937596329.8 0.03072 21.76630 8758847.925 40604.6 27690870.ZZ 0.050 47411.480 2.9407E+11 Kurtosis 80.212 126.361 148.841 204.311 55.15645097 42.63340811 69.13406489 33.61051 199.86800 216.98738 101.610 216.993086 1.042 61.621 47.3618597 Skewness 7.292 10.368 11.237 14.089 6.508652344 6.170453969 -0.233987248 2.77219 13.94168 -14.73028 -9.973 -14.7305703 1.388 -6.677 5.65140206 Range 17.511 11.712 15.269 18.259 29077150.47 2720052.5 5630668.75 2.53875 69.80553 43733.69129 2443.378 775677.968 1.000 2781.803 5652902.88 Minimum -2.436 -1.078 -2.433 -1.111 0 0 -2932362.5 -1.14523 -2.40489 -43594.20751 -2213.652 -775171.945 0.000 -2136.977 0 Maximum 15.075 10.634 12.836 17.148 29077150.47 2720052.5 2698306.25 1.39351 67.40064 139.48378 229.726 506.023039 1.000 644.826 5652902.88 Sum 1.135 29.215 20.976 17.491 181410182.7 17280722.25 3699305.75 5.97237 83.56393 -43196.48861 -4006.407 -776438.92 37.361 -3534.614 55204121.5 Count 217 217 217 217 217 217 217 217 217 217 217 217 217 217 217 Table 2 Descriptive Statistics of Cement Sector AR CCMt CEBItMt CDMt NFMt CtMt CtMtCCMt CACMt CIMt OL FL TL LR LRCCMt WACC Mean -0.0022 0.3485 0.1482 466.5387 2314056.378 68134.95794 45701.66902 0.0123 15.9685 3.6409 -26.6954 -31.2389 0.2513 -8.0480 322330.023 Standard Error 0.1090 0.1812 0.0936 431.7426 487538.3145 27402.81013 61236.35743 0.0042 15.8921 1.3569 29.6055 30.0958 0.0269 9.6120 48541.7605 Median -0.3200 0.0000 0.0023 0.0000 352285.5728 6628.850982 0 0.0024 0.0023 0.7454 0.0000 -0.0261 0.2399 0.0000 101714.839 Mode -0.3300 0.0000 0.0000 0.0000 0 0 0 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0 Standard Deviation 1.2234 2.0338 1.0501 4846.2988 5472604.008 307595.7808 687376.4074 0.0473 178.388 15.2312 332.3208 337.8246 0.3016 107.8947 544879.91 Sample Variance 1.4967 4.1365 1.1028 23486611.910 2.99494E+13 94615164390 4.72486E+11 0.0022 31822.3 231.99 110437.1 114125.48 0.0910 11641.269 2.9689E+11 Kurtosis 12.385 74.052 94.6760 124.8990 22.11551124 103.042707 78.65718542 45.9330 125.993 36.3703 123.3702 114.8071 5.8413 42.1837 9.26285538 Skewness 3.0350 8.0927 9.1795 11.1547 4.168285445 9.755675658 7.183158924 5.7494 11.2245 5.6346 -11.0446 -10.5172 -1.3280 -4.7545 2.91946824 Range 8.4507 21.595 12.5559 54319.0443 41214663.45 3329072.727 9765029.545 0.5211 2003.59 141.6478 3983.85 3944.6518 2.2498 1224.6454 2976581.32 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3 3.5 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 Cement Chemical Fertilizer Review of Economics and Development Studies, Vol. 6 (1) 2020, 153-166 161 Minimum -1.3312 -1.4756 -1.5068 -0.2564 -529875.455 0 -2966880.165 -0.1011 -1.14 -22.3630 -3708.33 -3708.333 -1.2667 -914.1729 -84366.775 Maximum 7.1195 20.119 11.0492 54318.7879 40684788 3329072.727 6798149.38 0.4200 2002.45 119.2849 275.52 236.3185 0.9832 310.4725 2892214.55 Sum -0.2760 43.911 18.6671 58783.8727 291571103.6 8585004.7 5758410.297 1.5455 2012.03 458.7485 -3363.62 -3936.099 31.662 -1014.049 40613582.9 Count 126 126 126 126 126 126 126 126 126 126 126 126 126 126 126 Table 3 Descriptive Statistics of Fertilizer Sector AR CCMt CEBItMt CDMt NFMt CtMt CtMtCCMt CACMt CIMt OL FL TL LR LRCCMt WACC Mean -0.0106 0.0074 0.0556 0.0043 636569.59 85491.74 -4641.98 0.0012 0.44674 1.0000 16.679 