The Effect of Inflation Uncertainty on the Capital Structure of Non-Financial Firms

Pal. Jour. V.16, I.3, No.2 2017, 523-530 Copyright 2017 by Palma Journal, All Rights Reserved Available online at: http://palmajournal.org/ The Effect of Inflation Uncertainty on the Capital Structure of Non-Financial Firms Reza Tehrani, Associate Professor, Faculty of Management, University of Tehran, Tehran, Iran Sara Najafzadeh Khoee Corresponding Author,Master of Financial Management, Faculty of Management, University of Tehran, Tehran, Iran Email: sara.najafzadeh@ut.ac.ir Abstract The study of capital structure attempts to explain the mix of securities and financing sources used by corporations to finance real investments. Most of the research on capital structure has focused on the internal variables in order to describe firm specific characteristics influencing their preferences between debt and equity. Recent researches in this area has shown that macroeconomic condition has a significant effect on financing decisions. To our knowledge there is no study which has considered the effect of macroeconomic uncertainty on Iranian firms leverage. As inflation affects economies in various positive and negative ways, we tried to shed light on the effect of uncertainty over future inflation on the capital structure of non-financial firms listed in Tehran Stock Exchange. We used a sample of 186 manufacturing firms for the period from 2007 to 2014 with applying an EGARCH model to proxy for uncertainty. In this paper we used a fixed effect regression and the effect of inflation uncertainty on every firm in the sample was estimated separately. The results revealed that inflation uncertainty had a negative effect on the leverage of more than 50 percent of the firms in the sample, while others were positively affected by this type of uncertainty. Key words: Capital Structure, Inflation Uncertainty, ARCH, EGARCH, Panel Regression. Introduction Decades of intensive research in the field of capital structure and several theories in the finance literature trying to recognize new factors influencing on financing decisions has made capital structure as one of the main puzzles in finance. The importance of capital structure lies with the fact that it affects firms value. The aim of conventional corporate financial theory when making decisions is to maximize the value of the business or firm and all of corporate finance is built on three decisions including the investment decision, the financing decision, and the dividend decision. Any decision (investment, financial, or dividend) that increases the value of a business is considered a good one, whereas one that reduces firm value is considered a poor one [1]. Therefore considering the financing choice of a company as one of its major decisions which will have substantial effects on the cost of capital and consequently on its value is vital and of high importance. The debt equity decision is one of the most researched areas in finance and the capital structure determinants have been in the center of attention in the past decades. Over the years, research in capital structure has enhanced the overall perceiving of how firms make their financing decisions [2], [3], [4], [5], [6], [7], [8], [9]. However there is no universally accepted theory which can fully explain a firm preference in choosing financial resources [10]. The primary studies used to give consideration to the special firm characteristics. Based on these studies, there are almost similar agreements on the key internal factors affecting capital structure including profitability, firm size, asset structure, liquidity, growth opportunities, uniqueness, industry classification, earning volatility and stock return [10],[11], [12], [13], [14],while recent surveys have started to investigate external factors affecting debt-equity ratio which are mostly associated with Macroeconomic conditions. Frank and Goyal (2003) have come to conclusion that around 30 percent of differences in the capital structure inside the country can be explained by internal determinants, which posits that there are other factors than internal determinants influencing financing choices [15]. Hackbarth et al. (2006) revealed that macroeconomic conditions have considerable impacts Palma Journal

