2013 Seventh International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing
Macroeconomic Condition and Capital Structure Adjustment Speed - Evidence from the Indonesian Stock Market Shyh-Weir Tzang
Kuei-Yuan Wang
Relia Novita Rahim
Department of Finance, Asia University, Taichung,Taiwan Email:
[email protected]
Department of Finance, Asia University, Taichung,Taiwan, Corresponding author Email:
[email protected]
Department of Finance, Asia University, Taichung,Taiwan Email: reily
[email protected]
[5] were the first to simultaneously endogenize both the adjustment factor and the target leverage ratio. In addition to identifying the determinants of the target capital structure, their setup allows to estimate the speed of adjustment towards the target capital structure and to identify the determinants of the speed of adjustment simultaneously. On the other hand, [6] stated that the adjustment speed varies across firms and time period because of the varying adjustment costs incurred by the firms. The adjustment costs of the companies vary over the different types of companies. There are some factors that can affect the adjustment costs of the companies which in turn affect the adjustment speed to the target capital structure. While [3] stated that macroeconomic factors do not seem to have an important impact on the firms capital structure decisions as firm level factors. As the country becomes richer, firms continue to be financed by debt. The growth rate of a country has a huge effect on the leverage decisions of firms. As the country grows, leverage also increases. In emerging markets, firms consider the cost of interest when they are financed by long term debt. It seems that they do not take cost of interest into account for short term debt. Recent studies found that cost deviation from target leverage can affect the adjustment speed. Firms often face financial deficits/surpluses, and these circumstances provide a convenient time for them to adjust their capital structures with low transaction costs. If the adverse selection/transaction costs are higher for equity than they are for debt, firms with financial surpluses are more likely to reduce debt than equity in order to preserve the debt capacity for future financing needs and to avoid the higher costs of re-issuing equity. Thus, adjustments toward a target can be asymmetric in the sense that firms weigh differently positive and negative deviations of their leverage ratio from a target. Specifically, when the cost of deviation from target leverage are relatively higher when firms are over-levered than when they are under-levered suggesting over-levered firms possibly have more incentives or are under greater pressures to adjust toward target leverage than their under-levered counterparts ([7]).
Abstract—This paper presents the impact of the macroeconomic condition on the speed of adjustment of capital structure for non-financial firms listed in the Indonesian Stock Exchange from 1992 to 2010. Based on a two-stage OLS and integrated partial adjustment approach, the paper finds that Indonesian firms adjust their leverage faster in bad economic condition and the adjustment speed of the over-levered firms is higher than the under-levered firms. By controlling the variable of GDP growth rate, the over-levered firms exhibit a faster adjustment on their capital structure measured by book value but an insignificant adjustment measured by market value. By controlling the variable of inflation rate, the over-levered firms show a faster adjustment in their capital structure measured by book value when the GDP growth rate is high. In contrast with previous literature, the results provide different interpretations about the adjustment speed toward target leverage measured by book and market debt ratios. Index Terms—capital structure, integrated partial adjustment
I. I NTRODUCTION This paper tries to examine the degree to which the Indonesian macroeconomic conditions affect the speed of capital adjustment for firms listed in the Indonesian stock exchange. [1] argue that, as an economy, firms will finance with more equity due to higher efficiency in the financial intermediation of capital markets. This implies that the debt ratio of firms is negatively related to economic development due to the greater equity used in the course of economic development. [2] stated that the macroeconomic condition affects firms adjustment speed in its capital structure. They found that firms adjust their capital structure faster in favorable macroeconomic condition. [3] investigated the determinants of capital structure of firms for 25 developing countries and found that macroeconomic conditions do have an impact on the capital structure decisions of firms. However, [4] found that when they define either good or bad state as an additional explanatory variable the model, the Spanish firms adjust their capital structure back to target leverage faster in bad state than in good state. This result contradicts with the previous international evidence. 978-0-7695-4974-3/13 $26.00 © 2013 IEEE DOI 10.1109/IMIS.2013.141