0.4337 0.1955 8.1098 3763733.43 Standard Error 0.0813 0.0104 0.0540 0.0024 188844.42 42562.61 3544.572 0.0015 0.44785 0.2501 17.529 0.7746 0.0299 16.0096 822090.907 Median -0.0980 0.0000 0.0000 0 74466.2349 1378.679 0 0 0 0.6316 0.9577 0.6162 0.1577 0 2523845.56 Mode -0.1900 0.0000 0.0000 0 0 0 0 0 0 0.0000 0 0 0 0 0 Standard Deviation 0.4945 0.0630 0.3286 0.0147 1148695.77 258898.2 21560.79 0.0089 2.72417 1.5211 106.62 4.7117 0.1821 97.3824 5000583.76 Sample Variance 0.2446 0.0040 0.1080 0.0002 1.3195E+12 6.7E+10 4.65E+08 8E-05 7.42112 2.3137 11369 22.2 0.0332 9483.34 2.5006E+13 Kurtosis 0.7947 26.4220 36.8374 20.638 3.30020663 15.50172 26.55863 14.797 36.9977 5.9476 36.442 28.054 -0.149 18.6577 2.64903433 Skewness 0.7442 4.5656 6.0634 4.2602 2.02155328 4.020568 -5.04952 0.756 6.08249 2.0174 6.0126 -4.9705 0.8516 3.05205 1.77956574 Range 2.2535 0.4673 2.0473 0.086 4234412.48 1224904 125131.5 0.0718 16.6428 8.1942 693.27 29.882 0.6176 748.286 17925004.3 Minimum -0.9606 -0.1160 -0.0487 -0.0057 0 0 -122346 -0.0325 -0.0736 -1.126 -47.77 -25.741 0 -256.008 0 Maximum 1.2929 0.3513 1.9986 0.0803 4234412.48 1224904 2785.509 0.0393 16.5692 7.0676 645.51 4.1406 0.6176 492.278 17925004.3 Sum -0.3908 0.2726 2.0569 0.159 23553074.8 3163194 -171753 0.043 16.5296 36.999 617.14 16.049 7.2331 300.063 139258137 Count 37 37 37 37 37 37 37 37 37 37 37 37 37 37 37 4.2 Correlation Matrix Table 4 indicate that in chemical sector net financing to lagged market value has r = 0.21 with abnormal return and lagged cash values to lagged market values crossed by change in cash to lagged market value r = 0.17 to abnormal return and lagged cash values to lagged market values has highly significant relationship of 0.76 to abnormal return. Table 5 indicates that change in dividend to lagged market value has 0.31 degree of correlation to abnormal return. Net financing to lagged market value has 0.25 degree of relationship to abnormal return. Table 4 Correlation Matrix for Chemical Sector AR CCMt CEBItMt CDMt NFMt CtMt CtMtCCMt CACMt CIMt OL FL TL LR LRCCMt WACC AR 1.00 CCMt 0.01 1.00 CEBItMt 0.09 0.03 1.00 CDMt -0.03 0.01 0.00 1.00 NFMt 0.22 0.01 0.03 0.08 1.00 CtMt 0.06 0.02 0.07 0.06 0.66 1.00 CtMtCCMt 0.18 0.23 0.07 0.04 0.08 0.03 1.00 CACMt -0.03 0.06 0.22 0.03 0.32 0.66 -0.07 1.00 CIMt 0.76 0.07 0.06 -0.02 0.31 0.29 0.33 0.26 1.00 OL -0.07 -0.07 0.01 0.00 -0.04 0.01 0.00 -0.01 0.01 1.00 FL -0.09 -0.04 0.01 0.01 -0.03 -0.02 0.01 0.01 0.01 -0.01 1.00 TL -0.07 -0.07 0.01 0.00 -0.04 0.01 0.00 -0.01 0.01 1.00 -0.01 1.00 LR -0.04 0.01 -0.03 -0.05 0.06 -0.08 -0.06 -0.07 -0.06 0.05 0.01 0.04 1.00 LRCCMt 0.00 0.01 0.01 0.01 0.05 0.03 0.00 0.01 0.01 -0.01 0.02 -0.01 -0.10 1.00 WACC 0.06 0.02 -0.03 0.11 0.63 0.22 0.00 0.02 0.02 0.01 -0.11 0.01 0.21 0.09 1.00 Significant at 5% level Table 5 Correlation Matrix for Cement Sector AR CCMt CEBItMt CDMt NFMt CtMt CtMtCCMt CACMt CIMt OL FL TL LR LRCCMt WACC AR 1.00 CCMt 0.10 1.00 CEBItMt 0.00 0.00 1.00 CDMt 0.31 -0.02 -0.02 1.00 NFMt 0.25 0.29 0.21 0.00 1.00 CtMt 0.06 0.04 0.89 0.02 0.37 1.00 CtMtCCMt 0.06 0.89 -0.34 -0.01 0.17 -0.29 1.00 CACMt -0.15 0.15 0.31 -0.06 0.16 0.36 0.04 1.00 CIMt -0.06 0.02 -0.01 -0.01 -0.03 -0.02 -0.01 0.01 1.00 OL -0.05 -0.03 0.11 -0.01 0.01 0.03 -0.03 0.32 -0.02 1.00 FL -0.03 