524 R.Tehrani and S.N.Khoee, on target capital structure [10]. Internal factors and their impacts can be managed by the firm, while macroeconomic factors cannot be controlled by managers and both types of determinants have significant effects on the corporate capital structure. Being aware of the level, direction and power of their impacts can help companies to make effective decisions according capital structure for the aim of financial stability and sustainable growth [16]. With considering the fact that external sources of financing are directly affected by the macroeconomic conditions while firm characteristics including probability of bankruptcy, profitability and capital investment are indirectly influenced by stages of life cycle via cost of capital, cash flows, leverage and the balance sheet components, it is implied that the target capital structure and its adjustments are both directly and indirectly affected by macroeconomic conditions and different stages of corporate life cycle [10]. Furthermore, a firm s financing choices might change as it makes the transition from a start-up firm to a mature firm to final decline. Typically, startup firms and firms in rapid expansion use debt sparingly; in some cases, they use no debt at all. As the growth eases and as cash flows from existing investments become larger and more predictable, we see firms beginning to use debt. Debt ratios typically peak when firms are in mature growth [1]. As we can see, global attention has been rising to consider other important variables which are necessary for managers prospects when making financing decisions, Inflation affects economies in various positive and negative ways and Iran is a country dealing with the issue of high inflation rate which will produce high inflation uncertainty so in this research we are trying to extend the literature on the effect of inflation uncertainty on the capital structure by using a panel data of 186 firms listed in Tehran stock exchange covering a period from 2007 to 2014. The rest of the paper is organized as follows; Section 2 discusses relevant literature. Section 3 describes the data and the research methodology used in this paper. Section 4 reports the empirical results and Section 5 summarizes and concludes the paper. Literature review One of corporate finance best-known theorem is written by the Modigliani-Miller theorem (1958). In their first model, they argued that in a frictionless world with no taxes, transaction costs and possibility of default, the value of a firm is unaffected by its leverage. However, they ultimately reversed this claim, explaining that leverage has a positive effect on the value of the firm and it is maximized when a firm is entirely financed with debt [3]. Miller and Modigliani were pioneers in moving capital structure analysis from an environment in which firms picked their debt ratios based on comparable firms and management preferences, to one that recognized the trade-offs. The trade-off theory of capital structure recognizes that target debt ratios may vary from firm to firm. This trade-off theory states, despite the fact that existing debt in the capital structure of firms creates tax shield and increases its value, risk increases as the firm adds debt to the capital structure. Providing tax shield and being a cheaper source of financing, make Debt beneficial for firms at low levels, But when large amounts of debt is taken on, the firms commence to be financially distressed by trying to meet interest payment obligations [17]. So according to this theory, capital structure decisions depend on benefits and costs of utilizing more debt [18]. Harkbarth et al. (2006) claimed that, if a firm determines its optimal capital structure by balancing the related benefits and costs of debt, then both benefits and costs should depend on macroeconomic conditions; the expected benefit of debt which is also used for the purpose of reducing the agency conflicts between managers and shareholders depends on whether there is an economic expansion or recession since it effects