EJEFAS_41_09

European Journal of Economics, Finance and Administrative Sciences ISSN 1450-2275 Issue 41 (2011) © EuroJournals, Inc. 2011 http://www.eurojournals.com

Testing the Pecking Order and the Target Models of Capital

Structure: Evidence from UK

Mousa F. Al Manaseer Corresponding Author, Assistant Professor of Finance, Chairman

Department of Finance and Banking, Business School Mu’tah University, Al Karak- Jordan

Tel: 00962-779818475 E-mail: [email protected]

Eleimon Gonis

Senior Lecturer in Accounting and Finance Programme Manager: MSc Finance, Centre for Global Finance

Bristol Business School, University of the West of England, Bristol E-mail: [email protected]

Riyad Mohamad Al-Hindawi

Dean, The Institute of Banking Studies (IBS), Amman-Jordan Email: [email protected]

Iaad Issa Sartawi

Faculty of Economics & Administrative Sciences Yarmouk University, Irbed- Jordan

E-mail: [email protected]

Abstract Purpose – This study investigates empirically two of the most influential theories of capital structure: the pecking order and the target capital structure theories in the UK market during the period 1999-2004. Design/ methodology/ approach - The study employs and extends the approaches used by Shyam-Sunder and Myers (1999), and Frank and Goyal (2003) to investigate the pecking order theory. In addition, it uses the partial adjustment model in order to investigate the target capital structure theory. Findings – The results show support for the pecking order theory against target capital structure theory, in which firms prefer to finance their deficits through debt rather than moving toward a target capital structure. Research limitations/implications – The study covers the 1999-2004 period. The scope of the study is limited to firms listed in London Stock Exchange. Originality/value – The study employs new approaches to investigate the pecking order theory and target capital structure in the UK market. Furthermore, the study extends and modifies the approaches of Shyam-Sunder and Myers (1999), and Frank and Goyal (2003). Article Type – Research paper. Keywords: Pecking order theory, Target capital structure, Partial adjustment model, UK

85 European Journal of Economics, Finance and Administrative Sciences – Issue 41 (2011)

1. Introduction Since the seminal work of Modigliani and Miller (1958), researchers have continued to investigate how firms finance their projects. Modigliani and Miller (1958) argue that the firm’s value is independent of its financial structure under certain assumptions. However, Modigliani and Miller (1963) contend that due to the deduction of interest payments from the firm’s taxable income, the capital structure mix is relevant to a firm’s value.

The pecking order theory is an application of the asymmetric information theory proposed by (Myers and Majluf, 1984). According to the asymmetric information theory, the managers of a firm (the insiders) are likely to have private information about the firm’s condition, return and investment prospects. Hence, the choice of the firm’s capital structure signals to the investors (outsiders) the information of the insiders. The pecking order theory proposed by Myers (1984) suggests that firms finance their projects by using internal resources first, then through using debt, and finally, by issuing equity. A change in debt levels happens when there is an imbalance between internal cash flow, net of dividends, and real investment opportunities (Shyam-Sunder and Myers, 1999). In the same context, Frank and Goyal (2003) argue that under the pecking order theory, it is the firm’s deficit that matters. A unit increase in any of the components of the deficit must have the same unit impact on the change of debt.

The target capital structure theory suggests that firms have a long-run target capital structure. They use debt, equity or both in order to move toward their target capital structure (Myers, 1984). The theory predicts that the overall target will be a function of bankruptcy risk and tax. Moreover, the composition of debt will depend on the company’s size, asset composition, and the uncertainty about future inflation rates.

The purpose of this study is to, empirically; investigate the evidence of the pecking order and target capital structure theories in the UK market during the period 1999-2004.

The current study differs from previous studies in different ways. For example, the study examines the pecking order theory in the UK market whereas, the vast bulk of existing research focus on the US market (see for example, Shyam-Sunder and Myers (1999) and Frank and Goyal (2003)). Moreover, the study extends and modifies the approaches suggested by Shyam-Sunder and Myers (1999), and Frank and Goyal (2003) for testing the pecking order and target capital structure theories in UK market. Finally, the study employs new approaches to investigate the pecking order theory and target capital structure in the UK market. These approaches will be discussed in more detail in Section 3.