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Moreover, [8] studied U.S firms from 1971-2008 and found that over-levered and under-levered firms will adjust toward their target leverage at different rates. Recent literature also mentioned the proof according to the effects of under-levered, over-levered firms and macroeconomic conditions on the speed of adjustment to target capital structure. [9] stated that in the application of the partial adjustment model with financial constraint of over-leverage and under-leverage taken into account, this thesis finds that economic development affects the adjustment behaviour of capital structure decisions. The result shows that the evidence of a positive effect of the long term shifts in the level of economic development on the debt ratio adjustment and actual debt ratio for firms with financial constraint of under-levered. [10] found that when good and bad states of macroeconomic conditions are defined by the term spread, gross domestic product (GDP) growth, default spread, and dividend yield, over-levered firms tend to adjust toward the target faster than under-levered firms in good state than in bad state based on U.S. data from 1977-2006. From above discussion, it is proven that the adjustment speed is not asymmetric and it can be influenced by some variables and factors. However, no study has been done using firms listed in the Indonesian Stock Exchange. The differences in institutional and historical environment of Indonesia and the fact that Indonesian economy is still recovering from economic crisis are expected to deliver new results in this topic. Several purposes are outlined in this study: 1) To explore whether the macroeconomic condition has an impact on capital structure adjustment speed on firms listed on Indonesia Stock Exchange. 2) To explore whether over- and under-levered firms has different reactions in their capital structure adjustment speed for firms listed on Indonesia Stock Exchange. The paper is structured as follows. Section II presents three hypotheses and models used to test them. Section III is the empirical results. Section IV is the conclusion.
calculated as total debt to total assets (defined as total debt plus equity): MDi,t =
•
•
II. H YPOTHESES , VARIABLES AND MODELS
SDi,t + LDi,t , SDi,t + LDi,t + Si,t Pi,t
(2)
where Si,t Pi,t denotes the number of common shares outstanding multiplied by the stock price per share at time t, which denotes the market value of firm is equity. Firm-level factors We follow the variables defined by the literature that has concentrated on figuring out the determinants affecting the optimality of capital structure and providing theoretical explanations of the relationships between firm characteristics and capital structure ([11], [12], [13], [14]). The firm-level characteristics are defined as follows ([10], [4]): profitability, size, tangibility, net debt tax shield, selling expense and growth. Macroeconomic condition Due to the limitations of macroeconomic data of Indonesia, the macroeconomic indicator used in this paper only includes inflation rate and GDP growth rate. By following the definition of [10] for GDP growth rate, good macroeconomic states are defined as the highest quintile factor years, moderate macroeconomic states in the mid-three quintiles factor years and bad macroeconomic states with the lowest quintile factor years since good states are defined as higher current GDP growth rate. For the inflation rate, the zero percent inflation may appear ideal, but it is neither practical nor desirable. A moderate rate of inflation—1 to 2 percent—is considered desirable by a vast majority of economists. An inflation rate of up to 5 percent is tolerable. Double-digit inflation rates, however, are definitely considered undesirable by most economists. For inflation rate, bad macroeconomic states are defined as the highest quintile factor years, moderate macroeconomic states in the mid-three quintile factor years, and good macroeconomic states with the lowest quintile factor years. The quintile years by inflation rate and growth rate are summarized in Table I.
A. Hypotheses We propose three hypotheses in the paper to clarify the relationship between macroeconomic condition and speed adjustment in capital structures of Indonesian companies: H1: In good (bad) state, the adjustment speed of capital structure is slower (faster). H2: For an over-levered (under-levered) firm, its adjustment speed of capital structure is faster (slower).
