Financial Conservatism. Determinants of cash and leverage - Dissertation Example

? Financial Conservatism: Determinants of cash and leverage conservatism in UK firms and Contents Introduction 3 Literature review 5 Introduction 5 Financial flexibility motive and agency theory 6 Ownership structure 9 Empirical Framework and Data Description: 11 The model for leverage conservatism 11 Data description 11 Low Leverage model 12 The whole sample 13 Sub samples 13 Test of equality 18 Correlation matrix (whole sample) 20 Conclusion 23 Financial Conservatism: Determinants of cash and leverage conservatism in UK firms Introduction The meaning of the term financial conservatism can be understood in different contexts, depending on the variable used in categorizing the financially conservative entities. Many of the studies that have been conducted in this area classify this concept using either cash holdings or leverage of a firm. For a case in point, Mikkelson and Partch (2003) provide that financially conservative firms are those that continually hold huge cash balances to finance their financial activities, meaning that they hardly rely on borrowed capital to finance their investment opportunities. A firm that holds large cash balances essentially means the one that holds more than 25% of its assets in cash and cash equivalents for five consecutive years. Elsewhere, Minton and Wruck (2001) defines a financially conservative firm as the one that continually assumes low leverage policy; that is, if for five consecutive years its total debt to total asset ratio is categorized in the first 20% of all entities in a particular category. In this analysis, both cash conservative and leverage conservative firms will be taken into account. The essence of investigation of the two policies at the same is to find out if a firm can use leverage conservative policy in the place of cash conservative policy or vice versa. From the earlier analysis, the key reason why firms are motivated to adopt conservative financial policies is to protect them from the cost that is associated with missed out investment opportunities. The analysis of the literature will reveal that firms tend to maintain large cash reserves or do away with their debt capacity to make sure that they do not miss out investment opportunities. It is not rare to find firms exercising both policies simultaneously, though it is difficult to establish the reasons why they do so. All in all, according to major theoretical frameworks of capital structure, it is strange to find a firm that adopts high leverage policy having high cash balances in their capital structure. For instance, according to the pecking order theory, firms tend to result to eternal financing only after exhausting their internally available funds. Many researchers have covered this area of financial conservatism, especially regarding the rationale behind different accumulation of huge amounts of cash and cash equivalents as well as the repercussions of such policies (Ozkan & Ozkan, 2004; Mikkelson & Partch, 2003). However, the determinant of cash and leverage conservatism in UK firms has not been focused on. What’s more, most of the studies that have dealt with this area have been concentrated on the US firms, hence making it important to investigate whether the puzzle regarding decisions of firms in relation to financial conservatism extends to the UK firms. As such, this study will focus on the UK firms especially because it is commonly known for observance of extreme debt conservatism, considering UK firms have the lowest leverage ratio, weighed against to other developed countries (Rajan and Zingales, 1995). This study will use leverage of firms or cash holdings to determine whether the firms are finically-conservative. The objective of this paper is to carry out an empirical study on the debt policies adopted by the UK firms, which particularly focus on the factors that influence large cash reserves and extremely low leverage. In effect, the paper will attempt to find answers to a number of research questions. First and foremost, I will seek to know what motivates firms to use ultra-low leverage and high cash reserves. Second, I will want to find the features of different firms that maintain low leverage and high cash reserves, and especially establish possible commonalities. Third, I will investigate the factors that influence firms to shift from a less conservative policy to a more conservative policy, whereby they tend to use very low leverage in their capital structure. Fourth, I will seek to establish what causes firms with low leverage in their capital structure to opt for a less conservative policy by using more debt to finance their investments. In order to adequately find answers to the above questions, a number of explanations will be drawn from theories of capital structure. For example, an investigation will be carried out to establish if firms tend to use low leverage in their capital structure when they are experiencing cash problems and when they are not able to access finance from their lenders (Faulkender & Petersen 2006). Additionally, the pecking order theory , which asserts that firms do not use leverage capital to finance new investments when they have enough funds from their internal sources, will be used to find an answer to the question of why firms may tend to prefer low-leverage policy. The paper will begin with an extensive analysis of the literature, particularly