The remaining of this paper is organized as follows: section two reviews the existing literature on the pecking order and the target capital structure theories. Section three discusses the sample, data and the models employed. The results of the data analysis are reported and discussed in section four. Finally, conclusions are drawn in section five. 2. Literature Review 2.1. Pecking Order Theory

The pecking order theory is one implication of how asymmetric information influences investment and financing choices (Myers, 1984). According to the asymmetric information theory, a firm’s capital structure is designed to mitigate the inefficiencies in the firm’s investment decisions caused by the information asymmetry. Because investors have less information than managers regarding the value of the firm, it follows that the value of the issued equity will be under-priced by the market. One way to avoid this situation is through financing with a security not undervalued by the market, such as internal funds or riskless debt (Myers and Majluf, 1984). Based on this argument Myers (1984) proposed “the pecking order” theory of financing, which claims that firms prefer to raise their new investment, first from their internal funds through using retained earnings, second by low-risk debt, and finally by issuing equity.


The pecking order theory presumes that managers who make financing decisions do not really take into consideration an optimal capital structure. Instead, they simply take the “path of least resistance” and choose what appears to be the low-cost financing instrument, which is debt (Myers, 1984). In other words, changes in debt ratios are driven by the need for external funds, not by any attempt to reach an optimal capital structure (Shyam-Sunder and Myers, 1999).

Changes in debt levels happen when there is an imbalance between internal cash flow, net of dividends, and real investment opportunities. Thus, highly profitable firms with limited investment opportunities try to reduce their debt levels, while firms that have more investment opportunities, will borrow more debt. Shyam-Sunder and Myers (1999) proposed a model to investigate the pecking order theory. They claim that if pecking order theory holds, the slope of a firm’s deficit equals one, and the coefficient of the intercept is zero when regressing them to the change of debt in year t. Moreover, they assume that each component of the financing deficit should have the predicted dollar-for-dollar impact on corporate debt.

Frank and Goyal (2003) contended that the pecking order theory implies that the financing deficit should wipe out the effects of other explanatory variables. Firms usually issue either debt, equity, or both to finance their deficit. The total amount of debt issued and/or equity issued from one year to another must, by definition, be equal to the total deficit at the end of the year. A unit increase in any of the components of the deficit must have the same unit impact on the change of debt. Chirinko and Singha (2000) questioned the interpretation of Shyam-Sunder and Myers (1999) regression test. They note that the Shyam-Sunder and Myers’s assumption about the slope of the deficit (should be close to one) is neither a necessary nor a sufficient condition for the pecking order theory to be valid. They claim that the slope coefficient could fall well short of unity when the pecking order theory holds, and be close to unity when it does not.

Several studies have investigated the empirical evidence of the pecking order theory in the US market. Shyam-Surnder and Myers (1999) found that the pecking order is an excellent first-order descriptor of corporate financing behavior. Furthermore, when testing the pecking order model and target adjustment model jointly, the results show a greater confidence in the pecking order theory than in the target adjustment model. Frank and Goyal (2003) found that firms’ deficit does not wipe out the effects of the conventional variables that affect capital structure mixture (tangibility, market to book ratio, size, and profitability) [1]. Leary and Roberts (2005) found evidence that firms are less likely to use external capital markets when they have sufficient internal funds, but are more likely to use it when they have large investment needs. Fama and French (2002) found that firms with more investment opportunities have less market leverage. This result is in line with the tradeoff model and complex version of the pecking order theory.