C. Model specification 1) Macroeconomic condition and target leverage: A dynamic panel data model was used to analyze the impact of adjustment costs and firm-level variables on optimal leverage ratios. We follow the setup of [10] and let the optimal leverage ∗ . It is specified as ratio for firm i at time t be denoted as Di,t a function of a vector of the firm and time varying variables. Specifically, in this model the optimal leverage is allowed to vary across firms and over time. Since the factors that determine a firm’s optimal leverage may change over time, it is likely that the optimal leverage ratio itself may also move over time for the same firm. By accommodating the dynamic nature of a firm’s capital structure decision, the model is specified as follows:
B. Definition of variables •
Leverage ratio BDi,t =
SDi,t + LDi,t , TAi,t
(1)
where SDi,t + LDi,t is the sum of firm i s short-term and long-term book debt at time t, and TAi,t denotes the net assets. The second approach is market leverage ratio
∗ = γMacrot−1 + βXi,t−1 , Di,t
785
(3)
TABLE I D EFINITION OF GOOD AND BAD STATE Quintile
Inflation Rate
GDP Growth Rate
Year
Rate
State
Year
Rate
2000 2009 2010 1997
3.72 4.81 5.13 6.23
G G G G
1998 1999 2001 2002
-13.13 0.79 3.64 4.50
B B B B
21-40%
2004 2007 2003 1992
6.24 6.41 6.59 7.53
M M M M
2009 1997 2003 2000
4.58 4.70 4.78 4.92
M M M M
41-60%
1996 1994 1995 1993
7.97 8.52 9.43 9.69
M M M M
2004 2006 2005 2008
5.03 5.50 5.69 6.01
M M M M
61-80%
2008 2005 2001 2002
9.78 10.45 11.50 11.88
M M M M
2010 2007 1992 1993
6.10 6.35 7.22 7.25
M M M M
13.11 B 1994 7.54 20.49 B 1996 7.64 58.39 B 1995 8.40 moderate state; B: bad state
G G G
0-20%
2006 1999 1998 *G: good state; M: 81-100%
on the capital structure adjustment speed by including the partial adjustment and firm fixed effects in one integrated capital structure model. Specifically, we model the target debt level of firm i in period t(Di,t ) as a linear function of a set of lagged macroeconomic variables (Macrot−1 ) and firm characteristic variables (Xi,t−1 ) , which are the same as in Eq. (1). The standard partial adjustment model is equivalent to Eq. (2). By substituting (1) into (2) and rearranging, the integrated model is as follows:
State
Di,t = (1 − δ)Di,t−1 + δβXi,t−1 + δγMacrot−1 + εi,t . (5) The speed of capital structure adjustment is estimated from Eq. (5) across the good and bad macroeconomic states, respectively. We will estimate Eq.(5) in the paper based on the robust t-statistics with standard errors corrected for heteroskedasticity. 3) Overleverage, underleverage and speed adjustment towards target: [16] stated that a company will defined as being either over-levered or under-levered depending on its ratio of debt to property, plant and equipment (PPE). Furthermore, [15] stated that debt capacity (Debt/PPE) has important implications for both future financing and capital structure adjustment speed. To test hypothesis 2, we use the formula as follows: ∗ = βXi,t−1 . Di,t
where D∗ is the target leverage ratio, Macro and X denote the vector of macroeconomic conditions and firm-level factors, respectively. Similar to [10], we use the quasi-maximum likelihood estimation method (QMLE) to estimate the fitted value of Eq. (3) which is used as the proxy for target leverage. Under the ideal conditions, Di,t , the observed leverage of firm i at time t, should not be different from the optimal leverage, i.e., ∗ . In a dynamic setting, this implies that the change Di,t = Di,t in actual leverage from the previous to the current period should be exactly equal to the change required to attain optimal at time t. However, if adjustments using external finance are costly, as reflected in Di,t , then firms may not find it optimal to adjust fully, but only adjust partially, which is represented as: ∗ − Di,t−1 ) + εi,t (4) Di,t − Di,t−1 = δ(Di,t
(6)
Under the trade-off theory, firms adjust their leverage towards an optimal level resulting from balancing the benefits and costs associated with debt financing. The recent empirical literature argues that adjustment costs may prevent firms from quickly offsetting the deviations from target leverage. Following the recent papers from [15], the partial adjustment estimation procedure is adopted to model the dynamics of leverage: ∗ − Di,t−1 ) + εi,t . Di,t − Di,t−1 = δ(Di,t
(7)
A positive and below one δ would suggest that firms are reverting their leverage towards its optimal level overtime while δ above one reveals the absence of a target ratio. Substituting (6) into (7) and rearranging yields the following: Di,t = (1 − δ)Di,t−1 + δβXi,t−1 + εi,t