focusing on past empirical studies, which had been performed to establish the factors that influence firms to adopt or depart from financial conservative policies. An empirical Framework and Data Description will follow, which will summarise the method that is used in this study, as well as description of data/variables. The third section of this paper is the Empirical Analysis, which is dedicated to illustrate the analysis and findings. Finally, the paper will wrap up with a conclusion, which presents a summary of the findings of the dissertation. Literature review Introduction The research on this area is scanty, with most of the work being associated with the reasons why firms tend to adopt low-leverage policies (Minton & Wruck 2001). This section will review the previous empirical studies that have explained why firms tend to adopt high cash balances and low leverage policies. Financial flexibility motive and agency theory There are several reasons why firms tend to maintain low leverage. One of them can be explained by the pecking order theory, which explains that firms that are financially conservative have huge cash balances for their investment activities. What’s more, such firms only seek external funds when they feel that they really need it, and they hardly exhaust their internal cash balances (Minton & Wruck 2001). Elsewhere, Kaplan and Zingales (2000) has documented that financial conservatism is a transitory policy. This means that, eventually, most of the firms that cling to financial conservative policies eventually depart from such policies and start relaying on borrowed cash to finance their activities. As long as their internal source of cash is sufficient, many firms tend to maintain low leverage, but the moment they get exposed to more investment opportunities to the extent that their internal funds are insufficient, they tend to depart from their conservative policies and reach out to external sources of finance. According to Myers and Majluf (1984), the existence of asymmetric information between external investors and firms increase asset substitution, transaction costs, and underinvestment, among other agency problems. The problem of asymmetric information is associated with adverse selection and moral hazard, which comes with the primary agency relationships, hence hampering the financial stability of a firm. In view of this, for many firms, adoption of a conservative financial policy is a possible remedy to financial flexibility. In reference to agency theory, Myers (1984) has established an underinvestment hypothesis, which shows that firms with risky debt overhang and immense growth potentials tend to reduce investment in positive NPV ventures because their payoff may partly benefit the debt holders instead of benefiting the equity-holders in whole. Reduction of the risky debt overhand is a possible remedy underinvestment, and indeed many firms maintain very low leverage in a bid to address this problem. According to Myers (1984), firms tend to pursue financial flexibility so they can have cheaper ways of raising capital for financing their projects. This flexibility is usually achieved through what is referred to as conservative financial policies. This is whereby firms are involved in holding of large amounts of cash reserves or rather do away with debt capacity to avoid plunging into cash problems in the future. An empirical study by Faulkender and Petersen (2006) revealed that firms that have access to the bond market holds 35 percent debt above those that do not have access to such markets. This implies that financially conservative firms may be influenced to follow such a policy because they are not able to access funds from potential lenders. Graham and Harvey (2001) have carried out a recent survey, which shows that capital structure decisions are highly influenced by financial flexibility. As such, based on the components on pecking order and trade-off theories, explains how the concept of financial flexibility influences firms to maintain large cash balances and low leverage, with the aim of accumulating debt capacity and safeguard their ability to borrow funds that in be used in new ventures in the future (DeAngelo & DeAngelo 2009; Gamba &Triantis 2008). According to Mayers and Majluf, (1984), very low leverage decisions can be explained by the desire to accumulate, safeguard and draw down financial flexibility. Financial Distress and static trade-off theory The profit generated by the borrowers of funds is greatly reduced by the impact of financial distress. Mayers and Majluf, (1984) demonstrates how bankruptcy risk can be used to substitute financial distress, by exploring the direct relationship amongst book to returns and market, size, and distress risk. Firms usually find it important to have financial flexibility so they can be able to face unforeseen events, hence disregarding the cost of external financing (Mayers & Majluf, 1984). The static trade-off theory posits that firms establish an optimal capital structure, whereby tax shields and financial distress of debt financing are balanced. Under this theory, a firm with low tax shields and high financial distress should adopt a low-leverage policy (Graham, 2000). Debt is an important component of a capital structure for any firm, but unfortunately this has been a source of conflict amongst security holders, especially following change of investment policy or issue of new securities. While Myers (1984) finds an inverse relationship between leverage and profitability, Graham (2000) discovers that ordinary firms do not achieve an optimum level of borrowing because they tend to borrow less that they require. The manager of a firm becomes accountable and responsible when a firm uses debt capital because of the added burden of paying the interest and the principle. However, a firm experiences distress risk when it utilizes a debt higher than the optimum level, because this means it has invested in ventures that are not profitable. This means that a firm can enhance the status of the shareholders by use of leverage, but the value of the shareholders can decline drastically in the event that the firm experiences interest expenses and credit risk due to non-payment. According to Hennessy and Whited (2005), a firm that adopts a less conservative policy engages in a more sensitive business venture than the one that adopts a less-conservative policy. Elsewhere, Deangelo and Masulis (1980) assert that highly levered firms have their investment being more responsive to earnings. This implies that leverage can make a firm to generate more gains as well as losses. Nevertheless, firms tend to adopt conservatism policy as a way of mitigating such risks. Different literatures reveal that a sustainable level of debt can be achieved when a firm is able to enjoy many tax advantages while the NPV of the cost of financial distress is minimal (Modigliani & Miller, 1963; Deangelo & Masulis, 1980). Managers tend to adopt low leverage policy with the aim of reducing financial pressure and enhancing liquid asset. Jensen and Mackling (1976) finds that financial distress can be costly when operations and financing decisions are affected the managers’ conflict of interest. This, according to Fama and French (2000), complicates measurement of the present value of interest tax shield. During economically distressed periods, the firms operating in R&D experienced the worst shocks. What’s more, high leverage is associated with vulnerability to financial distress and this is the reason why many firms adopt financial conservative policies (Hovakimian & Titman 2003). Elsewhere, DeAngelo and Masulis (1980) provides that low-leverage is adopted by firms with high non-debt tax shields, while Yan (2006) maintains that this policy should be adopted when a firm has essentially huge alternatives for debt such as leases. What’s more, vibrant trade-off models advocate that firms may survive with low leverage and depart from target leverage, as a result of investment dynamics or transaction costs (Fischer et al. 1989; DeAngelo et al. 2009). However, an empirical study by Strebulaev and Yang (2006) implies that the existence of many low-leveraged firms and of equity finance can hardly be explained by the existing trade-off models. Ownership structure One of the ways in which conservatism of financial policies of a firm can be influenced is through the ownership structure. There is a lot of literature touching on the impacts of conflicts between managers and owners/shareholders. Jensen (1986) maintains that managers tend to hold large amounts of cash motive for the purpose of serving their own interests rather than that of the shareholders. It is also worth noting that the managers have the incentive to hold high amounts of cash so they can cushion themselves from punitive measures exerted upon them by the external investors. However, it is known that managers can desist from squandering of the shareholders resources if they assume substantial ownership, which makes them to bear the cost of misuse of resources and hence motivating them to pursue value maximization for the benefits of themselves and other shareholders. According to Hermalin and Weisbach (2003), the composition of the board of directors has an essential role in influencing the managers to serve the interest of the shareholders, especially because non-executive directors are meant to protect the interests of the shareholders. Fama and Jensen (1983) add that the executive boards have the incentive to discipline and monitor the executive directors. This implies that boards that do not constitute executive directors are better in protecting the interest of the shareholders. In support of this argument, Borokhovich et al., (1996) has performed an empirical study that shows that the markets reacts more positively to firms which are led by more outsiders that insiders. Therefore, this means that domination of a firm by outsiders would encourage managers to hold less cash balances, which would in turn reduce the cost of external financing, encouraging firms to depart from financially-conservative policies. Other researchers have argued that the shareholders themselves can put their managers on their toes, so they do not use the company’s funds for the purpose of their own interests (Shleifer & Vishny 1997; Faccio et al. 2001; and Holderness 2003). However, the problem that emerges here is that a common shareholder may not be in a position to monitor their managers because the cost of doing so is more than the expected benefit. Even so, the shareholders that hold huge proportions of shares in their firm usually yield immense influence resources wise, and the cost of monitoring the managers to them is less because they enjoy private benefits. This argument suffices to say that firms with large shareholders are more likely to adopt leverage-conservancy or even cash-conservancy because