In the UK market, Panno (2003) found a negative effect of the available reserves which are used as a proxy for internally generated funds. Jordan et al. (1998) found that the pecking order theory is a very important determinant of capital structure in UK small firms. Chittenden et al. (1996) showed that the pecking order theory emerges as a good explanation of a capital structure with a heavy reliance on internally generated funds in small unlisted firms. Michaelas and Chittenden (1999) findings indicated that profitability is negatively related to gearing which provides some evidence for the pecking order theory. Support for the pecking order theory is also provided by the negative relationship between the age of the firm and gearing. Brounen et al. (2005), using data from four European countries (UK, Germany, Netherland, and France), claimed that their results are in line with the predictions of the pecking order theory. Bentio (2003), using data from the UK and Spanish markets, pointed out that the results for the UK market are in line with the pecking order theory and against the trade-off model [2]. 2.2. Target Capital Structure Theory

According to the target capital structure theory, the firm sets a target capital structure and gradually moves towards it. The firm changes equity for debt or debt for equity until the value of the firm is maximized (Myers, 1984). The firm’s choice of financing instrument will rely on the difference


between its current and target debt ratios. The overall target will be a function of bankruptcy risk and tax, and that the composition of debt will depend on the company’s size, asset composition, and on uncertainty about future inflation rates (Marsh, 1982).

Brounen et al. (2005) argue that the static trade-off theory is confirmed by the importance of a target debt ratio in general, but also specifically by tax effects and bankruptcy costs. Marsh (1982) contends that when choosing between financing instruments, firms appear to try maintaining long term target debt levels, although they may deviate from these in the short run in response to timing considerations and capital market conditions. Furthermore, Cai and Ghosh (2003) assert that when a firm’s capital structure is different from the optimal capital structure, then the firm tries to “correct” it. Brounen et al. (2005) indicate that the two prevailing determinants of leverage in the static trade-off theory are tax benefits and bankruptcy costs. In the same context, Cai and Gosh (2003) show that when a firm’s gearing is below the firm’s optimal gearing ratio, the firm adjusts its gearing upward, and when a firm’s gearing is above the firm’s optimal gearing ratio, the firm decreases its debt level. Hovakimian et al. (2004) point out that the theory of target leverage implies that high profitability could be associated with high target debt ratios. This is due to the fact that the higher profitability levels suggest higher tax savings from debt, lower possibility of bankruptcy, and potentially higher overinvestment, which all imply a higher target debt ratio.

Several studies using US data have provided evidence consistent of target capital structure theory. Taggart (1977a), and Jalilvand and Harris (1984) found significant adjustment coefficients, which they interpret as evidence that firms optimize debt ratios. Bradely et al. (1984) conclude that their results support the modern balancing trade-off theory of capital structure. Hovakimain et al. (2001) found that firms are likely to move them toward target debt ratios that are consistent with trade-off models of capital structure choice. Shyam-Surnder and Myers (1999) found that when the simple target adjustment model is tested independently, it performs well. However, when the pecking order theory and the target capital models are tested jointly, the coefficients and significance of the pecking order model change hardly at all; the performance of the target-adjustment model degrades, though coefficients still appear statistically significant. Graham and Harvey (2001), using a survey of 392 Chief Financial Officers (CFOs) showed that 71% of the CFOs in their sample responded positively to having a target range for their debt-equity ratio and another 34% indicated that they have “strict” target debt ratio. Fama and French (2002) note that firms’ debt ratios adjust slowly toward their targets. Leary and Roberts (2005) found that the motivations behind corporate financing decisions are consistent with dynamic rebalancing of leverage. They found that firms are more likely to increase (decrease) leverage if their leverage is relatively low (high). Cai and Ghosh (2003) found that when a firm’s debt level is out of the range, it will try to correct it and converge back to the range. Hovakimian et al. (2004), claim that the evidence they develop supports the hypothesis that firms have target capital structures. Kayhan and Titman (2007) found that firms behave as though they have target debt ratios, but their cash flows, investment needs, and stock price realization lead to significant deviations from these targets. Kayhan and Titman (2007) results indicate that the capital structures of firms back towards their targets but at a slow rate. These results are in line with the dynamic capital structure models presented in Fischer et al., (1989), and Titman and Tsyplakov (2005) who show that with reasonable levels of transaction costs along with the traditional costs and benefits of debt financing, debt ratios vary over a relatively large range. Flannery and Rangan (2006) results indicate that firms do target a long run capital structure, and that the typical firm converges toward its long-run target at a rate of more than 30% per year.