where δ represents the proportion of deviation away from the firms target leverage, closed by the firm between period t − 1 and period t. In other words, the estimated δ captures the adjustment speed back toward target leverage, which is the main focus of this thesis. δ = 1 indicates that firms fully adjust for any deviation away from their targets. In the presence of adjustment costs, as in this paper, δ is expected to be less than 1. 2) Integrated dynamic partial-adjustment capital structure model: By evaluating the two-stage estimation procedure, [15] show that the partial adjustment speed reflected by the coefficient on target leverage from first-stage regressions is abnormally smaller than theory would predict and that the long-term elasticity of the observed debt ratio relative to its target is significantly different from unity. Thus, following [15] and [10], we estimate the impact of macroeconomic conditions
(8)
To test hypothesis 3, Eq. (3), (4) and (5) will be estimated across over- and under-levered firms. To account for the fractional data in linear models, we follow [17] and use the quasi-maximum likelihood estimation method (QMLE) to estimate the fitted value of Eq. (3) and (6) as the proxy for target leverage. 4) Data: The data are obtained from the COMPUSTAT of the Indonesian Stock Exchange from 1992 to 2010 and are divided into 3 different types consisting of manufacturing, non-manufacturing and miscellaneous. The entire populations are 5,131 firms for 18 years observations. Firm-level factors such as profitability, size, growth, tangibility, selling expenses and net debt tax shields are also collected from COMPUSTAT. Data for relevant information on the macroeconomic condition are collected from World Bank website.
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TABLE II S TATISTICS OF LEVERAGE
TABLE III S TATISTICS OF LEVERAGE ACROSS STATES
BD
year
inflation rate
GDP growth rate
mean
median
std.
obs
Panel A: BD
mean
median
mean
median
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
0.434 0.448 0.422 0.450 0.494 0.636 0.784 0.686 0.748 0.698 0.553 0.516 0.529 0.514 0.521 0.545 0.840 1.171 0.494
0.443 0.475 0.425 0.459 0.509 0.670 0.739 0.668 0.671 0.575 0.472 0.470 0.488 0.495 0.498 0.508 0.520 0.475 0.476
0.173 0.177 0.177 0.188 0.186 0.223 0.412 0.385 0.565 0.640 0.506 0.324 0.323 0.296 0.307 0.374 4.651 10.509 0.312
32 34 52 56 83 133 157 163 174 188 194 198 203 210 219 216 242 240 228
Good Bad G vs. B
3.8899 1.9932 1.8967
2.3643 1.9249 0.4394
1.3866 2.8313 -1.4447
1.4096 2.6567 -1.2471
Panel B: MD Good Bad G vs. B
0.1216 0.0670 0.0545
0.0043 0.0043 0.0000
0.0473 0.0908 -0.0435
0.0012 0.0053 -0.0041
Overall
0.646
0.518
3.260
3,022
and GDP growth rate. The table reports the resulting of estimating Eq. (3) by controlling the firm fixed effects and adjusting standard errors for time clustering across good and bad states. Following [10], during model estimation, firm fixed effect has been controlled since [15] find that this increase adjustment speed, and also standard errors calculated by time because the residuals could be correlated across firms or across time. Columns 2 through 4 in Panel A show that when the leverage ratio is defined by book value, firms adjust slower in good state as reported in LAGLEV (51.56%=1-48.44%) than in bad state (63.68%=1-36.32%). GOODDUMMY*LAGLEV, the estimate on the interaction term between the lagged leverage ratio and good state dummy in the pooled regression, is significantly positive, which further confirms the evidence that the adjustment speed is faster in bad state than in good state. Columns 2 through 4 in Panel B show that when macroeconomic condition is defined by GDP growth rate, firms are also found to adjust slower in good state as reported in LAGLEV (13.14%=1-86.86%) than in bad state (25.58%=174.42%). GOODDUMMY*LAGLEV is significantly positive and further provides evidence that the adjustment is faster in bad state than in good state. In conclusion, when the macroeconomic condition is defined by the inflation rate and GDP growth rate, the speed of adjustment is faster in bad state than in good state. Columns 5 through 7 in Table IV show the results when the leverage ratio is defined by market value. Panel A shows that when macroeconomic condition is defined by inflation rate, firms are found to adjust faster in good state as reported in LAGLEV (85.65%=1-14.35%) than in bad state (30.94%=169.06%). However, a significant positive estimate of GOODDUMMY*LAGLEV (0.1130) from the pooled-state regression indicates that the adjustment speed is faster in bad state than in good state. Therefore, we have mixed results from the leverage ratio defined by market value. But we also note that the estimate of LAGLEV in good state is insignificant such that we cannot conclude the adjustment speed to be faster for firms measured by market-valued structure in good state than in bad state. Panel B shows the results when macroeconomic conditions based on GDP growth rate. This panel shows that the firm