the cost of external financing is potentially huge. Empirical Framework and Data Description: The model for leverage conservatism The leverage conservatism model will involve examining the firms that are leverage conservative. This will involve use of a discriminant analysis depending on the non-parametric estimation of the distribution of cash holdings and leverage. As discussed earlier, a firm is considered to be leverage conservative when its total debt to total asset ratio falls below 20 percent. For the purpose of identifying leverage conservatism, a dummy qualitative variable will take the values of 0 while 1 shall be used to put data into a mutually exclusive classification. In this case, a firm that adopts leverage conservative policy shall be assigned 1 while a firm that maintains high cash balances will be assigned 0. For low leverage firms, different tests will be undertaken in reference to suggestions of different theories. Data description The following table shows the definition of all the variables that shall be used for this analysis. Variable Definition Code Cash flow This is the proportion of EBIT plus total assets plus depreciation CASHFLOW Liquidity Proportion of cash to assets CASHTOASSET Market to book The proportion of total assets book value minus the book value of equity added to equity market value to assets book value. MTB Fixed assets Money invested to purchase fixed assets CAPEX Size The logarithm of total assets in constant prices SIZE Table 1: Definition of variables Low Leverage model Low leverage = ?1 + ?2MTB = ?3CASHFLOW + ?4SIZE+?5CASHTOASSETS + ?6CAPEX + ?j Where, ?1 = Y intercept for the low leverage model. ?k = the coefficients of all independent variables for firm j (j= 1,2….6) ?j = this is the term representing the error for low leverage model for all the firms The study will involve a random selection of 30060 UK firms, whose financial years spread from 1987 to 2012. In order to establish their leverage conservative policies, different individual variables will be taken and several statistical tests performed using Eviews 7.0 software. The first phase will involve investigation of leverage conservative policy on the entire sample. In the second phase, the entire sample will be split into sub-samples, as shown in the following table. Each of the periods, i.e. 2010-2012 represents an observation window or panel, which can be tested independently. Year Total Leverage conservatism 1 1987-1989 995 140 2 1990-1992 2138 164 3 1993-1995 2383 123 4 1996-1998 4249 319 5 1999-2001 4384 626 6 2002-2004 3096 388 7 2005-2007 5183 936 8 2008-2010 4478 809 9 2011-2012 3154 611 Table 2: Allocation of Firms over the Nine Windows The whole sample Table 3: The whole sample will be used to empirically test the determinants of low leverage model, as shown in Table 3 below. Apparently, all the variables are statistically significant at 5%, considering that their p-values are all less than 0.01. However, the liquidity (CASHTOASSETS) has a significant positive impact on leverage conservative. On the other hand, SIZE has a significant negative impact on leverage conservative. Variable Coefficient Std. Error t-Statistic Prob.   CAPEX 0.026365 0.021287 1.238585 0.2155 CASHFLOW -0.003888 0.000614 -6.333568 0.0000 CASHTOASSET 0.531211 0.010013 53.05130 0.0000 LEVERAGE -0.000141 3.56E-05 -3.966687 0.0001 MTB -0.038036 0.001111 -34.22131 0.0000 SIZE -0.042741 0.001889 -22.62533 0.0000 C 0.177469 0.004745 37.40105 0.0000 Table 3: regression analysis for Leverage conservative model (the entire sample) Sub samples Table 4: all the variables are significant at 5% except CAPEX, CASHFLOW, and CASHTOASSET. SIZE has a significant negative impact on leverage conservative, while liquidity has the strongest positive relationship. Dependent Variable: LEV_CON Variable Coefficient Std. Error t-Statistic Prob.   CAPEX -0.409452 0.557030 -0.735064 0.4625 CASHFLOW 0.029862 0.024543 1.216738 0.2240 CASHTOASSET 0.134266 0.098162 1.367795 0.1717 MTB -0.082030 0.012189 -6.729779 0.0000 SIZE -0.099081 0.012978 -7.634731 0.0000 C 0.439623 0.037197 11.81861 0.0000 Table 4: 1987-1989 Table 5: Except CAPEX, all other variables are statistically significant at 5%. On the other hand, CASHTOASSET has the highest positive relationship with leverage conservative. Dependent Variable: LEV_CON Variable Coefficient Std. Error t-Statistic Prob.   CAPEX -0.470543 0.119933 -3.923394 0.0001 CASHFLOW -0.107473 0.043669 -2.461112 0.0139 CASHTOASSET 0.081835 0.047192 1.734100 0.0830 MTB -0.055101 0.006805 -8.097426 0.0000 SIZE -0.078572 0.006839 -11.48940 0.0000 C 0.330453 0.020016 16.50973 0.0000 R-squared 0.097518 Adjusted R-squared 0.095402 Table 5: 1990-1992 Table 6: Except CAPEX, all other variables are statistically significant at 5%. On the other hand, CASHTOASSET has the highest positive relationship with leverage conservative, while CASHFLOW has the highest negative value. The Adjusted R-squared is 11.9, which implies that the model is not very good in predicting the dependent variable. Dependent Variable: LEV_CON Variable Coefficient Std. Error t-Statistic Prob.   