In the UK market, Marsh (1982) provides evidence that firms do appear to make their choice of financing instrument as though they had target levels in mind for both the long-term ratio, and the ratio of short-term to total debt [3]. Ozkan (2001) found that firms have a long-run optimal target debt ratio which is assumed to be a function of several firm-specific characteristics which vary over time, over firms, or over both times and firms. Ozkan (2001) found also that an adjustment process takes place, which involves a lag in adjusting to changes in the optimal target debt ratio. Brounen et al. (2005), using data from UK, Germany, Netherlands, and France, found that in the UK, the Netherlands and


Germany over two-thirds of firms aim for some target debt ratio. Furthermore, they found that in each of the countries merely 10% of all firms maintain a strict target capital structure.

3. Method 3.1. Sample and Data

The basic sample of the study comprises all firms listed in the London Stock Market during 1999-2004. However, in line with previous studies, the following criteria were applied to determine the final sample:

Firms that operate in the financial sector (banks, insurance and investment firms) were excluded (e.g., Shyam-Surnder and Myers, 1999; Frank and Goyal, 2003; Rajan and Zingales, 1995; Titman and Weasels, 1988; Lasfer, 1995; Ozkan, 2001) [4].

Firms engaged in merger during the period of the study were excluded. Firms lacking a complete set of financial statements (on the Osiris database) covering the

period of the study were excluded. Applying the above criteria restricted the basic sample to 1935 firms. A total of 138 firms were

randomly selected to form the final sample of the study. The study used the SIC classification for the firms’ industry classification. Eight sectors were represented in the sample. Table 1 shows the distribution of the industries of the sample. Finally, the required financial data for all the firms was collected from the OSIRIS database. Table 1: Industry classification for firms included in the study

Industry Classification SIC Total number of Firms Mining and Quarrying 4 Manufacturing 60 Electricity, Gas and Water Supply 10 Construction 4 Wholesale And Retail Trade 17 Hotels And Restaurants 5 Transport, Storage & Communication 12 Business Activities 26

Total 138 3.2. The Models Employed

3.2.1. The Pecking Order Theory Models The study employs a set of models to investigate the pecking order theory in the UK market. In the first model, the dependent variable measures the change in debt from yeart to yeart+1 as a function of the deficit. In the second model the dependent variable measures the change in equity from yeart to yeart+1 as a function of the deficit. The aim of testing the two models is to investigate the firm’s usage of equity or/and debt to finance their deficit as illustrated in Model (I). According to Frank and Goyal (2003) Model (I) indicates that firms usually issue either debt or equity or both to finance their deficit. The total amount of debt issued and/or equity issued from one year to another year must, by definition be equal to the total deficit at the end of the year.

ititit DEFE I Where: ΔDit: is the change in the total debt from yeart to yeart+1. It equals the change on the long- term

debt + the change of short-term debt. ΔEit: is the net equity issued in yeart. It equals sale of common stock minus stock repurchases

from yeart to yeart+1.


DEF: is the deficit of firmi at yeart [5]. Frank and Goyal (2003) employs the following formula in order to calculate the firm’s deficit.

.ttttttt EDCWIDIVDEF Where: DIVt: is the cash dividend payment in yeart. It: is the net investment in yeart [6]. ΔWt: is the change in working capital in yeart [7]. Ct: is the operating cash flow after interest and taxes [8]. Shyam-Surnder and Myers (1999) argued that in the strict pecking order theory model all

components of the deficit are exogenous as long as safe deficit can be issued. There is no incentive to move down the pecking order and issue stock. Furthermore, the simple pecking order’s predictions do not depend on the sign of the deficit. In principle the firm could become a net lender if funds surpluses persist.

