1
Ownership Concentration and Capital Structure
Adjustments
Salma Kasbi1
26 Septembre 2009
Abstract
We investigate the capital structure dynamics of a panel of 766 firms from five Western Europe countries: France, Italy, UK, Germany and Switzerland over the period 1996-2007. If firms adjust their capital structure towards an optimal level with adjustment costs affecting this optimization behavior, then firms with a concentrated ownership structure should adjust their leverage at a slower rate. Indeed, large blockholders are likely to avoid control-diluting equity issuances. They may also face higher transaction costs arising from the extent of adverse selection problems they face or because of the lower liquidity of their stocks. Besides, one can expect them to avoid large upward leverage adjustments in order not to increase the risk of their undiversified portfolio. However, if firms are indifferent towards their capital structure, past timed securities issuances should have a persistent impact on their current leverage regardless of their ownership structure. Using system-GMM methodology on panel data, we find that European firms have a target leverage to which they revert at different rates. The speed of adjustment is the lowest for firms with a single large blockholder. These results extend previous literature by introducing the transaction costs and the agency costs inherent to ownership concentration as significant determinants of capital structure dynamics.
Keywords Capital Structure, Costly Adjustments, Market Timing, Ownership Concentration.
JEL Classification: G32
1
CEREG-DRM Paris-Dauphine University. Place du Maréchal de Lattre de Tassigny, 75775 Paris Cedex 16, France. E-mail : salma.kasbi@dauphine.fr. Tel : 06 62 37 02 06
2 I. Introduction
Despite extensive coverage by researchers, firms’ financing decisions remain a puzzle. Since the Modigliani and Miller (1985) proposition, three major theories have emerged: the Tradeoff theory, the Pecking Order Theory and the Market Timing Theory. Under the tradeoff theory, firms have a target capital structure resulting from balancing the costs and benefits associated with debt financing. On the contrary, the Pecking Order and the Market Timing theories state that firms are indifferent towards their capital structure. According to the Pecking Order theory, adverse selection leads managers to fund their financing needs with internal funds then with debt and choose equity as a last resort. Under the market timing theory, managers take advantage of “windows of opportunities” to successfully time their security offerings without consequently undoing the resulting impact on their leverage.
Recent empirical papers generally agree on the mean reverting behavior of leverage. However, the speed at which this occurs varies among the different papers and is used as a mean to disentangle between the competing theories. While Baker and Wurgler (2002) find that the impact of timed security issuances on firms’ capital structure lasts for at least ten years, Leary and Roberts (2005) emphasize the importance of transaction costs in preventing firms from actively rebalancing their capital structure.
In this paper we investigate whether agency costs and adverse selection costs embedded in firms’ ownership structures induce uneven patterns of leverage changes. If the persistent impact of past timed financing activities is due to firms’ indifference towards their capital structure then ownership concentration should be irrelevant. However, if transaction costs and agency costs shape the dynamics of leverage then the speed of adjustment towards target leverage should be negatively related to the extent of ownership concentration. Indeed, one can expect the costs of moving towards the target to be higher for firms with large shareholders for several reasons. First, transaction costs may be higher for these firms because of the adverse selection problems they face and the lower liquidity of their stocks (e.g. Edmans and Manson (2007), Dyck and Zingales (2004), Pagano and Röell (1998)). Second, as large shareholders are concerned about maintaining their control over the firm, they are more likely to avoid large equity issues.
3 Third, large blockholders might incur higher costs of levering up, as this will result in an increase of the risk of their particularly undiversified portfolio (e.g. Anderson and Reeb (2003), Ødegaard (2009).
The empirical evidence regarding capital structure dynamics is rather mixed. Baker and Wurgler (2002) findings suggest that firms are indifferent towards their capital structure and that current leverage is the outcome of historical market timing activities. More recently, Huang and Ritter (2008) add support to this view by showing that to meet their financing needs, firms choose their financing mix in accordance with the equity market risk premium level. When the equity risk premium is high, firms issue debt to fund their financing deficit and issue equity at lower levels of the equity risk premium. Leary and Roberts (2005) point instead to clustered adjustments occurring on average once a year and find that firms offset the impact of equity shocks within two years. They conclude that the adjustment costs prevent firms from continuously rebalancing their capital structure. Using a partial adjustment estimation procedure, Flannery and Rangan (2006) confirm the presence of a target leverage and the costly rebalancing hypothesis. They estimate an annual reverting rate of approximately 35%. Several papers analyze the potential effect of firm-specific variables or macroeconomic conditions on the speed at which firms revert their leverage towards its optimal level or range. Yet none of them considered firms’ ownership structure as a factor likely to affect the adjustment decision. For instance, Faulkender et al. (2008) document that firms with lower expected incremental adjustment costs (low or high cash flow level) have a higher speed of adjustment than firms with higher expected adjustment costs (close to zero cash flow level). Drobetz et al (2006) investigate the adjustment speed of European firms’ capital structure. They find that larger firms, firms with higher levels of growth opportunities and firms that are further away from their target leverage adjust faster. Byoun (2008) documents that firms have different adjustment rates with regard to their financial constraints and to the degree of adverse selection they are facing, measured by their financial deficit/surplus. Cook and Tang (2008) show that firms rebalance their capital structure faster in good macroeconomic states than in bad states. Korajczyk and Levy (2003) suggest that firms’ financing decisions reflect macroeconomic conditions and financial constraints.