CAPEX -0.171878 0.071329 -2.409660 0.0160 CASHFLOW -0.212067 0.033219 -6.383931 0.0000 CASHTOASSET 0.132012 0.038700 3.411142 0.0007 MTB -0.061399 0.005201 -11.80410 0.0000 SIZE -0.052868 0.005248 -10.07348 0.0000 C 0.271203 0.016303 16.63529 0.0000 R-squared 0.121346     Mean dependent var 0.051616 Adjusted R-squared 0.119498     S.D. dependent var 0.221296 Table 6: 1993-1995 Table 7: Except CAPEX, all other variables are statistically significant at 5%. On the other hand, CASHTOASSET has the highest positive relationship with leverage conservative, while CASHFLOW has the highest negative value. The Adjusted R-squared is 11.9, which implies that the model is not very good in predicting the dependent variable. Dependent Variable: LEV_CON Variable Coefficient Std. Error t-Statistic Prob.   CAPEX -0.106556 0.041034 -2.596784 0.0094 CASHFLOW 0.002002 0.004816 0.415681 0.6777 CASHTOASSET 0.321938 0.025364 12.69254 0.0000 MTB -0.058716 0.003371 -17.41905 0.0000 SIZE -0.041711 0.004318 -9.660763 0.0000 C 0.202744 0.011741 17.26863 0.0000 R-squared 0.136213     Mean dependent var 0.075076 Adjusted R-squared 0.135196     S.D. dependent var 0.263546 Table 7: 1996-1998 Table 8: Except CAPEX and CASHFLOW, all other variables are statistically significant at 5%. On the other hand, CASHTOASSET has the highest positive relationship with leverage conservative, while CAPEX has the highest negative value. The Adjusted R-squared is 24.25, which implies that the model relatively good, though still significantly erroneous. Dependent Variable: LEV_CON Variable Coefficient Std. Error t-Statistic Prob.   CAPEX -0.157200 0.053967 -2.912907 0.0036 CASHFLOW 0.000205 0.000782 0.262492 0.7930 CASHTOASSET 0.569424 0.023697 24.02958 0.0000 MTB -0.070540 0.003586 -19.66862 0.0000 SIZE -0.040720 0.004986 -8.167230 0.0000 C 0.222662 0.012968 17.16982 0.0000 R-squared 0.243375     Mean dependent var 0.142792 Adjusted R-squared 0.242511     S.D. dependent var 0.349900 Table 8: 1999-2001 Table 9: Except CAPEX, all other variables are statistically significant at 5%, since their respective p-values are less than 0.001. CASHTOASSET has the highest positive relationship with leverage conservative, meaning that firms with higher liquidity are more inclined to leverage conservative policies. The Adjusted R-squared is 16.25, pointing to the poor predictive power of the model. Dependent Variable: LEV_CON Variable Coefficient Std. Error t-Statistic Prob.   CAPEX -0.032152 0.055296 -0.581446 0.5610 CASHFLOW 0.006329 0.001533 4.129289 0.0000 CASHTOASSET 0.478134 0.023242 20.57242 0.0000 MTB -0.037397 0.002699 -13.85418 0.0000 SIZE -0.036245 0.004497 -8.059902 0.0000 C 0.161148 0.010972 14.68683 0.0000 R-squared 0.162274     Mean dependent var 0.133932 Adjusted R-squared 0.161398     S.D. dependent var 0.340615 Table 9: 2002-2004 Table 10: Except CAPEX, all other variables are statistically significant at 5%, since their respective p-values are less than 0.001. However, CASHTOASSET has the highest positive relationship with leverage conservative, followed by CAPEX. The Adjusted R-squared is 29.9, which is makes the model relatively good compared to other windows. Dependent Variable: LEV_CON Variable Coefficient Std. Error t-Statistic Prob.   CAPEX 0.157157 0.066166 2.375192 0.0176 CASHFLOW 0.028375 0.005610 5.057726 0.0000 CASHTOASSET 0.607782 0.027088 22.43770 0.0000 MTB -0.059892 0.004004 -14.95713 0.0000 SIZE -0.054794 0.006163 -8.890970 0.0000 C 0.212936 0.015022 14.17453 0.0000 R-squared 0.250171     Mean dependent var 0.195534 Adjusted R-squared 0.249096     S.D. dependent var 0.396668 Table 10: 2005-2007 Table 11: Except CAPEX, all other variables are statistically significant at 5%, since their respective p-values are less than 0.001. However, CASHTOASSET has the highest positive relationship with leverage conservative, followed by CAPEX. Dependent Variable: LEV_CON Variable Coefficient Std. Error t-Statistic Prob.   CAPEX 0.086567 0.050806 1.703889 0.0885 CASHFLOW -0.013491 0.001965 -6.864334 0.0000 CASHTOASSET 0.599232 0.023975 24.99451 0.0000 MTB -0.032311 0.002563 -12.60466 0.0000 SIZE -0.034331 0.004660 -7.366754 0.0000 C 0.162285 0.011421 14.20941 0.0000 R-squared 0.169431     Mean dependent var 0.182403 Adjusted R-squared 0.168708     S.D. dependent var 0.386210 Table 11: 2008-2010 Table 12: Except CAPEX and CASHFLOW, all other variables are statistically significant at 5%, since their respective p-values are less than 0.001. However, CASHTOASSET has the highest positive relationship with leverage conservative, followed by CAPEX. At 22.9% of Adjusted R-squared, the model is relatively good compared with other windows. Dependent Variable: LEV_CON Variable Coefficient Std. Error t-Statistic Prob.   CAPEX 0.170049 0.098004 1.735131 0.0829 CASHFLOW 0.030579 0.010137 3.016555 0.0026 CASHTOASSET 0.611895 0.046946 13.03409 0.0000 MTB -0.103324 0.007288 -14.17644 0.0000 SIZE -0.063612 0.008083 -7.870146 0.0000 C 0.308722 0.022021 14.01923 0.0000 R-squared 0.231912     Mean dependent var 0.197236 Adjusted R-squared 0.229863     S.D. dependent var 0.398017 Table 12: 2010-2012 Test of equality Table 13: this test shows that CAPEX and CASHTOASSET have their means very close to leverage conservative policy, indicating that these variables have a strong determining power on the dependent variable. CASHFLOW has the highest standard deviation and standard error of mean, possibly because different firms have very different cash flow habits, which is affected by very many factors. Also, MTB and SIZE have their mean very close because the size of a firm largely determines its market to book value. Category Statistics Std. Err. Variable Count Mean Std. Dev. of Mean CAPEX 30060 0.052755 0.086559 0.000499 CASHFLOW 30060 -0.052366 3.194068 0.018423 CASHTOASSET 30060 0.144186 0.193456 0.001116 LEV_CON 