on the level of corporate cash flows. Further, expected costs of debt (bankruptcy costs and agency conflicts between creditors and shareholders) depend on probability of default and loss given default both of which should depend on the current state of the economy [15]. Myers and Majluf (1984) presented the pecking order theory which starts with asymmetric information indicating that managers know more about their companies prospects, risks, and values than do outside investors. Asymmetric information affects the choice between internal and external financing and between new issues of debt and equity securities. This leads to a pecking order, in which investment is financed first with internal funds, reinvested earnings primarily; then by new issues of debt; and finally with new issues of equity [19]. Myers and Majluf (1984) anticipated that leverage decreases with the increase of free cash flow [18]. Many studies have investigated the relation between capital structure and firm-level determinants and they have introduced almost a same set of factors. Mokhova &

Uncertainty on the Capital Structure of Non-Financial Firms 525 Zinecke (2014) have found that external determinants of capital structure play a substantial role in financial decision-making process and the knowledge about the power and direction of such influence supports managers to make effective and accurate financial decision for stable and successful development [10], [15], [20], [21], [22], [23], [26]. Mokhova & Zinecker (2014) have considered the effect of external factors on the capital structure. GDP is one of the most used external factors. As a rule, during the period of economic expansion, when interest rates are rising, banks are willing to increase loans to private sector, therefore, financial leverage should rise [16] but according to the pecking order theory, when product market goes up, it leads to more retained earnings therefore the use of debt will decrease [20]. Inflation rate is another external factor being considered in the researches. Inflation is expected to have a positive effect since it increases the true value of tax deductions on debt [22]; [23]. Camara (2012) has showed that inflation is negatively related to leverage since cost of borrowing will increase in the inflationary condition. Another variable which is suspicious to be related to leverage is exchange rate. The exchange rate sensitivity affects the firm value and its stock price. This would occur due to the adjustments of firms cash flows according to the fluctuations in foreign exchange rate. For instance the profit of an exporting firm is more likely to decrease based upon the appreciation of domestic currency and so is its value, therefore the firm ought to use external sources of financing. Since the stock price has been fallen, the issuance of new equity does not make sense and due to the reduction of profit, investors will not be interested in buying new shares, so borrowing would be a better choice, in this condition the amount of debt would increase. The macroeconomic environment has significant effects on the growth and financial performance of firms. The economic cycle for example has been discovered to affect profitability, leverage, cash flow and by means of that influence company failures. Bhattacharjee et al. (2009) have studied US and UK firm exits through bankruptcies and acquisitions and have discovered that both modes of exit depend on the macroeconomic environment, specifically, macroeconomic instability [24]. Baum et al. (2006) argued that higher uncertainty will obstruct managers ability to predict firm-specific information such as expected future cash flows. They showed that macroeconomic uncertainty signaling increased uncertainty hampers efficient use of resources. They also found that Firms experiencing rapid growth, firms that are financially constrained and capital-intensive firms are found to be quite sensitive to macroeconomic uncertainty. As we can see, macroeconomic condition seems to be an important factor for firms when deciding to overcome their financial needs.in this paper, we are interested to investigate the effect of inflation uncertainty