4 The results we document in this paper are consistent with the predictions of the costly adjustment hypothesis. Specifically, we find that past timed financing activities have a higher impact on the current leverage of firms with high levels of ownership concentration. In light of these results, we estimate the speed of adjustment towards target leverage using a system-GMM estimation procedure. The results confirm the costly adjustment hypothesis and reveal that firms with a single reference shareholder adjust at a slower rate. The annual adjustment rate is 13% for firms with a majority shareholder vs. 32% for firms without such a large shareholder. Besides, among the group of firms where the first reference shareholder detains at least one third of the cash flow rights, the annual rate of adjustment of market leverage raises by approximately 19% when a second reference blockholder exists. We finally relax the assumption of constant partial adjustments and investigate whether adjustments depend on the sign and the magnitude of the deviation of actual leverage from its target level. This specification also allows us to investigate whether the slower adjustments documented among concentrated ownership firms occur when they are overlevered or when they are underlevered. Our results show an asymmetric adjustment pattern. Overlevered firms adjust faster than underleverd firms. These results can be attributed to the latter’s preference to preserve their debt capacity and to the former’s threat of financial distress. We also find that ownership concentration induces slower adjustments, whether they imply increasing leverage or decreasing it.
The remainder of the paper is as follows. In section II we explain why we expect capital structure adjustment costs to be higher among concentrated ownership firms. In section III we describe the sample and the variables. In section IV we present the empirical framework and the results and in section V we conclude.
5 II. Ownership Concentration and capital structure adjustment costs
While the market timing theory states that firms are indifferent towards their capital structure, the dynamic tradeoff theory attributes the persistence of leverage to the existence of transaction costs preventing firms from actively rebalancing their leverage towards the target. In a dynamic tradeoff framework, firms with a concentrated ownership structure, being likely to incur higher costs of adjustments, should revert their leverage at a lower speed than more widely held firms. Indeed, a large reference blockholder is likely to strive to preserve his control over the firm. Hence he will have an incentive to prevent the managers from undertaking control-diluting actions such as large equity issuances designed to offset the deviation of actual leverage from its target.
Besides, a large range of theoretical and empirical evidence suggests that ownership structure has a direct impact on firms’ cost of external finance. The literature generally agrees on the negative impact of ownership concentration on price informativeness. Several papers recognize that the level of private information is higher in firms with a concentrated ownership structure. For instance, Holmström and Tirole (1993) argue that ownership concentration impedes the informational role of the stock market by reducing the stock liquidity. Sarin et al. (2000) document that the level of insider ownership is positively correlated with spreads width and negatively correlated with market depth. More recently, Ginglinger and Hamon (2007) and Becker et al. (2008) provide similar results in the presence of large blockholders, illustrating a higher degree of adverse selection due to these informed traders. Nevertheless, the presence of multiple blockholders beyond the largest one may enhance information production. According to Edmans and Manson (2007), the competitive trading behavior of multiple small blockholders improves price informativeness. Easley and Ohara (2004), among others, investigate the link between price informativeness and the cost of capital. They report that uninformed investors, holding stocks with greater private information, demand a higher return to balance their inability to adjust accurately their portfolio composition. Moreover, in firms with a major blockholder, the risk of expropriation encountered by minority shareholders makes them require a premium for holding the firm’s stock. Shleifer and Vishny (1997) argue that most countries should be concerned
6 by the issue of the expropriation of minority shareholders by the controlling ones. According to Dyck and Zingales (2004) or Laporta et al. (1997, 2002), the potential for private benefits extraction by the controlling shareholders increases the cost of finance. In this context, several papers report that other large blockholders reduce the potential for wealth diversion, either by monitoring or by forming a coalition with higher equity stakes that internalizes the diversion costs (Maury and Pajuste (2005). Pagano and Röell (1998) and Laporta et al. (1999) argue that the other blockholders have the ability to monitor the controlling one. Bloch and Hege (2001) provide further evidence on the reduction of private benefits extraction in the presence of multiple shareholders and argue that wealth diversion increases when the wedge between the cash flow rights of the largest shareholders increases. Laeven and Levine (2008) report similar results: the wedge between the equity stakes of the two leading shareholders negatively affects firm value. Accordingly, firms with a single large blockholder or firms with a majority blockholder are more likely to incur higher costs of external finance than firms with no majority blockholder or firms with multiple blockholders. Indeed, a recent paper by Attig et al. (2008) provides direct empirical evidence on this relationship. They report that firms with multiple large shareholders beyond the ultimate owner incur lower costs of external finance compared to firms with a single controlling shareholder. In light of these results, we expect concentrated ownership firms to incur higher costs of adjustments when adjusting towards the target implies issuing equity.