0.06 0.01 0.01 0.03 0.02 0.05 0.04 0.01 0.01 1.00 TL 0.00 0.06 0.01 0.02 0.02 0.02 0.05 -0.08 0.01 0.01 0.97 1.00 LR 0.03 -0.13 -0.43 -0.05 0.02 -0.42 0.06 -0.21 -0.04 -0.05 -0.01 -0.01 1.00 LRCCMt 0.04 0.01 0.01 0.01 0.06 0.02 0.00 -0.01 0.01 0.02 0.31 0.31 0.04 1.00 WACC 0.05 -0.04 0.32 -0.01 0.27 0.37 -0.13 0.07 -0.04 -0.04 0.03 0.04 0.01 0.00 1.00 Significant at 5% level Table 6 indicate that net financing to lagged market value has positive relationship to abnormal Review of Economics and Development Studies, Vol. 6 (1) 2020, 153-166 162 return in chemical sector with r = 0.37 significant at p = 0.05 and change in total assets net of cash to lagged market value also has -0.41 significant at p < 0.05 negative relationship to abnormal return. More over operating leverage has positive relationship to abnormal and financial leverage and leverage ratio to change in cash crossed by lagged market values has negative as well as total leverage and weighted average cost of capital has positive degree of relationship to abnormal return. 4.2 Regression Models Table 7 indicates that change in total assets net of cash to lagged market value has significant negative impact on abnormal return at p < 0.01 as expressed by OLS, Fixed effect as well as in random effect behavior. In fertilizer only fixed effect exist at p < 0.1 on abnormal return. Change in dividend to lagged market value has positive significant impact on abnormal return at p = 0.01 in cement sector in each model but in chemical sector OLS and random effect model has negative impact whereas fixed effect has positive effect. Lagged cash values to lagged market has positive impact at p = 0.01 in chemical sector. Lagged cash values to lagged market values crossed by change in cash to lagged market value has significant impact in chemical sector but fertilizer sector is visualized in OLS and random effect at p = 0.10. Financial leverage has negative impact on fertilizer sector at p = 0.1 and in chemical sector p < 0.05. Net Financing to lagged market value has positive impact p < 0.05 in cement sector and in fertilizer sector but no impact in chemical sector. Operating Leverage has positive significant impact p < 0.01 in fertilizer sector only. R2 values indicate that these variables are significant explanatory variable in fertilizer and chemical sectors. Table 6 Correlation Matrix of Fertilizer Sector AR CCMt CEBITMt CDMt NFMt CtMt CtMtCCMt CACMt CIMt OL FL TL LR LRCCMt WACC AR 1.00 CCMt -0.09 1.00 CEBItMt 0.01 0.93 1.00 CDMt 0.00 -0.26 -0.03 1.00 NFMt 0.38 -0.06 0.17 0.59 1.00 CtMt 0.17 -0.33 -0.04 0.89 0.69 1.00 CtMtCCMt -0.34 0.38 0.07 -0.64 -0.67 -0.88 1.00 CACMt -0.42 0.12 0.05 0.40 0.04 0.13 0.32 1.00 CIMt 0.02 0.92 1.00 -0.05 0.16 -0.05 0.06 0.01 1.00 OL 0.19 -0.10 -0.10 0.22 -0.02 0.19 -0.10 0.18 -0.11 1.00 FL -0.09 -0.02 -0.03 -0.03 -0.07 -0.02 0.03 0.01 -0.03 -0.10 1.00 TL 0.10 -0.02 -0.01 0.07 0.08 0.06 -0.06 0.00 -0.02 0.36 -0.92 1.00 LR 0.11 0.32 0.34 0.03 0.49 0.02 0.03 0.12 0.33 0.06 -0.02 0.08 1.00 LRCCMt -0.10 0.02 -0.01 0.00 -0.13 -0.03 0.03 0.00 -0.01 -0.03 0.83 -0.80 0.06 1.00 WACC 0.18 -0.23 -0.04 0.43 0.52 0.60 -0.57 -0.03 -0.04 0.03 -0.06 0.09 0.05 -0.29 1.00 Significant at 5% level Table 7 OLS, Fixed Effect and Random Effect in