In order to investigate the pecking order theory in UK market, models (II) and (III) are estimated. Shyam-Surnder and Myers (1999), and Frank and Goyal (2003) employed model (II) in the investigation of the pecking order theory. This study extends their works by using model (III). Because the pecking order is driven by asymmetric information, capital structure depends on the net requirement for external finance. Thus, no balance-sheet variables appear in model (II) (Shyam-Surnder and Myers, 1999).

ititit εDEFD 1oαΔ II

ititit εDEFE 1oΔ III In order to address statistical problems such as heteroskedasticity, all variables are scaled by the

total assets of firmi at the end of each year. According to Frank and Goyal (2003) that scaling is most often justified as a method to control the differences in a firm’s size.

It is important to note that the results from testing models (I), (II), and (III) provide important specifications. First, the sum of the deficit coefficients (DEF) in models (II) and (III) must equal one. This is due to the fact that the deficit equals the change in debt (ΔD) plus the change in equity (ΔE). Second, the sum of the intercepts in model (II) and (III) must equal zero. This is a consequence of the first point above.

Shyam-Sunder and Myers (1999) and Frank and Goyal (2003) argued that if the pecking order theory holds, the intercept should equal zero (α0= 0) and the slope of the deficit variable should equal one (α1= 1) in model (II). Shyam-Sunder and Myers (1999) assumed that under the pecking order, each component of financing the deficit should have the predicted dollar-for-dollar impact on corporate debt [9]. The current study extends the previous assumption by hypothesizing that the slope of the deficit in model (III) should not be different from zero (β1= 0). This means that firms do not issue equity to finance their deficit if pecking order theory holds. We also hypothesize that the error term is more important, i.e., larger in model (III) than in model (II). In addition, we hypothesize that β0 in model (III) will be positive; as a consequence α0 in model (II) will be negative.

It is worth noting that the scaling of models (II) and (III) employed by Shyam-Sunder and Myers (1999,) and Frank and Goyal (2003) needs to be modified. This is due to the fact that when we multiply models (II) and (III) by total assets, the intercept in both models becomes the total assets variable. Therefore, we modify models (II) and (III) by scaling all the variables in both models (including the intercept) by the firms’ total assets. A new intercept that equals one for all firms should be added to both models. This is necessary for the ∑ei=0 property (the total residuals equal zero). The modified scaled models are:

ititit εATADEFD oαΔ21

IV

1 2Δ oit it itE DEF ATA ε V

Where:


ATA: is the reciprocal of the firms’ assets [10]. If models (IV) and (V) are multiplied by total assets, then α2 and β2 respectively will be the

intercept for the unscaled model, and α0 and α1 respectively will become the total assets variable. Following Shyam-Sunder and Myers (1999), and Frank and Goyal (2003), all the models

employed in the study are tested using pooled sample data because it has many advantages. For example, it generates more informative data, more variability, less collinearity among variables, more degrees of freedom, and more efficiency. Furthermore, aggregating data of many observations minimises the bias that might result if individuals or firms are broadly aggregated (Gujarati, 2003). 3.2.2. The Target Capital Structure Theory Model (the Partial Adjustment Model) As mentioned previously, the study employs the target adjustment model to investigate the target capital structure. Shyam-Surnder and Myers (1999) employ this method in order to estimate the target capital structure in the US market. They argue that the simple form of the target adjustment model states that changes in the debt ratio are explained by deviations of the current ratio from the target capital. Unfortunately the target capital is unobservable. Therefore, the current study employs a two-stage estimation procedure to estimate the target capital structure ratio. In the first stage, the predicted leverage ratios (the optimal capital structure) are estimated. For this purpose, a static model is employed (Barclay and Smith, 1995; Jordan et al., 1998; Rajan and Zingales, 1995; Bevan and Danbolt, 2002, and 2004; Chung, 1993; Panno, 2003; Titman and Wessels, 1988; Mackie-Mason, 1990; Michaelas et al., 1999; Chittenden et al., 1996; Ghosh and Cai, 1999; Bennet and Donnelly, 1993). The static model (model VI) is used to predict the optimal capital structure for each firm in the sample.