30060 0.136926 0.343775 0.001983 MTB 30060 1.174427 1.754045 0.010117 SIZE 30060 1.729477 1.023813 0.005905 All 180360 0.530901 1.693810 0.003988 Table 13: Test for Equality of Means between Series Table 14: the Mean Scores for CAPEX, CASHFLOW, and CASHTOASSET are all moving in the same direction with the leverage conservative policy, implying that the factors are incentives for adoption of leverage conservative policies. However, SIZE and MTB have an opposite Mean Score to the dependent variables, implying that these factors are disincentives for adoption of leverage conservative models. > Overall Variable Count Median Median Mean Rank Mean Score CAPEX 30060 0.029251 4126 64181.74 -0.395530 CASHFLOW 30060 0.097211 14478 72016.21 -0.468676 CASHTOASSET 30060 0.069917 12132 81831.82 -0.128820 LEV_CON 30060 0.000000 4116 40816.93 -0.832067 MTB 30060 0.997653 26684 132421.1 0.747308 SIZE 30060 1.679886 28644 149815.2 1.116131 All 180360 0.101097 90180 90180.50 0.006391 Table 14: Test for Equality of Medians between Series Table 15: the test of equality is used to test whether the means of the size in each subgroup are statistically different or equal, which is the null hypothesis. The conclusion is the rejection of the null hypothesis since Anova F-test and Welch F-tests are highly significant, meaning that the variables are different. Test for Equality of Means Between Series Date: 08/08/13 Time: 12:59 Sample: 1 30060 Included observations: 30060 Method df Value Probability Anova F-test (5, 180354) 35.07621 0.0000 Welch F-test* (5, 75484) 17038.67 0.0000 *Test allows for unequal cell variances Table 15: test of equality on all variables Correlation matrix (whole sample) Table 16: Although there is no perfect correlation between any of the variables, a number of them are closely correlated. Market to book value has the closest negative correlation with the dependent valuable, followed by the SIZE. This further confirms that the two variables play key roles in influencing firms to depart from leverage conservative policies. CASHTOASSET has the highest positive relationship with the dependent variable, confirming that firms that have large cash balances tend to adopt leverage conservative policies. Cash flow and Market to Book value are closely and positively correlated, possibly because firms which are highly valued tend to have adequate cash flows. CAPEX CASHFLOW CASHTOASSET LEV_CON MTB SIZE CAPEX  0.007492 -0.043755 -0.000800 -0.001047  0.02555  0.000383 CASHFLOW -0.04375  10.20173 -0.054401 -0.010869 -1.9744  0.404990 CASHTOASSET -0.00080 -0.054401  0.037424  0.023692 -0.0190 -0.06881 LEV_CON -0.00104 -0.010869  0.023692  0.118177 -0.1159 -0.08002 MTB  0.025555 -1.974441 -0.019091 -0.115989  3.07657 -0.06935 SIZE  0.000383  0.404990 -0.068818 -0.080023 -0.0693  1.04815 Table 16: Correlation matrix Table 17: CAPEX CASHFLOW CASHTOASSET LEV_CON MTB SIZE  Mean  0.052755 -0.052366  0.144186  0.136926  1.174427  1.729477  Median  0.029251  0.097211  0.069917  0.000000  0.997653  1.679886  Maximum  4.000000  5.806600  1.000000  1.000000  192.0000  5.556694  Minimum -0.867765 -388.6000  0.000000  0.000000 -1.305703 -3.000000  Std. Dev.  0.086559  3.194068  0.193456  0.343775  1.754045  1.023813  Skewness  9.046555 -93.47663  2.220397  2.112311  50.45393  0.116284  Kurtosis  234.8536  10096.72  8.051678  5.461856  4863.461  3.450795  Jarque-Bera  67739500  1.28E+11  56663.23  29944.97  2.96E+10  322.2743  Probability  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  Sum  1585.808 -1574.110  4334.225  4116.000  35303.29  51988.08  Sum Sq. Dev.  225.2172  306664.1  1124.964  3552.412  92481.74  31507.61  Observations  30060  30060  30060  30060  30060  30060 Table 17: summary statistics Discussion of Empirical Analysis Some of the reasons why firms adopt leverage conservative policies include avoiding the risk of financial risk. The variables tested in this study have revealed significant responsive to low leverage conservatism. The firms that have been found to adopt leverage conservatism policies tend to follow pecking order theory and precautionary demand. Different tests have shown that different independent variables plays different roles in influencing the dependent variable, though some reveal no relationship but they still play some role. The market to book value of companies have negative relationship with the leverage conservative policy, which implies that firms with high market to book value tend to have high leverage in their capital structure, for reasons such as easy access to credit. The market value is determined by the stock exchange market capitalization. The performance of a company is determined by the ratio of the market to book values. A security is considered to be overvalued when the ratio is greater than 1 and undervalued when the ratio is less than 1. As such, a firm can experience financial distress risk due to its ratio of book to market value, a condition that makes firms to adopt leverage conservative policies. Whenever a company faces agency cost or asymmetric information, its market value is usually higher than the company’s book value, hence pushing up its leverage. The impact of liquidity on leverage conservatism policy is immense, in that a firm tends to adopt leverage conservative policy when it has ample cash balances in order to avoid future risk. In order to finance the available investment opportunities, firms tend to deploy the ‘Pecking Order Approach’. There are