In addition, these firms may also be more reluctant to undertake leverage-increasing adjustments in order to preserve their debt capacity to avoid issuing equity for future financing needs. Increasing the firm’s debt level also implies an increase in the firm’s risk. Large shareholders have generally particularly undiversified portfolio making them more likely to avoid increasing their firm’s leverage. According to Anderson and Reeb (2003), US family-firms invest 69% of their personal wealth in their company, Ødegaard (2009) reports that the median largest owner in Norway has 88% (nearly 100% for an individual) of his wealth invested in the same firm. Therefore, we assume that leverage-increasing adjustments are more costly for concentrated ownership firms.
7 By controlling for ownership structure, we are able to disentangle between the predictions of the market timing theory and the tradeoff theory. In particular, we analyze whether the previously documented persistent impact of timing activities (e.g. Baker and Wurgler (2002)) is the result of firms’ indifference towards their capital structures, as predicted by the market timing theory, or whether it is the consequence of adjustment costs that prevent firms from actively rebalancing their leverage. To do so, we analyze firms’ leverage dynamics according to the extent of adverse selection they face. While according to the market timing theory, ownership structure should not have any implications on leverage dynamics, the costly adjustments hypothesis predicts that concentrated ownership firms will display a slower rebalancing speed.
III. Sample and Variables
The initial sample comprises firms from five European countries: France, Germany, Italy, Switzerland and the United Kingdom, that initiated their IPO between 1996 and 2005 for French firms, 1996 and 2004 for UK firms and between 1997 and 2005 for firms from the 3 remaining countries2. We track firms’ financing decisions since the IPO in order to have a complete picture of their historical financing activities. Besides, having the IPO dates allows us to exclude erroneous observations with pre-IPO market figures. While US IPO dates are generally extracted from the SDC database, information regarding European IPOs is highly unreliable in this database. Therefore, we turned to the stock markets’ websites to download the IPO information and contacted them when the historical data were not available. This procedure explains the selection of those five countries. We then matched the IPO firms’ sample with non-financial firms present in the Thomson Financial database (that provides non-financial statements data) between 1997 and 2007, resulting in a total number of 10,894 firm year observations from 1341 firms among which 331 French firms, 122 Italian firms, 507 UK firms, 334 German firms and 47 Swiss firms. We further restrict the sample to
2 The unavailability of IPO data resulted in an unbalanced sample. We obtain similar results if we run our
8 observations with available data on the ownership composition, extracted from the Thomson One Banker Ownership database. Firm-year observations with a total sum of percentage ownership exceeding 100% are corrected using annual reports when available or replaced by the preceding year ownership. To avoid outliers, we further cut-off the total percentage ownership variable at the 5% and 95% percentile thresholds. Firms a minimum value of assets below 5 million Euros or without complete data on total assets between the IPO year and the year the firm exits Thomson are dropped. Table 1 presents a summary of the variables we use. Following prior literature (e.g., Baker and Wurgler (2002)), Book equity (E) is defined as total asset minus total liabilities and preferred stock plus deferred taxes and convertible debt. If preferred stock is missing, it is replaced with the redemption value of preferred stock (or zero if the redemption value of preferred stock is also missing). If the data on deferred taxes or convertible debt is missing it is replaced with zero. Book debt (D) is defined as total assets minus book equity. Market equity is defined as the share price times common shares outstanding. The market-to-book ratio (MB) is defined as the market value of assets divided by total assets. The Market value of assets is total assets minus book equity plus market equity. Net equity issuance (e/A) is the change in book equity minus the change in balance sheet retained earnings divided by assets. Newly retained earnings (∆RE/A) is defined as the change in retained earnings divided by assets. Net debt issuance (d/A) is defined as the residual change in assets divided by assets. Profitability is defined as earnings before interest, taxes and depreciation divided by assets. Firm size (SIZE) is the logarithm of net sales. Tangibility (PPE) is defined as net plant, property and equipment divided by assets. Research and development expenses (RD) are divided by assets and replaced by zero when missing. RDD is a dummy variable that takes the value of one when RD/A is missing and zero otherwise. We measure the amount of Non-debt tax shields (DEPR) by the ratio of depreciations to total assets. Observations are dropped if MB is above 10. All the scaled variables, defined above, are by fiscal year end total assets and outliers are excluded. The final sample is comprised of 766 firms (France: 236, Germany: 231, Italy: 108, UK: 167 and Switzerland: 24) and 3290 firm-year observations. We measure leverage using both its book and market value, as the literature is unclear on which definition is the most relevant. Book leverage
9 (Bleverage) is defined as book debt divided by total assets. Market leverage (Mleverage) is defined as book debt divided by the market value of total assets. Observations with book leverage or market leverage exceeding 1 are excluded from the book leverage regressions or the market leverage regressions, respectively.