on the capital structure. Data and methodology This paper is based on the evidence of firms listed in Tehran stock exchange and the sample contains 186 non-financial firms for the period 2007-2014. All of the companies in the sample are calendar-year taxpayers and the debt to equity ratio which is considered as a proxy for capital structure, is positive. Availability of appropriate information was another selection requirement, therefore companies which had all required data for the period 2007-2014 were selected. The required data of financial statements were obtained from official Tehran stock exchange database and the data of inflation including inflation rate was provided by central bank of Islamic Republic of Iran database. An EGARCH model was used to proxy for inflation rate uncertainty. Based on the literature this approach seems more appropriate comparing to other proxies which are derived from moving standard deviations of macroeconomic series or those that are based on the dispersion of forecasts [13]. We employed a panel data regression given as Eq. 1: Yit i X it Uit (1) Where i is the individual dimension and t is the time dimension. Y is the dependent variable which is a measure of capital structure. We have data of 186 firms for 8 years so our total observation is 1488. We have checked the stationary of inflation variable by using unit root test. The stationarity or otherwise of a series can strongly influence its behavior and properties and the use of non-stationary data can lead to spurious regressions. Stationary series can be defined as one with a constant mean, constant variance and constant autocovariances for each given lag which is the concept of weak satationarity. The early and

526 R.Tehrani and S.N.Khoee, pioneering work on testing for a unit root in time series was done by Dickey and Fuller [25]. Based on the Augmented Dickey-Fuller test, inflation rate had a unit root,therefore its first difference was utilized. Autocorrelation and partial autocorrelation functions were applied for modeling the inflation rate and the results showed AR (1) for inflation, details are shown in table 1. Table 1: Modeling the inflation rate Variable Coefficient Std. Error t-statistic Prob. C 9.177276 4.277009 2.145723 0.0444 INF(-1) 0.570171 0.193883 2.940808 0.0081 R-squared 0.301880 Mean dependent var 20.66364 Adjusted R-squared 0.266974 S.D. dependent var 9.547353 S.E. of regression 8.174154 Akaike info criterion 7.126339 Sum squared resid 1336.336 Schwarz criterion 7.225525 Log likelihood -76.38973 Hannan-Quinn criter. 7.149705 F-statistic 8.648352 Durbin-Watson stat 1.685597 Prob(F-statistic) 0.008082 The results from using Autoregressive conditionally heteroscedastic (ARCH) model revealed that the variance of errors of inflation is not constant at the 95 percent confidence interval, see table 2. If the variance of the errors is not constant, this would be known as heteroscedasticity, if the errors are heteroscedastic, but assumed homoscedastic, an implication would be that standard error estimates could be wrong. It is unlikely in the context of financial time series that the variance of the errors will be constant over time, and hence it makes sense to consider a model that does not assume that the variance is constant, and which describes how the variance of the errors evolves [25]. Table 2: Heteroskedasticity Test: ARCH- inflation rate F-statistic 4.682022 Prob. F(1,19) 0.0434 Obs*R-squared 4.151777 Prob. Chi-Square(1) 0.0416 In order to calculate inflation uncertainty, we used an EGARCH model which was proposed by Nelson (1991) which is shown by Eq. 2: 2 2 u u 1 1 2 t t (2) ln( t ) ln( t 1) 2 2 t 1 t 1 This model has several advantages over the pure GARCH specification. First, since the log (σ 2 t ) is 2 modelled, then even if the parameters are negative, σ t will be positive. There is thus no need to artificially impose non-negativity constraints on the model parameters. Second, asymmetries are allowed for under the EGARCH formulation [25]. The EGARCH model of inflation rate is shown in tables 3, respectively. The unit root test was also applied for the dependent variable and non-stationarity problem was not seen among them. Table 3: EGARCH model-inflation rate Variable Coefficient Std. Error z-statistic Prob. C 3.065184 0.232838 13.16447 0.0000 AR(1) 0.798389 0.086671 9.211696 0.0000 Variance Equation C(3) -0.126677 0.627454-0.201891 0.8400 C(4) -1.912784 0.928821-2.059369 0.0395 C(5) 0.831217 0.545304 1.524318 0.1274 C(6) 0.365221 0.170672 2.139905 0.0324 R-squared 0.303911 Mean dependent var 2.940519 Adjusted R-squared 0.269107 S.D. dependent var 0.418600 S.E. of regression 0.357870 Akaike info criterion 0.572117 Sum squared resid 2.561425 Schwarz criterion 0.869674 Log likelihood -0.293292 Hannan-Quinn criter. 