III. E MPIRICAL RESULTS A. Statistics of leverage Table II provides statistics summary on the unbalanced (actual) data with total 3,022 firms. BD is the sum of the firms short-term and long-term book debt divided by net assets, and MD is the sum of the firms short-term and long-term book debt divided by the number of common shares outstanding multiplied by the stock price per share, which denotes the market value of the firms equity. The number of the sample firms ranges from 32 firms in 1992 to 228 in 2010 for BD, while for MD the number of firms ranges from 21 in 1992 to 179 in 2010. Due to limited space, statitics of MD are skipped and there is no significant trend found for MD. However, BD decreases in period 1992-1994 and increases in period 1994-1998 and 2005-2008. The highest BD is found in 1998 and 2009 which should be related to the Asia financial crisis in the period 1998 and subprime financial crisis in 2007-2009. B. Statistics of leverage across states Table III shows the summary statistics across the bad states and good states. This table reports both the mean and median. When the macroeconomic condition based on the inflation rate is good, the mean of leverage ratio is higher for BD and MD. But the result reverses for both BD and MD when the macroeconomic condition defined by GDP growth rate is bad, which is different from the findings of [10] that higher debt is usually found in in bad states. C. Integrated dynamic partial adjustment model across macroeconomic conditions Table IV shows the results of the adjustment speed of firms across macroeconomic conditions defined by the inflation rate
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overly adjusts (-2.51%=1-102.51%) in good state as reported in LAGLEV in contrast with the adjustment speed in bad state (27.83%=1-72.17%). Since the expected value of δ should be less than 1, we think this result should be caused by limited number of observations from firms defined in good state in that there are only 81 observations in contrast with 1,969 observations for firms defined in bad state. Based on the results from above, we conclude that firms adjust faster in bad state when leverage ratio is measured by the book value. The results tend to support the hypothesis that Indonesian firms adjust their leverage faster in bad state than in good state. However, the results of adjustment speed are inconclusive across macroeconomic conditions when firms leverage is defined by market value.
to firm valuation. Since then many researcher research the field of capital structure. One of the fields is about target capital structure and speed adjustment. While many researches have been conducted in this field, the other idea arises to seek the factors determine the adjustment process towards target leverage. [10] and [9] agree that macroeconomic condition plays an important role in determining the adjustment speed and firms financing choices because capital structure choices are quite diverse and much dependent on the time and the firms leverage type. We present in this paper the impact of the macroeconomic condition on the adjustment speed of capital structure for non-financial firms listed in Indonesian stock exchange from 1992-2010. Furthermore, the under- and over-levered firms are also researched to find the degree to which the firms leverage affects the speed of adjustment and the relationship between levered firms and macroeconomic conditions. The results show that when the macroeconomic condition is defined by inflation rate, the Indonesian firms adjust their capital structure faster in bad states when leverage ratio is defined by book value. When the macroeconomic condition is defined by GDP growth rate, the adjustment speed is greater when leverage ratio is defined by book value. Indonesian firms adjust faster in bad states when the leverage ratio is defined by either book or market value, a result similar to [4]. When the firms grouped by over- and under-levered firms, the over-levered firms adjust faster when leverage ratio is defined by either book or market value. This result is similar with [19]. The relationship between macroeconomic conditions and over- and under-levered firms are insignificant. The conclusions in this paper may be limited to the data availability that only 18 years of the period 1992-2010 are considered. Patterns of firms financing decisions might not be observed clearly within 18 years. The financing decisions and the effect of macroeconomic condition may be more completely portrayed if the observed period could be extended. The macroeconomic condition defined by the paper only includes inflation rate and GDP growth rate. This may also deliver inconsistent results delivered by both measures. However, we at least confirm two hypotheses out of three hypotheses proposed in the paper.