We further use the following variables to account for the level of ownership concentration. The percentage of cash flow rights is provided by the Thomson One banker Ownership database. %First is the percentage of cash flow rights held by the largest shareholder. %First is set to zero if the largest shareholders holds less that 5% of the outstanding shares. In all our regressions, %First is expressed in percentage terms. First50 is a dummy variable set to one for observations with a majority shareholder, namely a shareholder with more than 50% of the firm’s cash flow rights. Even if the database we use does not provide information about the voting rights, to some extent, First50 also accounts for the presence of a leading controlling blockholder, as shareholders with more than 50% of the cash flow rights rarely hold lower levels of voting rights. First33, is set to one when the leading shareholder holds at least one third of the outstanding shares and zero otherwise. One third is the highest mandatory bid threshold in force among the countries of our sample3. Finally, we account for the presence of a second large shareholder using a dummy variable: Second10. This variable is equal to one for observations with a second shareholder, holding more than 10% of the cash flow rights, beyond the first reference blockholder (First33).
Table 2 presents summary statistics on leverage, size, assets tangibility, profitability, market to book ratio, R&D expenses and non-debt tax shields for the full sample and for the four subsamples of observations grouped according to the level of ownership concentration. On average, firms with a majority shareholder have significantly higher debt levels than firms without a majority shareholder. This could be the result of higher adverse selection costs borne by these firms, driving a wedge between the cost of equity and the cost of debt and because they are more concerned about control-dilution. In addition, they are larger, with more tangible assets, lower
3To ensure the robustness of our results, we run our tests using the mandatory bid threshold of each
country: 33% for France and Switzerland, and 30% for UK, Germany and Italy. Our results are qualitatively unchanged.
10 market to book ratios and less R&D expenses, consistent with these characteristics facilitating the access to debt financing. Regarding the subsample of firms with large blockholders, the coefficients show similarities amongst firms with a single blockholder and firms with at least two reference blockholders. Leverage is virtually the same within the two groups and although the other variables display some disparities in their means, the medians are insignificantly different.
Table 3 reports summary statistics of the previously defined ownership concentration measures across the five countries of our sample. On average, first blockholders hold 36% of the cash flow rights. This coefficient significantly drops among the Swiss and UK firms, consistent with these countries having lower ownership concentration levels. Italian and French firms have the highest average percentage of cash flow rights held by the first shareholder, 46% and 41%, respectively and are followed by German firms (34%). On average, the mean level of cash flow rights held by the second reference shareholder is similar across the five countries, with Italy and Switzerland exhibiting the lowest coefficients. Note that this variable is set to zero when there is no second blockholder (at the 5% ownership level). 31% of our sample firm-year observations have a majority shareholder in their ownership structure. Italy displays the highest number of firms held by a majority shareholder, 54%, followed by France, 38%, Germany, 26%, Switzerland, 16%, and only 9% for UK. Among the subsample of firms with at least one large blockholder at the 1/3 level, 31% have a second reference blockholder. Except for Italy and Switzerland, where the mean approximates 20%, the other countries display an average of 31% to 39% of firm-year observations with at least a second reference blockholder.
IV. Empirical framework and results
IV.1. Ownership concentration and the short-term impact of market timing on capital structure
In this section, we analyze whether firms change their leverage in response to high market valuations. More specifically, we estimate the annual changes in leverage
11 induced by an increase in the MB ratio among firms with different ownership concentration levels. We expect a negative correlation between MB and the annual change in leverage.
However, this relation is unclear for concentrated ownership firms. On the one hand they might be more reluctant to issue equity because of the various costs they incur. On the other hand, periods of high valuations are windows of opportunities with a particularly favorable cost of equity. Therefore, more equity issues can be expected during these periods.
We estimate the short-term impact of market timing on the annual change in leverage using the following equations.
D/A
it D/A
it101(M/B)it12(M/B)it1*%Firstit13Xit1it ( 1 )
D/A
it D/A
it101(M/B)it12(M/B)it1*First50it13Xit1it ( 2 )
it it it it it it it it it X Second B M First B M B M A D A D 1 4 1 1 3 1 1 2 1 1 0 1 10 * ) / ( 33 * ) / ( ) / ( / / ( 3 )In equation (1), the marginal impact of market timing on the annual change in leverage related to the percentage of shares held by the first shareholder is β1+
β2*%First.