0.642213 Durbin-Watson stat 1.840066

Uncertainty on the Capital Structure of Non-Financial Firms 527 Empirical Results Hausman and Chaw tests were applied in order to see whether a fixed effect or a random effect model is appropriate. Based on these tests, a fixed effect model is preferred, see table 8 and 9. Table 4: Hausman test Test Summary Chi-Sq. Statistic Chi-Sq. d.f. Prob Cross-section and random effect 8.558 4 0.07 Table 5: Chaw test Effects Test Statistic d.f. Prob. Cross-section F 6.622409 (185,1295) 0.0000 Cross-section Chi-square 990.719289 185 0.0000 The results of the fixed effect regression is presented in Table 10. It appeared that inflation uncertainty had a negative effect on the capital structure of 55 percent of the firms in the sample. The R-squared of the model is 84 percent which implies that 84 percent of variations in the dependent variable is explained by inflation uncertainty and the probability of F-statistic is zero which shows that the estimated model is significant. In explaining the negative effect of inflation uncertainty on corporate capital structure we can say that Inflation rate uncertainty would increase the firm s business risk by increasing the volatility of the firm s volume of sales, product and input prices. Therefore the volatility of the firm s operating income and its probability of bankruptcy will increase. In appointing the optimal capital structure, it is very important for the management to consider the size and the stability of the firm s cash flows relative to the fixed charges associated with the use of debt. Hence in a highly inflationary environment with heightened inflation uncertainty, a firm which is facing high business risk and uncertain cash flows and needs to raise funds for its investments, may choose to issue new equity and it will decide to keep some unused debt capacity for the future in order to maintain some flexibility. Otherwise, if the firm decides to borrow for its capital needs, it may be forced to issue new shares on unfavorable terms in the future [26]. Assaf (2014) found the same results as Hatzinikolaou et al, (2002), in his master thesis. He considered the effect of inflation uncertainty and discovered that Inflation uncertainty reduces leverage exogenously. It increases business risk, which refers to more volatile operating income, causing tax-shields to become more uncertain. Consequently, reduces the use of debt [27]. In explaining the positive effect of inflation uncertainty, this can be argued that inflation uncertainty increases inflation rate which will increase stockholders expected rate of return, in this case the price of shares will decrease, with increasing stockholders expected rate of return, WACC will rise so most of projects will have negative NPVs and they will not be implemented by firms hence it will have negative effect on GDP growth and this will have a negative effect on capital market so firms will prefer to finance their needs via money market [16]. Table 6: Fixed Effect Regression Variable Coefficient Prob. Variable Coefficient Prob. C 2.139438 0.0000 _94--UNCINF -1.713346 0.0175 _1--UNCINF 0.692265 0.4062 _95--UNCINF 0.144965 0.9815 _2--UNCINF -5.736158 0.2323 _96--UNCINF 0.676659 0.2900 _3--UNCINF -2.024116 0.3861 _97--UNCINF 2.637008 0.5343 _4--UNCINF -8.739667 0.8768 _98--UNCINF 7.054035 0.4459 _5--UNCINF -4.658386 0.5216 _99--UNCINF -5.902304 0.1851 _6--UNCINF -0.751476 0.9944 _100--UNCINF -4.290676 0.3301 _7--UNCINF -33.83055 0.2117 _101--UNCINF -2.163279 0.2947 _8--UNCINF -2.485473 0.5140 _102--UNCINF 2.273068 0.5035 _9--UNCINF -0.953959 0.7636 _103--UNCINF -0.915601 0.0863 _10--UNCINF 1.257887 0.5314 _104--UNCINF 5.113823 0.5089 _11--UNCINF 18.09970 0.2771 _105--UNCINF -0.401611 0.6206 _12--UNCINF -0.026678 0.9957 _106--UNCINF -0.792965 0.6162 _13--UNCINF 0.986955 0.5186 _107--UNCINF -3.863722 0.5653 _14--UNCINF -3.110504 0.9150 _108--UNCINF 42.76710 0.3240 _15--UNCINF -22.09962 0.1693 _109--UNCINF 0.756694 0.0010