ititititititit AGESIZPROCOLTAXCS 543210 VI Where: CSit: is a measure of firms’ leverage (gearing). It is the firm’s total debt divided by total assets. β0: is the intercept. TAXit: is the measure of the firm’s tax burden. It equals the ratio of firmi’s tax paid during yeart

divided by its profit before interest and tax. COLit: is the measure of the assets structure (tangibility). It equals the ratio of firmi’s total fixed

assets divided by its total assets in yeart. PROit: is the measure of profitability. It equals the ratio of firmi’s earnings before interest and

tax in yeart divided by its total assets. SIZEit: is the measure of the firm’s size. It equals the firmi’s total assets in yeart. AGEit: is the firmi’s age. It equals the natural logarithm of firmi’s age since incorporation. εit: is the error term. In the second stage, the predicted leverage ratio (from the first stage regression) is used as a

proxy for the firm’s target leverage. Following (Shyam-Surnder and Myers, 1999), variable PADJ is constructed; it is the difference between a firm’s predicted leverage ratio in yeart and the actual leverage ratio in yeart-1 (the one year lag of the actual capital structure ratio). PADJ is used as a predictor of whether the firms adjust toward their target leverage or not. PADJ variable is constructed using model (VII).

1*

ititit DDPADJ

VII Where: D*it: is the target leverage ratio in yeart (the predicted leverage from the static model). Dit-1: is the one year lag of the actual leverage ratio. It equals the total debt divided by total

assets for firmi. The inclusion of PADJ represents a partial adjustment model. The coefficients of PADJ are

considered as the target-adjustment coefficients. The hypothesis to be tested is that target adjustment


coefficient > 0, indicating adjustment towards the target leverage, but to be < 1, implying positive adjustment costs. Shyam-Surnder and Myers (1999) employs model (VIII) to investigate the target capital theory. The current study extends their work by employing model (IX) as well:

0 1it it itD PADJ VIII

0 1it it itE PADJ IX

These models are modified by adding the scaled intercept by the total assets (ATA) (see model modification in the pecking order theory)

0 1 2it it itD PADJ ATA X

0 1 2it it itE PADJ ATA XI

4. Data Analysis 4.1. Descriptive Statistics

The descriptive statistics in table 2 indicate that mean value of DEF variable is 0.148. In addition, it shows that the mean value of ΔD variable is higher than ΔE variable. This indicates that firms use more debt than issuing equity to finance their deficits. Moreover, table 2 shows that the mean age of the firms in the sample is 46.2 years, while the mean of the total assets of the firms is ₤ 6.42 billion. Moreover, the average of profitability variable is 11.6%. Table 2: Descriptive statistics for the firms included in the study

Variable Pooled sample

Mean Std. Dev DEF 0.148 2.469 ΔD 0.133 2.492 ΔE 0.0189 0.174 CS 0.286 0.187 TAX 0.181 0.133 COL 0.578 0.228 PROF 0.116 0.224 AGE 46.2 35.85 TA (₤ 000) 6,424,114 1,923,775 Number of firms 138

4.2. The Results of Estimating the Pecking Order Theory Models [12]

The results presented in table 3 strongly support the pecking order theory. The estimated coefficients of the deficit variable in models (II) and (IV) are strongly significant and positive which indicates that firms finance their deficit by using debt. Moreover, the slope of deficit is statistically not different from one. Further more, the intercepts in models (II) and (IV) are negative, close to zero, and insignificant. The explanatory power of models (II) and (IV) are very high, 90%, and 91% respectively. Considering the simplicity of the model, it is obvious that the pecking order model did very well. The deficit variables in models (III) and (V) are insignificant. This; again indicates that firms in UK do not finance their deficit by issuing equity. The pecking order models’ results show that external funding is dominated by debt. These results are in line with those of Shyam-Sunder and Myers (1999) who found evidence that supports the pecking order theory. However, they only estimated a change in debt equation, (model (II)). It is worth noting that there is no great change in coefficients of the deficit and intercept in the modified models (IV) and (V), compared with the (II) and (III). However, the explanatory power of models (IV) and (V) are higher than models (II) and (III). Therefore it can be


concluded that models (IV) and (V) perform better than models (II) and (III). The interpretation of the intercept in models (II) and (III) is that there is a fixed amount affecting the dependent variable: the change in total debt/total assets, and the change in total equity/total assets. If all the variables in models (II) and (III) are unscaled to return to ‘level’ measures, we need to multiply each by total assets. This makes the intercept the total assets variable, which is thus a measure of size. The fact that the intercept is insignificant in models (II) to (V) indicates that the size of the firm does not affect the debt to equity choice. Table 3: The results of estimating models (II-V)