In equation (2), the marginal impact of market timing is measured by the coefficient β1+ β2 in firms with a majority blockholder and by β1 in firms without a
majority blockholder. If firms decrease their leverage in response to high valuations, then β1<0 and β1+ β2<0. If this decrease in leverage, is lower among firms with a
majority shareholder then β2 will be positive and statistically significant, so that β1+ β2>
β1.
In equation (3) we measure the marginal impact of market timing when there is a single reference shareholder at the one-third level (β1+ β2) or when there is a second
12 impact is β1 for firm-year observations where no shareholder holds more than one third
of the outstanding shares.
In each equation, X is a vector of control variables: size, tangibility of assets, profitability, non-debt tax shields, research and development expenses (set to zero when missing) a dummy variable set to 1 when research and development expenses are missing and zero otherwise. Lagged leverage is included to control for mechanical backward moves of leverage when it reaches one of its boundaries. We further control for year, industry and country fixed effects. We also include the main effects of ownership concentration (%First (equation (1)), First50 (equation (2)), and First33 and
Second10 (in equation (2)) to ensure that the interaction terms measure exclusively the
effect related to high valuations.
Petersen (2005) suggests that in the presence of a firm effect, the Rogers (1993) standard errors clustered by firm are more accurate that the OLS standard errors. Therefore, we adjust the t-statistics for clustering at the firm level.
Table 4 reports the coefficients from the estimations of equations (1), (2) and (3), in the first columns of Panels A, B, C respectively. The estimates indicate that the net effect of the market to book ratio is to decrease leverage. The coefficients of the interaction terms are not statistically significant, suggesting that ownership concentration does not significantly affect the magnitude of leverage changes induced by high valuations.
To investigate whether the decrease in leverage is due to equity issues as market timing implies, we decompose the change in leverage using the following accounting identity:
D/A
t D/A
t1
e/At RE/A
t Et1(1/At 1/At1)( 4 )
The first term in Equation (4) is the negative of net equity issues. If firms time the equity market then the negative relation observed between the market to book and leverage should come through equity issues. The second term is the negative of newly retained earnings. It allows us to test whether the decline in leverage is due to higher earnings retention following a period of high valuations. The last term is the residual
13 change in leverage, which depends on the total growth in assets from the combination of equity issues, debt issues, and newly retained earnings. We estimate Equations (1), (2) and (3) using the components of the change in leverage as dependant variables. The results are reported in the three last columns of each panel of Table 4. Consistent with the predictions of the market timing theory, the net effect of market to book on the annual change in leverage comes through higher equity issues. The coefficients of the interaction terms between the first shareholder and the MB ratio are positive and statistically significant, at the 10% level, in two out of three specifications. For instance, Panel A shows that if the first shareholder holds 20% of the firm’s outstanding shares, a 1% increase in the MB ratio results in a 2.756% increase in equity issues4. Panel B shows that firms with a majority shareholder increase their equity issuances at a lower rate than firms without a majority shareholder. The difference between the two groups is 1.253%. The coefficient of the interaction terms with First33 and Second10 are not statistically significant. However this coefficient is positive for First33, which is again in accordance with the hypothesis that firms with a more concentrated ownership structure are reluctant to issue equity. The interaction term with Second10 is, on the contrary, negative, suggesting that the presence of a second large blockholder facilitates equity issues. The remaining estimates suggest that larger firms issue less equity and are more likely to increase their leverage. Unexpectedly, asset tangibility has a negative, although insignificant, impact on the annual change in leverage, driven by equity issues. Profitability enters with a negative sign. This decline in leverage experienced by profitable firms is due to newly retained earnings.
IV.2. Ownership concentration and the persistent impact of market timing on capital structure
The evidence documented so far suggests that firms issue significantly larger amounts of equity following high valuations and that their level of ownership concentration does not significantly impede their timing ability. Whether this impact is
4 (β
14 persistent or whether firms subsequently revert their capital structure, is the main question of the debate.
Baker and Wurgler (2002) show that the impact of market timing lasts about ten years. They introduced the external finance weighted-average market-to-book ratio as a proxy for market timing. They define it as follows:
is t s t r ir ir is is it efwa MB d e d e B M
0 0 , ( 5 )0 is the IPO date. e and d denote the amount of net equity and net debt issued, respectively. Following Baker and Wurgler (2002), negative amounts of external finance are reset to zero.
The MBefwa is the weighted average of past market-to-book ratios. The weight for
each year is the yearly amount of external finance raised by the firm divided by the total amount of external finance raised since the IPO year. Subsequently, firms that issue securities when their valuations are high have higher values of MBefwa. A negative
relation between MBefwa and current leverage would be consistent with firms issuing
equity when their market valuations are high and debt otherwise, without subsequently reverting their leverage, resulting in a long lasting impact of past timed financing activities.