Cement, Fertilizer and Chemical Sector Cement Fertilizer Chemical OLS Fixed effect Random Effect OLS Fixed Effect Random Effect OLS Fixed effect Random Effect C -0.165262 -0.220954 -0.165262 -0.143758 -0.210643 -0.143758 -0.05613 -0.094081 -0.05613 p-value 0.3204 0.2594 0.3366 0.3049 0.3111 0.3174 0.4249 0.2548 0.4451 CACMT -4.405112 -3.899884 -4.405112 -42.7069 -48.47508 -42.7069 -2.080546 -1.853921 -2.080546 p-value 0.141 0.2374 0.1545 0.1145 0.0968* 0.1237 0.00*** 0.0001*** 0.00*** CCMT 0.157796 0.137478 0.157796 -2.560336 -2.16734 -2.560336 -0.026715 -0.00958 -0.026715 p-value 0.3494 0.4486 0.3655 0.6648 0.7268 0.673 0.6825 0.8935 0.6955 CDMT 0.0000763 0.000085 0.0000763 -8.070432 -7.503018 -8.070432 -0.003843 0.009267 -0.003843 p-value 0.0006*** 0.0005*** 0.0009*** 0.631 0.6707 0.6399 0.9309 0.8498 0.9339 CEBITMT -0.188717 -0.187192 -0.188717 -18.28941 -8.571451 -18.28941 0.157125 0.164571 0.157125 p-value 0.4399 0.4638 0.4552 0.494 0.8178 0.5052 0.0062*** 0.0087*** 0.0088*** CIMT -3.74E-04 -3.97E-04 -3.74E-04 2.244321 1.065661 2.244321 0.247282 0.247262 0.247282 p-value 0.5229 0.5318 0.5368 0.4827 0.8108 0.4941 0.00*** 0.00*** 0.00*** CTMT 4.46E-07 4.41E-07 4.46E-07 3.03E-06 0.00000221 0.00000303 -2.56E-07 2.59E-07 -2.56E-07 p-value 0.6154 0.6583 0.6272 0.1843 0.5471 0.1956 0.3795 0.5034 0.4004 CTMTCCMT -3.97E-07 -3.41E-07 -0.00000039 0.0000449 0.0000379 0.0000449 -6.01E-07 -4.05E-07 -6.01E-07 p-value 0.4483 0.5446 0.4634 0.0757 0.3289 0.0831* 0.0016*** 0.0609** 0.0026*** FL -0.00073 -0.000429 -0.00073 -0.004499 -0.005902 -0.004499 -0.000617 -0.000634 -0.000617 p-value 0.5987 0.7725 0.6108 0.0687*** 0.0719*** 0.0757*** 0.0183** 0.027** 0.0239** LR 0.096748 -0.029828 0.096748 -0.61518 -0.44282 -0.61518 -0.114929 -0.199994 -0.114929 p-value 0.8115 0.9494 0.8176 0.4208 0.6482 0.4329 0.6481 0.553 0.6623 LRCCMT 3.90E-04 3.16E-04 3.90E-04 0.000203 0.000364 0.000203 -0.000069 -0.000045 -0.0000696 Review of Economics and Development Studies, Vol. 6 (1) 2020, 153-166 163 p-value 0.7008 0.7796 0.7103 0.9191 0.8621 0.9212 0.7726 0.8607 0.7822 NFMT 5.2E-08 8.29E-08 0.000000052 4.25E-07 4.45E-07 0.000000425 2.81E-08 2.39E-08 2.81E-08 p-value 0.0268** 0.006*** 0.0322** 0.0096*** 0.0389** 0.0114* 0.421 0.5722 0.4413 OL 0.001549 0.000771 0.001549 0.217091 0.211024 0.217091 -0.000619 -0.000968 -0.000619 p-value 0.8379 0.925 0.8432 0.0052 0.0128 0.0063 0.8043 0.7199 0.8126 TL 5.38E-04 2.43E-04 5.38E-04 -0.105136 -0.136382 -0.105136 0.0000328 0.0000524 0.0000328 p-value 0.6955 0.8677 0.7051 0.1042 0.1023 0.113 0.8155 0.7299 0.8233 WACC -4.97E-09 3.16E-08 -4.97E-09 -1.13E-08 1.14E-08 -1.13E-08 6.39E-08 7.24E-08 6.39E-08 p-value 0.9814 0.9176 0.982 0.6556 0.7965 0.664 0.6426 0.6992 0.657 R-squared 0.208216 0.256934 0.208216 0.59091 0.61797 0.59091 0.690325 0.705565 0.690325 Adjusted R 2 0.108352 0.052212 0.108352 0.318184 0.257164 0.318184 0.668756 0.644362 0.668756 Cross Section 14 14 14 4 4 4 24 24 24 Observations 126 126 126 36 36 36 216 216 216 ***Significant at 1% level, **Significant at 5% level, * Significant at 10% level 5. Conclusion The industrial fall has become the most critical issue in the economy of Pakistan in the present era. These downfalls may be due to financial policies incorporated by firms in a traditional