Model (II) Model (III) Model (IV) Model (V)

A - 0.009 0.009 - 0.004 0.004 (0.008) (0.008) (0.007) (0.007)

ATA - - - 193.7*** 193.7*** (16.3) (16.3)

Def 0.89*** 0.11 0.88*** 0.12 (0.07) (0.07) (0.08) (0.08)

R2 0.90 0.13 0.91 0.22 N 512 512 512 512

The dependent variables are ΔD and ΔE. The standard errors are corrected for heteroskedasticity using Breusch-Pagan test. Numbers in bracket are the standard errors. * Statistically significant at 10%. * * Statistically significant at 5%. * ** Statistically significant at 1%.

In order to see whether there is an intercept for the level of debt, the ATA variable is added to models (IV) and (V). This, when multiplied by total assets, becomes an intercept in a “level” model. ATA variable is found to be strongly significant in models (IV) and (V). Furthermore, the coefficient values and standard errors in both models are the same because of the OLS method of estimation. The fact that the estimated coefficient is negative in the debt model (IV) and positive in the equity model (V) provides strong support for the pecking order theory. Taking the changes in equity results first, the strongly positive ATA estimated coefficient means that a proportion of the change in equity is exogenous, that is, it is not determined by the other variables in the model. In the debt equation the negative value indicates the amount of the deficit not financed by debt, and hence not determined by the size of the deficit. This further supports the pecking order theory.

As mentioned earlier, the sample of the study is pooled for the period 1999-2004. However, the study investigates whether using panel data analysis would have been better than pooling the data. The results presented in table 4 indicate that using pooled analysis is more suitable than using panel data analysis. Table 4: The results of Panel data diagnostic test

Dependent Variable Model (II) Model (III)

Lagrange Multiplier test 1.520 1.520

(0.220) (0.220)

Hausman test 23.60 23.60

(0.000) (0.000) Lagrange Multiplier is a test of classical regression (pool) with no group specific variables against panel. The Hausman test is a test to choose between the random and fixed effects models. The numbers in brackets below the coefficient, are the probabilities of significance. According to the results of Lagrange Multiplier test, we cannot reject the null hypothesis that pooled regression is better than panel methods.


4.3. The Results of Estimating the Target Adjustment Model

The results presented in table 5 do not support the target capital structure theory. The target variable (PADJ) in models (VIII-XI) is insignificant, which indicates that firms do not move toward a target debt ratio. In addition, the results show that the explanatory power for all the models is so low. The target adjustment model did not do well. Table 5: Estimating models (VIII-XI)

Model (VIII) Model (IX) Model (X) Model (XI)

A 0.020*** 0.008* 0.020** - 0.0006 (0.009) (0.005) (0.010) (0.008)

ATA - - -301.8 102.2 * (457.20) (384.20)

PADJ 0.020 - 0.020 0.020 - 0.020

(0.090) (0.030) (0.090) (0.030) R2 0.0004 0.001 0.001 0.01 N 317 317 317 317

The dependent variables are ΔD and ΔE. The standard errors are corrected for heteroskedasticity using Breusch-Pagan test. Numbers in bracket are the standard errors. * Statistically significant at 10%. * * Statistically significant at 5%. * ** Statistically significant at 1%. 4.4. Investigating the Pecking Order Theory Against the Target Capital Structure Theory