We analyze whether the adjustment costs related to ownership concentration result in a higher persistence of historical timed securities issuances. To do so, we interact our measures of ownership concentration with the MBefwa in the following
regressions of the determinants of current leverage:
D/A
t
0
1(M/B)efwa,t1
2(M/B)efwa,t1*%Firstt1
3Xt1
t ( 6 )
D/A
t
0
1(M/B)efwa,t1
2(M/B)efwa,t1*First50t1
3Xt1
t15
t t t t efwa t t efwa t efwa t X Second B M First B M B M A D
1 4 1 1 , 3 1 1 , 2 1 , 1 0 10 * ) / ( 33 * ) / ( ) / ( / ( 8 )In the equations above, the dependent variable is leverage measured either by its book value or by its market value.
In Equation (6), the marginal impact of MBefwa on current leverage related to the
percentage of shares held by the first shareholder is β1+ β2*%First.
In Equation (7), the marginal impact of MBefwa on current leverage is measured by
the coefficient β2+ β1 in firms with a majority shareholder, whereas the marginal impact
on firms without a majority blockholder is β1.
In Equation (8), the marginal impact of historical financing activities of firms with a single reference blockholder is β1+ β2. In firms with a second reference shareholder,
the marginal impact is β1+ β2 + β3 and in firms where there is no reference blockholder
at the one-third threshold, the marginal impact β1.
X is a vector of control variables: size, tangibility of assets, profitability, market to
book, non-debt tax shields, research and development expenses (set to zero when missing) a dummy variable set to 1 when research and development expenses is missing and zero otherwise. We also include the main impact of ownership concentration and year and industry dummies. T-statistics are based on robust estimation of standard errors with errors cluster-adjusted at the firm level.
The estimates of Equation (6), Equation (7) and Equation (8) are reported in Table 5 Panel A, B and C, respectively.In each Panel, the dependant variable is book leverage in the first column and market leverage in the second column. The estimates reveal a negative and statistically significant impact of the historical market to book ratio on current leverage only among firms with a high ownership concentration level. The long-run impact of market timing is more important when market leverage is the dependant variable. Panel A shows that if the first shareholder holds only 10% of a firm’s equity, the impact of MBefwa on current leverage is -0.086 while at the 25%
16 ownership level the impact is -0.8365. Panel B shows that when firms do not have a majority shareholder, the effect of MBefwa is small (-0.571) and not statistically
significant. This coefficient is significantly lower (in absolute values) than in firms with a majority shareholder. The difference between the two groups is -3.047 and is significant at the 1% level.
Overall, our results indicate that concentrated ownership firms are able to engage in market timing activities but do not subsequently rebalance their leverage as quickly as more widely held firms do. Although the negative impact of MBefwa on current
leverage is consistent with the predictions of the market timing theory, the fact that it is significantly stronger when the level of ownership concentration is high is in opposition with the predictions of this theory. Indeed, if firms were indifferent towards their capital structure, the impact of market timing would persist irrespective of the level of ownership concentration. Instead, the costly adjustments hypothesis provides a better explanation of the differential leverage patterns we document. If firms have an optimal level of leverage but adjustment costs prevent them from continuously rebalancing their capital structure to offset the impact of various shocks, then firms with a higher level of ownership concentration will avoid such costly rebalancing as the associated transaction costs are likely to outweigh the costs of being off-target.
To gain further insight on the role of ownership concentration on leverage dynamics, we analyze the patterns followed by firms with multiple blockholders in Panel C. Consistent with our hypothesis and with our previous results, the coefficients show that the historical market to book variable displays a strong and negative effect (β2) when interacted with First3. Consistent with the costly adjustments hypothesis, in
firms with a second reference shareholder, the interaction effect of the historical market to book variable is positive and significant suggesting that the presence of such a blockholder mitigates the persistence of past financing activities on current leverage.
To check the robustness of our results, we reestimate Equation (6), Equation (7) and equation (8) after including country dummies as control variables. This allows us to control for countries specific impact on leverage such as the difference in legislation in
17 force. The results are reported in Table 6. The magnitude of the MBefwa impact is
generally reduced. Although the impact is still negative, we observe a decline in the statistical significance of the interaction terms in two out of three book leverage regressions (Equation (6), Equation (8)). However, the conclusions hold for the three market leverage regressions.
IV.3. Ownership concentration and the speed of adjustment towards target leverage
In this section, we extend our analysis by evaluating the annual rate at which firms, in the different ownership concentration categories, revert their leverage in response to shocks that affect their capital structure.