some tax advantages that come with leverage, for example because higher debts attract more tax benefits. According to the pecking order theory, the investors tend to mark down new stock issues when they do not have information about its future growth prospects and its value (Myers & Majluf, 1984). As such, securities that are insensitive to mis-pricing and those that are less risky are preferred by most firms. The firms’ tendency to prefer retained earnings to debt financing and equity financing is used only when there is no other option. In support of Pecking Order Theory, Myers and Majluf (1984) assert that firms use internal financing as the first option when financing their projects, and only embark on the external sources as the last resort. On the other hand, high debts expose firms to the risk of defaulting as well as bankruptcy. According to Ross Westerfiels and Jordan (2008), firms that are able to take high debts are assumed to be of high quality, because those of low quality are not able to access debt capital due to the potential risk of bankruptcy. The tests on all the panels have established a negative relationship between size and leverage conservative policies, simply because firms that are big in size tend to adopt less leverage conservative policies (Rajan & Zingales 1995). The firms that are large in size tend to experience lower information asymmetries, which is an incentive for adoption of higher debt capital. What’s more, the firms that are large in size get a better opportunity to access the capital markets, getting an opportunity to enjoy an added advantage because they can issue securities at relatively low costs. The extra cash that firms have in their exposure, which they can use to invest in future investment opportunities, is referred to as cash flow. The firms that have high cash flows tend to hold high cash balances with the aim of investing them when a need arises. The firms with higher fixed assets are less likely to adopt leverage conservative policies, although the relationship between leverage and fixed assets depends on the performance of a firm. A firm with high tangible assets can depart from leverage conservative policy when it is experiencing cash shortages. On the other hand, firms with many tangible assets, but with adequate cash balances may adopt less leverage policies because they would want to enhance their debt capacity. Conclusion Among the major reasons why firms adopt leverage conservative policies have been found to include mitigation of financial distress risks and the availability of sufficient internal funds. This study has shown that this among other determinants of leverage conservative policies have played a significant influence. The pecking order theory as well as precautionary demands has roles to play in determining the firm’s conservative approach. In view of this, it has been found that firms can fail to use leverage capital to finance new investments when they have enough funds from their internal sources. 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Error t-Statistic Prob.   CAPEX 0.026365 0.021287 1.238585 0.2155 CASHFLOW -0.003888 0.000614 -6.333568 0.0000 CASHTOASSET 0.531211 0.010013 53.05130 0.0000 LEVERAGE -0.000141 3.56E-05 -3.966687 0.0001 MTB -0.038036 0.001111 -34.22131 0.0000 SIZE -0.042741 0.001889 -22.62533 0.0000 C 0.177469 0.004745 37.40105 0.0000 R-squared 0.173042     Mean dependent var 0.136926 Adjusted R-squared 0.172877     S.D. dependent var 0.343775 S.E. of regression 0.312651     Akaike info criterion 0.512773 Sum squared resid 2937.695     Schwarz criterion 0.514708 Log likelihood -7699.974     Hannan-Quinn criter. 0.513394 F-statistic 1048.106     Durbin-Watson stat 1.147528 Prob(F-statistic) 0.000000 1987-2012 Dependent Variable: LEV_CON Method: Least Squares Date: 07/19/13 Time: 14:36 Sample: 1 995 Included observations: 995 Variable Coefficient Std. Error t-Statistic Prob.   CAPEX -0.409452 0.557030 -0.735064 0.4625 CASHFLOW 0.029862 0.024543 1.216738 0.2240 CASHTOASSET 0.134266 0.098162 1.367795 0.1717 MTB -0.082030 0.012189 -6.729779 0.0000 SIZE -0.099081 0.012978 -7.634731 0.0000 C 0.439623 0.037197 11.81861 0.0000 R-squared 0.090779     Mean dependent var 0.140704 Adjusted R-squared 0.086182     S.D. dependent var 0.347890 S.E. of regression 0.332562     Akaike info criterion 0.642029 Sum squared resid 109.3807     Schwarz criterion 0.671593 Log likelihood -313.4095     Hannan-Quinn criter. 0.653268 F-statistic 19.74876     Durbin-Watson stat 1.938079 Prob(F-statistic) 0.000000 1987-1989 Dependent Variable: LEV_CON Method: Least Squares Date: 07/19/13 Time: 18:56 Sample: 1 2138 Included observations: 2138 Variable Coefficient Std. Error t-Statistic Prob.   CAPEX -0.470543 0.119933 -3.923394 0.0001 CASHFLOW -0.107473 0.043669 -2.461112 0.0139 CASHTOASSET 0.081835 0.047192 1.734100 0.0830 MTB -0.055101 0.006805 -8.097426 0.0000 SIZE -0.078572 0.006839 -11.48940 0.0000 C 0.330453 0.020016 16.50973 0.0000 R-squared 0.097518     Mean dependent var 0.076707 Adjusted R-squared 0.095402     S.D. dependent var 0.266189 S.E. of regression 0.253173     Akaike info criterion 0.093314 Sum squared resid 136.6538     Schwarz criterion 0.109220 Log likelihood -93.75293     Hannan-Quinn criter. 0.099135 F-statistic 46.07502     Durbin-Watson stat 1.864972 Prob(F-statistic) 0.000000 1990-1992 Dependent Variable: LEV_CON Method: Least Squares Date: 07/19/13 Time: 19:04 Sample: 1 2383 Included observations: 2383 Variable Coefficient Std. Error t-Statistic Prob.   