In this section, both the deficit and the target capital structure partial variables are tested in the same model. The results presented in table 6 show support to the pecking order theory rather than the target capital theory. The deficit variable is statistically significant in all models. This implies that firms use debt and equity to finance their deficits. However, the DEF coefficient is different from one. The intercept is close to zero and insignificant in all models which supports the pecking order theory. The ATA variable in the modified models is statistically significant and has the signs that support the pecking order theory. ATA is negative in ΔD model; while it is positive in ΔE model. The target capital variable is insignificant in all models. This implies that firms do not move toward target leverage. These results are consistent with Shyam-Surnder and Myers results (1999). Table 6: Estimating the pecking order theory and the target capital structure theories

ΔD ΔE ΔD ΔE

A - 0.0009 0.0009 0.003 - 0.003 (0.006) (0.006) (0.007) (0.007)

ATA - - -667.9** 667.9** (307.2) (307.2)

DEF 0.750*** 0.250** 0.760*** 0.240** (0.100) (0.100) (0.100) (0.100)

PADJ 0.020 - 0.020 0.020 - 0.020

(0.020) (0.020) (0.020) (0.020) R2 0.78 0.27 0.78 0.35 N 317 317 317 317

The dependent variables are ΔD & ΔE. The standard errors are corrected for heteroskedasticity using Breusch-Pagan test. Numbers in bracket are the standard errors. * Statistically significant at 10%. * * Statistically significant at 5%. * ** Statistically significant at 1%.


5. Conclusion This study, empirically, investigated two of the most influential theories of capital structure: the pecking order theory and the target capital structure in the UK market during the period 1999-2004. The study employed and extended the approaches used by Shyam-Sunder and Myers (1999), and Frank and Goyal (2003). In addition, the study used the partial adjustment model to investigate the target capital structure theory. The results showed support for the pecking order theory against the target capital structure, in which firms prefer to finance their deficits by using debt, rather than moving toward a target capital structure. These results are in line with Shyam-Sunder and Myers (1999). Notes:

1. Frank and Goyal (2003) found that the information in the financing deficit appears to be a factor in along with many other factors that firms take into account in capital structure decisions.

2. Bentio (2003) contended that higher cash flow implies lower levels of debt. Furthermore, according to the pecking order model a higher level of investment increases the need for debt finance. Moreover, he found that profitability is negatively related to debt.

3. Marsh (1982) found that the probabilities of debt and equity issues vary with the deviation of the current debt ratio from the target. Also he found that the probability that firm issues equity is significantly higher if the firm is above its target debt ratio, and significantly lower if below the target.

4. Lasfer (1995) argued that financial firms are excluded because of the specific characteristics of their capital structure and their special tax treatment. Rajan and Zingales (1995) exclude the financial firms because financial firms’ leverage is highly affected by explicit (or implicit) investor insurance schemes such as the deposit insurance.

5. Some firms included in the sample have a surplus rather than a deficit. That means that the firms become lenders rather than borrowers. According to the pecking order theory, such firms are likely to pay back debt first and then they re-buy their stocks from the market, i.e., same order as for financing a deficit (Myers, 1984).

6. I = (capital expenditures + increase in investments + acquisitions + other use of funds – sale of plant property equipment – sale of investment).

7. ΔW = change in operating working capital. (Change in operating working capital+ change in cash and cash equivalent).

8. C= (income before extraordinary items + depreciation and amortization + extra ordinary items and discontinued operations + deferred taxes + equity in net loss – earnings + other funds from operations + gain (loss) from sales of property plant equipment).

9. Shyam-Sunder and Myers (1999) claimed that the simple pecking order’s predictions do not depend on the sign of DEFt. In principle the firm could become a net lender if surplus funds persist.

10. The definitions of the variables ΔD, ΔE, DEF, α0, β0, and εit are the same as in models (II and III). 11. The study tests whether there is an effect of the firms’ size (the total assets), we estimate the unscaled models (II)

and (III) and we add the firms’ total size as another explanatory variable. The results show that the total assets variable is insignificant in both models. This indicates that the firms’ size has no effect in financing behavior (no distortion effect), and that the results are not driven by the firms’ size.

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