D. Integrated dynamic partial adjustment model for underand over-levered firms Table V shows that when leverage ratio is defined by the book value, over-levered firms adjust faster than underlevered firms by comparing their estimated coefficients LAGLEV (63.57% and 50.59%, respectively). In one year, firms generally adjust 61.77% (1-38.23%) and the coefficient estimate on the interaction term between the lagged leverage ratio and levered dummy in the pooled regression LEVDUMMY*LAGLEV shows the positive number means the overlevered firms adjust faster than the underlevered firms. Moreover, the positive relationship between TARGDIF*LAGLEV shows that firms adjust through external financing. The R-squared number shows 0.7326, it is high number and this could mean that there is a close association between the values y and the values predicted by model. The result when leverage ratio is in terms of market value shows that the firm adjusts 33.85% (1-66.15%) when firms are overlevered as reported in LAGLEV, and adjusts 5.23% (1-94.77%) when firms are underlevered. In one year, firms generally adjust 66.59% (1-33.41%) and the coefficient estimate on the interaction term between the lagged leverage ratio and levered dummy in the pooled regression LEVDUMMY*LAGLEV shows the positive number means the over-levered firms adjust faster than the underlevered firms. Moreover, the positive relationship between TARGDIF*LAGLEV shows that firms adjust through external financing. The R-squared number shows 0.8617, it is high number and this could mean that there is a close association between the values y and the values predicted by model. By the results in the above, when leverage ratio is defined by market value, the over-levered firms seem to adjust slower than under-levered firms in good state defined by inflation. However, there is no obvious relationship from the results when macroeconomic condition is defined by GDP growth rate. The conclusion about the empirical results is that H1 and H2 are accepted.
R EFERENCES [1] J. Boyd and B. Smith, “The coevolution of the real and financial sectors in the growth process,” World Bank Economic Review, vol. 10, no. 2, pp. 371–396, 1996. [2] W. Drobetz, P. Pensa, and G. Wanzenried, “Firm characteristics, economic conditions, and capital structure adjustments,” University of Basel, Working paper, 2006. [3] M. G. Bas, T. and K. Phylaktis, “Determinants of capital structure in emerging markets,” Cass Business School, Working paper, 2009. [4] G. Rubio and F. Sogorb, “The adjustment to target leverage of Spanish public firms: Macroeconomic conditions and distance from target,” Revista de Economi’a Aplicada, vol. 19, no. 57, pp. 35–63, 2011. [5] S. Banerjee, A. Heshmati, and C. Whilborg, “The dynamics of capital structure,” Research in Banking and Finance, vol. 4, pp. 275–297, 2004. [6] J. Mahakud and S. Mukherjee, “Determinants of adjustment speed to target capital structure: Evidence from indian manufacturing firms,” in International Conference on Economics and Finance Research, vol. 4. IACSIT Press, 2011, pp. 67–71.