528 R.Tehrani and S.N.Khoee, _16--UNCINF -14.89243 0.0636 _110--UNCINF -5.586306 0.5352 _17--UNCINF -3.696763 0.1125 _111--UNCINF 2.630159 0.4853 _18--UNCINF -6.688992 0.7785 _112--UNCINF -1.021871 0.8513 _19--UNCINF 2.728084 0.8516 _113--UNCINF 7.834706 0.6856 _20--UNCINF 1.052396 0.7385 _114--UNCINF -1.585512 0.6159 _21--UNCINF 3.237521 0.6368 _115--UNCINF -1.687281 0.7566 _22--UNCINF -3.851839 0.1102 _116--UNCINF 1.308062 0.3591 _23--UNCINF 0.925409 0.7156 _117--UNCINF 1.499688 0.6447 _24--UNCINF 0.609250 0.8579 _118--UNCINF -0.794201 0.7572 _25--UNCINF 4.115155 0.7596 _119--UNCINF 0.176026 0.9464 _26--UNCINF 5.059661 0.2550 _120--UNCINF 17.84173 0.1046 _27--UNCINF 2.604315 0.4872 _121--UNCINF -1.816637 0.2840 _28--UNCINF 1.567780 0.8033 _122--UNCINF 2.066052 0.4650 _29--UNCINF 17.04044 0.4116 _123--UNCINF -0.643625 0.6504 _30--UNCINF 2.723936 0.3670 _124--UNCINF -1.116355 0.7567 _31--UNCINF -16.66023 0.2853 _125--UNCINF 5.036251 0.3437 _32--UNCINF -2.626774 0.6352 _126--UNCINF -0.439182 0.7842 _33--UNCINF 1.451671 0.6387 _127--UNCINF -0.679854 0.8370 _34--UNCINF -2.547496 0.4613 _128--UNCINF 1.753584 0.7052 _35--UNCINF -0.308610 0.9909 _129--UNCINF 9.774748 0.3430 _36--UNCINF -13.81584 0.0605 _130--UNCINF 13.60518 0.4697 _37--UNCINF 0.417680 0.7414 _131--UNCINF 9.041857 0.6318 _38--UNCINF 8.236466 0.1361 _132--UNCINF -7.641858 0.2600 _39--UNCINF 12.17400 0.6032 _133--UNCINF -2.118265 0.2163 _40--UNCINF 7.364858 0.1121 _134--UNCINF -0.730184 0.8866 _41--UNCINF 10.23642 0.0279 _135--UNCINF -2.083896 0.5211 _42--UNCINF -1.779657 0.6055 _136--UNCINF 1.459180 0.7359 _43--UNCINF 1.990087 0.3395 _137--UNCINF -4.627002 0.3907 _44--UNCINF 1.174731 0.2518 _138--UNCINF -1.510752 0.3792 _45--UNCINF -14.21747 0.0064 _139--UNCINF -0.213264 0.9901 _46--UNCINF -1.630786 0.4373 _140--UNCINF 6.585920 0.4668 _47--UNCINF -18.62324 0.1753 _141--UNCINF 11.90624 0.1711 _48--UNCINF -0.034740 0.9964 _142--UNCINF 0.921792 0.9432 _49--UNCINF 2.630623 0.5341 _143--UNCINF 0.181276 0.9700 _50--UNCINF -3.381117 0.1712 _144--UNCINF -7.414159 0.3496 _51--UNCINF 1.191008 0.6599 _145--UNCINF -0.060857 0.9889 _52--UNCINF 1.873803 0.5939 _146--UNCINF 1.457005 0.3588 _53--UNCINF 4.143569 0.1695 _147--UNCINF -1.191219 0.7218 _54--UNCINF -3.127101 0.5397 _148--UNCINF 6.130345 0.3180 _55--UNCINF 0.156750 0.9467 _149--UNCINF 0.859247 0.8517 _56--UNCINF 2.101983 0.7811 _150--UNCINF -1.542232 0.6373 _57--UNCINF 1.629828 0.4968 _151--UNCINF 0.675600 0.9176 _58--UNCINF 0.099253 0.9185 _152--UNCINF -3.880485 0.8471 _59--UNCINF -2.637010 0.6228 _153--UNCINF -1.875706 0.5239 _60--UNCINF 6.311207 0.0008 _154--UNCINF -4.021130 0.5902 _61--UNCINF -2.539757 0.5739 _155--UNCINF -1.670343 0.5442 _62--UNCINF 0.701447 0.8855 _156--UNCINF -5.164497 0.0185 _63--UNCINF -1.931099 0.2324 _157--UNCINF 4.277773 0.0567 _64--UNCINF -9.073182 0.0355 _158--UNCINF -1.798573 0.8727 _65--UNCINF 0.629026 0.3733 _159--UNCINF -4.710220 0.2348 _66--UNCINF 0.502579 0.6566 _160--UNCINF -2.340539 0.5331 _67--UNCINF 6.849416 0.4884 _161--UNCINF -0.945283 0.4405 _68--UNCINF 2.875554 0.1650 _162--UNCINF -1.822644 0.8178 _69--UNCINF -7.470182 0.7769 _163--UNCINF 0.992917 0.4110 _70--UNCINF -3.978769 0.2567 _164--UNCINF 9.256933 0.0389 _71--UNCINF -8.793982 0.4333 _165--UNCINF -1.205999 0.5493 _72--UNCINF -3.594670 0.4128 _166--UNCINF 0.090752 0.8764 _73--UNCINF -0.786815 0.5087 _167--UNCINF 0.005631 0.9996 _74--UNCINF 0.772738 0.8337 _168--UNCINF -1.314516 0.6873 _75--UNCINF -1.715671 0.7357 _169--UNCINF -1.251218 0.3300 _76--UNCINF -6.016893 0.1230 _170--UNCINF -5.843751 0.6325 _77--UNCINF 4.809149 0.3948 _171--UNCINF -0.512797 0.9699 _78--UNCINF -4.191419 0.4373 _172--UNCINF 3.342123 0.7457