Under the tradeoff 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 recent papers (e.g. Flannery and Rangan (2006)) we use the following partial adjustment estimation procedure to model the dynamics of leverage:
it it
it t i t iY
Y
Y
Y
,
,1
*,
,1
, ( 9 )where, Yi,t is the leverage ratio of firm i at time t, Y is target leverage, i*,t i,t is the time varying error term and is the annual speed of adjustment of towards the target. A positive and below one would suggest that firms are reverting their leverage towards its optimal level overtime while above one reveals the absence of a target ratio. The target leverage is modeled as a function of a time varying vector of firm characteristics,
t i
X, , and firm fixed effects, i:
i t i t i
X
Y
*,
,1
( 10 )18 Substituting Equation (10) in Equation (9) yields to:
it it i it ti
Y
X
Y
,
1
,1
,1
,( 11 )
Firm characteristics are size, profitability, market to book, tangibility of assets, depreciation, R&D expenses and a dummy variable set to one when R&D is missing. Year dummies are also included6. Estimating equation (11) using OLS would result in biased coefficients since the firm fixed effects in the error term are unobservable and correlated with the lagged dependent variable used as a regressor Yi,t1, thereby violating one of the assumptions of OLS (Hsiao, 1985).
A first differentiation of equation (11) eliminates the fixed effects but the OLS estimators will still be inefficient because of the correlation between the lagged dependent variable in the Yi,t1 term and the lagged error in the i,t term. Bond (2002) notes that an accurate estimation of the true parameter of Yi,t1 should lie between the OLS coefficient and the fixed effect coefficient or near one of these two boundaries because the former is upward biased while the latter is downward biased.
One way to deal with this endogeneity problem is to use the Anderson and Hsiao (1981) 2SLS estimators by instrumenting Yi,t1 with Yi,t2orYi,t2. As long as the errors are not serially correlated, 2SLS provide consistent estimates of the coefficients but they are inefficient.
To overcome this inefficiency issue, Arellano and Bond (1991) propose the difference GMM technique that allows exploiting all available moment conditions by using deeper lags of the dependent variable as additional instruments. However, if the dependent variable is highly persistent, which is the case of leverage, Blundell and Bond (1998) recommend using a more relevant procedure: the system-GMM procedure. It consists in estimating a system of two equations, the equation in difference where the
6 In unreported results, to ensure the robustness of our tests we performed the same set of regressions by
19 differentiated endogenous variables are instrumented with the appropriate lags of the variables in levels and the original equation in levels where the endogeneous variables in level are instrumented with appropriate lags of the differentiated variables.
Autocorrelation tests are crucial for the instruments choice in this procedure. We expect the residuals in differences to be first order serially correlated (AR(1)) however evidence on second order serial correlation (AR(2)) would invalidate the corresponding variables are exogenous instruments. Deeper lags will then be used. To assess the validity of the instruments, we report the values of the Hansen J statistic, which is robust to heteroskedasticity, providing the instrument count is low. Therefore, we collapse the instruments and restrict their number (Roodman (2006)).
Table 7 reports the results for the full sample. The annual rate of adjustment towards target leverage is 26% (1-the estimated coefficient for lagged leverage) using book leverage. The rate increases to 40% when market leverage is used as dependant variable. The reported speeds are similar to the rates documented in Flannery and Rangan (2006), at least for book leverage. Yet European firms seem to adjust relatively faster than US firms do. While Lemmon, Roberts and Zender (2008) estimate, for book leverage, a comparable speed of adjustment of 25% per year, Huang and Ritter (2008) estimate an annual speed of 17% for book leverage and 23% for market leverage. Using a sample incorporating European firms, Antoniou et al. (2008) document a speed of 33% over the period 1994-2000.
Surprisingly, profitability enters with a positive, but insignificant, sign in the leverage regressions. Size and market to book display the expected signs, positive and negative, respectively. Non-debt tax shields also display the negative expected effect, consistent with tax deduction for depreciation being a substitute to the interest payment tax advantage (DeAngelo and Masulis (1980)). R&D expenses, a proxy for growth prospects, negatively affects leverage, as expected.
Table 8 reports the estimated annual rates of adjustment of book leverage (Panel A) and market leverage (Panel B) for the subsample of firms with a majority shareholder in the first column and the subsample of firms without a majority shareholder in the second column. In line with the evidence documented in the previous section, the results show that firms with a majority shareholder, incurring more costs of
20 external finance, adjust their leverage at a lower rate of 13% vs. 32% for firms without a majority shareholder. Similar estimations are obtained when using market leverage as dependant variable: the adjustment speed is of 26% and 46% for firms with a majority blockholder and firms without such a blockholder, respectively.
Table 9 displays the estimated adjustment speeds for firms with a single large blockholder and firms with at least a second reference blockholder. Our results remain consistent with our hypotheses. In the presence of a second leading blockholder, the rate at which firms respond to shocks is higher: 21% vs. 18% for book leverage and 49% vs. 35% for market leverage.