IV. C ONCLUSION Capital structure research started in 1958 when [18] derive conditions under which the capital structure choice is irrelevant
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TABLE IV I NTEGRATED RESULTS OF ADJUSTMENT SPEED FOR MACROECONOMIC CONDITION book leverage Good
market leverage
Bad
G vs. B
Panel A. Results from regressions when states are determined by inflation 0.3632∗∗∗ 0.4431∗∗∗ LAGLEV 0.4844∗∗∗ (0.1342) (0.0876) (0.079) GOODDUMMY −0.1033∗∗ (0.0335) GOODDUMMY*LAGLEV 0.2437∗∗∗ (0.0647) obs 670 1, 952 2, 622 R-square 0.2301 0.6248 0.6786 Fixed effect test No Yes Yes
Good
Bad
G vs. B
0.1435 (0.1672)
0.6906∗∗∗ (0.0574)
514 0.8844 Yes
1, 496 0.8377 Yes
0.7144∗∗∗ (0.0419) 0.0007 (0.0006) 0.1130∗ (0.0659) 2, 050 0.836 Yes
Panel B. Results from regressions when states are determined by GDP growth rate LAGLEV 0.8686∗∗∗ 0.7442∗∗ 0.4761∗∗∗ 1.0251∗∗∗ (0.0678) (0.0618) (0.0863) (0.0122) GOODDUMMY −0.1619∗∗ (0.057) GOODDUMMY*LAGLEV 0.2701∗∗ (0.1034) obs 129 2, 593 2, 722 81 R-square 0.8059 0.6029 0.6793 0.9684 Fixed effect test Yes Yes Yes No
0.7217∗∗∗ (0.0313)
1, 969 0.8328 Yes
0.7317∗∗∗ (0.0303) −0.0021 (0.002) −0.163 (0.1341) 2, 050 0.8344 Yes
TABLE V I NTEGRATED RESULTS OF ADJUSTMENT SPEED FOR OVER - AND UNDER - LEVERED FIRMS book leverage
LAGLEV
under-levered
over-levered
all
under-levered
over-levered
all
0.4941∗∗∗ (0.0697)
0.3643∗∗∗ (0.1058)
0.3823∗∗∗ (0.0579) −0.0384 (0.0046) 0.3902∗∗∗ (0.0775) 0.1421∗∗∗ (0.0308)
0.9477∗∗∗ (0.0577)
0.6615∗∗∗ (0.0882)
0.3341∗ (0.1983) −0.0014 (0.0027) 0.3360∗∗ (0.1389) −0.2909∗ (0.1587)
1, 202 0.8529 Yes
1, 965 0.6394 Yes
2, 715 0.7326 Yes
916 0.9665 No
1, 498 0.9102 Yes
2, 063 0.8617 Yes
LEVDUMMY LEVDUMMY*LAGLEV TARGDIF*LAGLEV obs R-square Fixed effect test
market leverage
[7] S. Byoun, “How and when do firms adjust their capital structures toward targets?” The Journal of Finance, vol. 63, no. 6, pp. 3069–3096, 2008. [8] W. B. Elliott, J. Ko¨eter-Kant, and R. S. Warr, “Target debt ratios, adverse selection costs, and equity mis-pricing,” University of Texas, Working paper, 2009. [9] H. H. Yeh and E. Roca, “Macroeconomic condition and capital structure: Evidence from taiwan,” Griffith Business School, Working paper, 2010. [10] D. Cook and T. Tang, “Macroeconomic conditions and capital structure adjustment speed,” Journal of Corporate Finance, vol. 16, pp. 73–87, 2010. [11] S. Titman and R. Wessels, “The determinants of capital structure choice,” The Journal of Finance, vol. 43, no. 1, pp. 1–19, 1988. [12] M. Harris and A. Raviv, “The theory of capital structure,” The Journal of Finance, vol. 46, no. 1, pp. 297–355, 1991. [13] I. Welch, “Columbuss egg: The real determinant of capital structure,” National Bureau of Economic Research, Working paper 8782, 2002. [14] M. Z. Frank and V. K. Goyal, “Trade-off and pecking order theories of debt,” in Handbook of Corporate Finance: Empirical Corporate Finance, B. Eckbo, Ed. Amsterdam, Elsevier/North-Holland, 2008, vol. 2, ch. 12, pp. 136–197. [15] M. Flannery and K. Hankins, “A theory of capital structure adjustment speed,” Working paper, 2007. [16] O. Camara, “Capital structure adjustment speed and macroeconomic condition: U.S MNCs and DCs,” International Research Journal of Finance and Economics, vol. 84, pp. 106–120, 2012.
[17] L. Papke and J. Woolridge, “Econometric methods for fractional response variables with an application to 401(K) plan participation rates,” Journal of Applied Econometrics, vol. 11, pp. 619–632, 1996. [18] F. Modigliani and M. H. Miller, “The cost of capital, corporation finance, and the theory of investment,” American Economic Review, vol. 48, pp. 655–669, 1958. [19] S. Kasbi, “Ownership concentration and capital structures adjustments,” Paris-Dauphine University, Working paper, 2009.
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