Uncertainty on the Capital Structure of Non-Financial Firms 529 _79--UNCINF -2.519240 0.8960 _173--UNCINF -9.167915 0.3009 _80--UNCINF -7.399763 0.8310 _174--UNCINF 1.977953 0.3505 _81--UNCINF -0.662371 0.9150 _175--UNCINF -4.704006 0.4637 _82--UNCINF -0.003476 0.9987 _176--UNCINF 0.344038 0.8028 _83--UNCINF 0.820276 0.8560 _177--UNCINF 2.817743 0.1472 _84--UNCINF 2.401094 0.2018 _178--UNCINF 2.313665 0.7065 _85--UNCINF 0.796815 0.8366 _179--UNCINF 13.93686 0.1045 _86--UNCINF 2.571740 0.0001 _180--UNCINF 0.963741 0.7281 _87--UNCINF -3.028967 0.4693 _181--UNCINF 0.385571 0.9581 _88--UNCINF -4.489773 0.1464 _182--UNCINF -1.313172 0.1562 _89--UNCINF -6.224011 0.2975 _183--UNCINF 1.403668 0.6618 _90--UNCINF -3.199059 0.1064 _184--UNCINF 6.755151 0.1005 _91--UNCINF 0.487239 0.9521 _185--UNCINF 2.967641 0.3489 _92--UNCINF -0.033338 0.9872 _186--UNCINF -0.781284 0.7324 _93--UNCINF -11.14835 0.0629 Effects Specification Cross-section fixed (dummy variables) Weighted Statistics R-squared 0.882867 Mean dependent var 5.720484 Adjusted R-squared 0.843927 S.D. dependent var 4.028496 S.E. of regression 1.839870 Sum squared resid 3777.795 F-statistic 22.67284 Durbin-Watson stat 1.353442 Prob(F-statistic) 0.000000 Conclusion Managers make their financial decisions according to the sources of financing based on both macroeconomic conditions and firm specifics characteristics. Most of the research on capital structure has focused on the internal variables in order to describe firm specific characteristics influencing their preferences between debt and equity. Recent researches in this area has proved that macroeconomic condition has a significant effect on financing decisions. To our knowledge there is no study which has considered the effect of macroeconomic uncertainty on Iranian firms leverage. As inflation affects economies in various positive and negative ways, we tried to shed light on the effect of uncertainty over future inflation on the capital structure of non-financial firms listed in Tehran Stock Exchange. We used a sample of 186 manufacturing firms for the period from 2007 to 2014 with applying an EGARCH model to proxy for uncertainty. In this paper we used a fixed effect regression and the effect of inflation uncertainty on every firm in the sample was estimated separately. The results revealed that inflation uncertainty had a negative effect on the leverage of 55 percent of the firms in the sample which can be implied that Higher inflation uncertainty will increase firms business risk and the volatility of their revenues and costs, in this case the probability of bankruptcy will rise, therefore firms will use less debt in their capital structure. In explaining the positive effect of inflation uncertainty, this can be argued that inflation uncertainty increases inflation rate which will increase stockholders expected rate of return, in this case the price of shares will decrease, with increasing stockholders expected rate of return, WACC will rise so most of projects will have negative NPVs and they will not be implemented by firms hence it will have negative effect on GDP growth and this will have a negative effect on capital market so firms will prefer to finance their needs via money market [16]. References Damodaran,A. (2010). Applied Corporate Finance (3rd Ed.). WILEY. Modigliani, F. & Miller, M. (1958).The cost of capital, corporation finance and the theory of investments. American Economic Review, 48, 261-97. Modigliani, F. & Miller, M. H. (1963).Corporate income taxes and the cost of capital, American Economic Review, pp. 433-443. Jensen, M., & William Meckling. (1976). Theory of the firm: Managerial behavior, Agency costs and ownership structure, Journal of Financial Economics, 3, 305-360. Myers, S. C. (1977). Determinants of corporate borrowing. Journal of Financial Economics, 5, 147-75. Myers, S. C. & Majluf, N. (1984). Corporate financing and investment decisions when firms have information that investors do not have. Journal of Financial Economics 13, 187 224. Stulz, R. (1990). Managerial discretion and optimal financing policies. Journal of Financial Economics, 26, 3-28. Ross, S. A. (1977). The determination of financial structure: The incentive-signaling approach. Bell Journal of Economics and Management Science, 8, 75-80.