CAPEX -0.171878 0.071329 -2.409660 0.0160 CASHFLOW -0.212067 0.033219 -6.383931 0.0000 CASHTOASSET 0.132012 0.038700 3.411142 0.0007 MTB -0.061399 0.005201 -11.80410 0.0000 SIZE -0.052868 0.005248 -10.07348 0.0000 C 0.271203 0.016303 16.63529 0.0000 R-squared 0.121346     Mean dependent var 0.051616 Adjusted R-squared 0.119498     S.D. dependent var 0.221296 S.E. of regression 0.207653     Akaike info criterion -0.303378 Sum squared resid 102.4961     Schwarz criterion -0.288834 Log likelihood 367.4745     Hannan-Quinn criter. -0.298085 F-statistic 65.65476     Durbin-Watson stat 1.935006 Prob(F-statistic) 0.000000 1993-1995 Dependent Variable: LEV_CON Method: Least Squares Date: 07/19/13 Time: 19:12 Sample: 1 4249 Included observations: 4249 Variable Coefficient Std. Error t-Statistic Prob.   CAPEX -0.106556 0.041034 -2.596784 0.0094 CASHFLOW 0.002002 0.004816 0.415681 0.6777 CASHTOASSET 0.321938 0.025364 12.69254 0.0000 MTB -0.058716 0.003371 -17.41905 0.0000 SIZE -0.041711 0.004318 -9.660763 0.0000 C 0.202744 0.011741 17.26863 0.0000 R-squared 0.136213     Mean dependent var 0.075076 Adjusted R-squared 0.135196     S.D. dependent var 0.263546 S.E. of regression 0.245084     Akaike info criterion 0.026980 Sum squared resid 254.8607     Schwarz criterion 0.035953 Log likelihood -51.31810     Hannan-Quinn criter. 0.030151 F-statistic 133.8187     Durbin-Watson stat 2.033432 Prob(F-statistic) 0.000000 1996-1998 Dependent Variable: LEV_CON Method: Least Squares Date: 07/19/13 Time: 19:18 Sample: 1 4384 Included observations: 4384 Variable Coefficient Std. Error t-Statistic Prob.   CAPEX -0.157200 0.053967 -2.912907 0.0036 CASHFLOW 0.000205 0.000782 0.262492 0.7930 CASHTOASSET 0.569424 0.023697 24.02958 0.0000 MTB -0.070540 0.003586 -19.66862 0.0000 SIZE -0.040720 0.004986 -8.167230 0.0000 C 0.222662 0.012968 17.16982 0.0000 R-squared 0.243375     Mean dependent var 0.142792 Adjusted R-squared 0.242511     S.D. dependent var 0.349900 S.E. of regression 0.304532     Akaike info criterion 0.461286 Sum squared resid 406.0144     Schwarz criterion 0.470026 Log likelihood -1005.139     Hannan-Quinn criter. 0.464370 F-statistic 281.6438     Durbin-Watson stat 1.902515 Prob(F-statistic) 0.000000 1999-2001 Dependent Variable: LEV_CON Method: Least Squares Date: 07/19/13 Time: 19:25 Sample: 1 4786 Included observations: 4786 Variable Coefficient Std. Error t-Statistic Prob.   CAPEX -0.032152 0.055296 -0.581446 0.5610 CASHFLOW 0.006329 0.001533 4.129289 0.0000 CASHTOASSET 0.478134 0.023242 20.57242 0.0000 MTB -0.037397 0.002699 -13.85418 0.0000 SIZE -0.036245 0.004497 -8.059902 0.0000 C 0.161148 0.010972 14.68683 0.0000 R-squared 0.162274     Mean dependent var 0.133932 Adjusted R-squared 0.161398     S.D. dependent var 0.340615 S.E. of regression 0.311919     Akaike info criterion 0.509107 Sum squared resid 465.0631     Schwarz criterion 0.517223 Log likelihood -1212.293     Hannan-Quinn criter. 0.511958 F-statistic 185.1846     Durbin-Watson stat 1.925267 Prob(F-statistic) 0.000000 2003-2004 Dependent Variable: LEV_CON Method: Least Squares Date: 07/19/13 Time: 19:30 Sample: 1 3493 Included observations: 3493 Variable Coefficient Std. Error t-Statistic Prob.   CAPEX 0.157157 0.066166 2.375192 0.0176 CASHFLOW 0.028375 0.005610 5.057726 0.0000 CASHTOASSET 0.607782 0.027088 22.43770 0.0000 MTB -0.059892 0.004004 -14.95713 0.0000 SIZE -0.054794 0.006163 -8.890970 0.0000 C 0.212936 0.015022 14.17453 0.0000 R-squared 0.250171     Mean dependent var 0.195534 Adjusted R-squared 0.249096     S.D. dependent var 0.396668 S.E. of regression 0.343732     Akaike info criterion 0.703805 Sum squared resid 411.9938     Schwarz criterion 0.714383 Log likelihood -1223.195     Hannan-Quinn criter. 0.707580 F-statistic 232.6787     Durbin-Watson stat 1.953322 Prob(F-statistic) 0.000000 2005-2007 Dependent Variable: LEV_CON Method: Least Squares Date: 07/19/13 Time: 19:36 Sample: 1 5751 Included observations: 5751 Variable Coefficient Std. Error t-Statistic Prob.   CAPEX 0.086567 0.050806 1.703889 0.0885 CASHFLOW -0.013491 0.001965 -6.864334 0.0000 CASHTOASSET 0.599232 0.023975 24.99451 0.0000 MTB -0.032311 0.002563 -12.60466 0.0000 SIZE -0.034331 0.004660 -7.366754 0.0000 C 0.162285 0.011421 14.20941 0.0000 R-squared 0.169431     Mean dependent var 0.182403 Adjusted R-squared 0.168708     S.D. dependent var 0.386210 S.E. of regression 0.352128     Akaike info criterion 0.751397 Sum squared resid 712.3449     Schwarz criterion 0.758342 Log likelihood -2154.641     Hannan-Quinn criter. 0.753814 F-statistic 234.3895     Durbin-Watson stat 1.908048 Prob(F-statistic) 0.000000 2008-2010 Dependent Variable: LEV_CON Method: Least Squares Date: 07/19/13 Time: 19:41 Sample: 1 1881 Included observations: 1881 Variable Coefficient Std. Error t-Statistic Prob.   CAPEX 0.170049 0.098004 1.735131 0.0829 CASHFLOW 0.030579 0.010137 3.016555 0.0026 CASHTOASSET 0.611895 0.046946 13.03409 0.0000 MTB -0.103324 0.007288 -14.17644 0.0000 SIZE -0.063612 0.008083 -7.870146 0.0000 C 0.308722 0.022021 14.01923 0.0000 R-squared 0.231912     Mean dependent var 0.197236 Adjusted R-squared 0.229863     S.D. dependent var 0.398017 S.E. of regression 0.349290     Akaike info criterion 0.737356 Sum squared resid 228.7564     Schwarz criterion 0.755026 Log likelihood -687.4829     Hannan-Quinn criter. 0.743864 F-statistic 113.2251     Durbin-Watson stat 1.946067 Prob(F-statistic) 0.000000 2011-2012 Read More