manner. Even researchers are trying to investigate the basic determinants which can affect the financial policy of firms in the industrial sector of Pakistan. The purpose of this study is to investigate the relationship and to model the leverage dynamics and corporate financial policies for abnormal return of the firms. This study has taken 10 years balanced panel data for the period 2008-2017 from chemical, cement and fertilizer sector of Pakistan. Moreover in fertilizer sectors negative abnormal returns exist. In this study we are focusing to investigate the changing dynamics of explanatory variables on the firm’s abnormal returns to optimize the best corporate financial policies for the industrial sector Pakistan. Results conclude that chemical sector is more growing sector in Pakistan and providing high abnormal returns than cement and fertilizer sector. Results indicate that the returns are positive in chemical sector and in Cement and fertilizer sector returns are negative. Correlation indicates that in chemical sector net financing to lagged market value has positive correlation with abnormal return and lagged cash values to lagged market values crossed by change in cash to lagged market value has little positivity to abnormal return and lagged cash values to lagged market values has highly significant relationship to abnormal return. Further results for Cement Sector indicate that change in dividend to lagged market value has positive degree of correlation to abnormal return and net financing to lagged market value has positive degree of relationship to abnormal return. Maia (2010) argued in the same manner. For Fertilizer Sector results indicates that net financing to lagged market value has positive relationship to abnormal return and change in total assets net of cash to lagged market value also has negative significant relationship to abnormal return. The study of Dangl and Zechner (2006), Abdullah, Aydemir, Gallmeryer and Hollifield (2006) Faulkender, Flannery, Hankins and Smith (2012) also support our argument as well. Moreover operating leverage has positive relationship to abnormal return leverage ratio to change in cash crossed by lagged market values has negative as well as total leverage and weighted average cost of capital has positive degree of relationship to abnormal return. Aivazian, Ge and Qiu (2005), Cai and Zhang (2011), Johnson, Chebonenko, Cunha, D’Almeida and Spencer (2011) and Obreja (2013) also support this last argument. Further results indicates that change in total assets net of cash to lagged market value has significant negative impact on abnormal return as expressed by OLS, Fixed effect as well as in random effect behavior. In fertilizer only fixed effect exist on abnormal return. Change in dividend to lagged market value has positive significant impact on abnormal return in cement sector in each model but in chemical sector OLS and random effect model has negative impact whereas fixed effect has positive effect. A lagged cash value to lagged market has positive impact in chemical sector. Lagged cash values to lagged market values crossed by change in cash to lagged market value has significant impact in chemical sector but fertilizer sector is visualized in OLS and random effect . Financial leverage has negative impact on fertilizer sector and in chemical sector and our study support to the facts of Riccetti, Russo Review of Economics and Development Studies, Vol. 6 (1) 2020, 153-166 164 and Gallegati (2016). Net Financing to lagged market value has positive impact in cement sector and in fertilizer sector but no impact in chemical sector. Operating Leverage has positive significant impact in fertilizer sector only as results reported by Frank and Goyal (2015). R2 values indicate that these variables are significant explanatory variable in fertilizer and chemical sectors. H1 is rejected it is concluded that change in cash to lagged market values has no negative impact on abnormal return and have no significant positive relationship with abnormal return and in the same manner H2 is rejected that change in EBIT to lagged market values has no positive impact on abnormal return and no significant positive relationship with abnormal returns. However H3 Change in dividend to lagged market values has a positive impact on abnormal return in cement sector only and also has a significant positive relationship with abnormal return in cement sector only but the same is hypothesis is rejected for other sectors. H4 results indicate that Net Financing to lagged market values has no impact in chemical sector but has a positive significant impact in cement and fertilizer sectors on abnormal return. According to H5 Lagged cash values to lagged market values has a positive impact on abnormal return and also has a significant positive relationship with abnormal return has highly significant relationship to abnormal return in chemical sector as well as H5 has a positive impact on abnormal return in chemical sector only. However results for H6 indicates that lagged cash values to lagged market values crossed by change in cash to lagged market value has a positive impact on abnormal return and also has a significant negative relationship with abnormal return has a little positive correlation to abnormal return in chemical sector as well as has a significant impact in chemical sector but fertilizer sector is visualized in OLS and Random Effect Model only. According to H7 Change in total assets net of cash to lagged market values has positive impact on abnormal return and also has a significant positive relationship with abnormal return has negative significant relationship to abnormal return in fertilizer sector as well as has a significant negative impact on abnormal return as expressed by OLS, Fixed effect and Random effect models but in fertilizer sector only fixed effect exist on abnormal return. However, H8 indicates that Change in interest to lagged market values has negative impact on abnormal return and also has a significant negative relationship with abnormal return is rejected in all sectors. As per H9 results it is concluded that Operating leverage has positive impact on abnormal return and also have a significant negative relationship with abnormal return has a positive relationship to abnormal return and also has a significant positive impact on abnormal return in fertilizer sector only but according to H10 Financial leverage has a positive impact on abnormal return and also has significant