530 R.Tehrani and S.N.Khoee, Baker, M., and Jeffrey W. (2002). Market Timing and Capital Structure. Journal of Finance 57 (February), 1-32. Camara, O. (2012). Capital Structure Adjustment Speed and Macroeconomic Conditions: U.S MNCs and DCs. International Research Journal of Finance and Economics, 84, 106-120 Titman, S., & Wessels, R. (1988).The determinants of capital structure choice. Journal of finance, 43, 1-19. Yang, C. C., Lee, C. F., Gu, Y. X., & Lee, Y. W. (2010). Co-determination of capital structure and stock returns- A LISREL approach: an empirical test of Taiwan stock markets. The Quarterly Review of Economics and Finance, 50, 222-233. Chakraborty, A. (2010).The Impact of Macroeconomic Uncertainty on Firms Changes in Financial Leverage. Accounting and Finance Faculty Publication Series. Paper 3. Mateev, M., Poutziouris, P., & Ivanov, K. (2013).On the determinants of SME capital structure in Central and Eastern Europe: A dynamic panel analysis. Research in International Business and Finance, 2013, 28-51. Bokpin, G. A. (2009). Macroeconomic development and capital structure decisions of firms: Evidence from emerging market economies. Studies in Economics and Finance, 2, 129-142. Mokhova, N., & Zinecker, M. (2014). Macroeconomic factors and corporate capital structure. Procedia - Social and Behavioral Sciences, 110, 530 540. Stretcher, R., & Johnson, S. (2011). Capital structure: professional Management guidance. Managerial Finance, 37, 788-804. Aggarwal, R., & Kyaw, N. A. (2010). Capital structure, dividend policy, and multinationality: Theory versus empirical evidence. International Review of Financial Analysis, 19,140-150. Breally, Richard A, & Myers, Stewart C. (2003). Principles of Corporate Finance (7 th ed.). The McGraw Hill Companies. You, M. T, & He, K. (2011).The Effect of Firm-Specific Variables and Macroeconomic Condition on Capital Structure: Evidence of Non-Linear Behaviors. Master thesis. Lund university-school of Economics and Management. Frank, M. Z., & Goyal, V. K. (2003). Capital Structure Decisions. AFA 2004 San Diego Meetings. Available at SSRN: http://ssrn.com/abstract=396020 or http://dx.doi.org/10.2139/ssrn.396020 Frank, M. Z., & Goyal, V. K. (2009). Capital Structure Decisions: Which Factors are Reliably Important? Financial Management, 38 (1), 1 37. Joeveer, K. (2013). Firm, country and macroeconomic determinants of capital structure: Evidence from transition economies. Journal of Comparative Economics, 41, 294-308. Bhattacharjee, A. & Han, J. (2014).Financial distress of Chinese firms: Microeconomic, macroeconomic and institutional influences. China Economic Review, 30, 244-262. Brooks, Chris. (2008). Introductory to econometrics for finance (2 nd ed.). Cambridge university press. Hatzinikolaou, D., Katsimbris, G. M., & Noulas, A. G. (2002). Inflation uncertainty and capital structure Evidence from a pooled sample of the Dow-Jones industrial firms. International Review of Economics and Finance. 11, 45 55. Assaf, A. (2014).Capital structure and inflation uncertainty: evidence from a pooled sample of Dutch firms. Master thesis in finance. TILBURG University- School of Economics and Management. Baum, C. F., Caglayan, M., Ozkan, N., Talavera, O. (2006).The impact of macroeconomic uncertainty on non-financial firms demand for liquidity. Review of Financial Economics, 15, 289 304.