negative relationship with abnormal return has a negative correlation with abnormal return in all sectors and also has a negative impact on abnormal return in fertilizer and chemical sectors. H11 is rejected for other sectors except fertilizer sector. H12 indicates that Leverage ratio has a positive impact on abnormal return and also has significant negative relationship with abnormal return but rejected in all sectors. However H13 indicates that leverage ratio to change in cash crossed by lagged market values has a positive impact on abnormal return and also has significant negative relationship with abnormal return fertilizer sector only. According to H14 the WACC has a positive impact on abnormal return and also has significant negative relationship with abnormal return is rejected for fertilizer sector where the results are in negative domain. Hence it is inferred from this study that modeling the leverage dynamics and corporate financial policies significantly contribute in the firm performance. Hence it is concluded that leverage dynamics are significant contributors in designing the corporate financial policies and have significant impact on the financial performance of the non-financial sector of Pakistan. The practical implication of this study reveals that it is guiding to the industrial policy makers and to the corporate financial managers in developing strategic plans for Non –Financial Sector of Pakistan. Review of Economics and Development Studies, Vol. 6 (1) 2020, 153-166 165 References Abdullah, C., Aydemir, M., Gallmeryer, F. and Hollifield, B. (2006). Financial leverage does not cause the leverage effect. AFA 2007 Chicago Meeting Paper. Admati,A., Demarzo, P,M., Hellwig, F, M., and Pfleidered, P. (2018). The leverage ratchet effect. The Journal of Finance,.Vol. LXXIII, No. 1. Aivazian, V., Ge, Y. and Qiu, J. (2005). The impact of leverage on firm investment: Canadian evidence. Journal of Corporate Finance.11(1), 277-291. Ball, R., Gerakos, J., Linnainmaa, J. T. and Nikolaev, V. (2016). Accurals, cash flows, and operating profitability in the cross section of stock returns. Journal of Financial Economics. 121(1), 28–45. Bhatti, A. M., Majeed, K., Rehman, I. and Khan, W. A. 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Ownership and financial leverage: Australian evidence. The Asia Pacific Journal of Economics & Business. 17, 13-2 http://www.sciencedirect.com/science/journal/0304405X/121/1 http://www.sciencedirect.com/science/journal/10590560/43/supp/C Review of Economics and Development Studies, Vol. 6 (1) 2020, 153-166 166 Abbreviations to variables AR Abnormal Return CCMt Change in cash to lagged market values CEBItMt Change in EBIT to lagged market values CDMt Change in dividend to lagged market values NFMt Net Financing to lagged market values CtMt Lagged cash values to lagged market values CtMtCCMt Lagged cash values to lagged market values crossed by change in cash to lagged market values CACMt Change in total assets net of cash to lagged market values CIMt Change in interest to lagged market values OL Operating Leverage FL Financial Leverage TL Total Leverage LR Leverage Ratio LRCCMt Leverage ratio to change in cash crossed by lagged market values WACC Weighted average cost of capital