Overall, our results are in favor of the dynamic tradeoff theory with adjustment costs preventing firms from continuously reverting their leverage, hence resulting in longer periods of deviations from the target for firms that are more likely to incur higher transaction costs due to their ownership concentration.
IV.4. Asymmetric Speed of Adjustment
To allow for a non-constant annual adjustment speed, we specify an asymmetric partial adjustment model, in which firms with above target leverage and firms with below target leverage may correct their deviations at different rates. The findings of Hovakimian et al. (2001) and Byoun (2008) for instance, reveal that the adjustment speed is higher among overlevered firms than among underleverd firms. Indeed, firms with above target leverage have a higher incentive to rebalance their capital structure because of the costs of financial distress.
However, decreasing leverage is likely to involve equity issuances. We therefore expect ownership concentration costs to delay these adjustments. Increasing leverage for underlevered firms implies that reducing debt capacity is less costly than staying below the target. Firms facing higher costs of equity issuances will have a higher preference towards preserving their debt capacity. Hence, we expect leverage-increasing adjustments to occur at a slower pace among concentrated ownership firms.
21 To test our hypotheses, we consider the following asymmetric capital structure adjustment models that interact the deviation of actual leverage from its target with the ownership concentration level:
it Below it Below it Above it Above it it it First Deviation Deviation First Deviation Deviation A D A D * 1 * * 1 * * * 1 * * 1 * * / / 4 3 2 1 0 1 ( 12 )Deviation is the distance between target leverage and actual leverage.
1 * / / it it it D A D A Deviation ( 13 )Deviation will be negative for overlevered firms and positive for underlevered firms.
Above
1 (1Below) is a dummy variable that takes the value of one when Deviation is negative (positive) and zero otherwise.
We estimate target leverage
D/A
*it as the fitted value from Equation (10) where the target is specified as a function of time-varying firm characteristics, year dummies to control for macroeconomic conditions and firm-fixed effects. To estimate the adjustment speed conditional on the level of ownership concentration, we include interactions with First, where First is either %First, First50 or First33, as well as the main effect of First.β1 is the speed of adjustment among overlevered firms when First equals zero. We
expect overlevered firms to decrease their leverage, but as Deviation is negative when leverage is above target, we expect β1 to be positive. If adjustement costs incurred by
concentrated ownership firms delay downward leverage adjustements, β2 will be
negative so that β1 + β2 < β1, where β1 + β2 is the adjustment speed of overlevered
concentrated ownership firms.
β3 is the speed of adjustment among underleverd firms when First equals zero.
We expect underlevered firms to increase their leverage which implies that β3 should be
22 leverage adjustements, β4 will be negative so that β3 + β4 < β3, where β3 + β4 is the
adjustment speed of underlevered concentrated ownership firms.
Accurately estimating Equation (12) requires taking into account the fact that the error term is autoregressive and the grouping structure of the data. To correct for heteroscedasticity across clusters and time-wise autocorrelation, we estimate Equation (12) using a Mixed-effect model (as in Byoun (2008)) allowing for time and firm random and fixed effects. We also include country and industry fixed effects.
The estimations results are reported in Table 10 Panel A (%First), Panel B (First50) and Panel C (First33). The annual rate of capital structure adjustments are greater when actual leverage is above its target level. Panel B Shows that firms without a majority shareholder revert their leverage at an annual pace of 25% when they are overlevered and at an annual speed of 17.6% when they are underlevered. Similar conclusions emerge from Panel A and Panel B. These results are consistent with our hypothesis: overlevered firms have a greater incentive to readjust their capital structure resulting in asymmetric patterns of leverage changes. The three Panels also reveal that ownership concentration negatively affects the annual adjustment speed. Overlevered firms with a majority shareholder undertake downward leverage adjustments at a lower speed of 15.2%, while underlevered firms with a majority shareholder offset annually only 10.2% of their deviation from their target debt ratio. A similar picture emerges when ownership concentration is measured by %First or First33. For instance, in Panel A; we can see that first shareholders at the 20% level display a lower speed of adjustments of 4%7. These results are in line with the hypothesis that concentrated ownership firms incur high adjustment costs that delay leverage-increasing adjustments as well as leverage-decreasing adjustments.
We check the robustness of our results, in unreported tests, by estimating Equation (12) using OLS with clustered-errors and feasible generalized least squares. These alternative methods provide slightly different adjustment speeds but they all lead to similar conclusions.
7 -0.002*20=-0.04
23 V. Conclusion
This paper investigates how European firms, with a different ownership structure respond to various shocks that affect their capital structure and move them away from their target leverage.
Ownership concentration is widespread and has been shown by a large number of papers from the corporate governance literature and the microstructure literature to be responsible for adverse selection problems due to information asymmetry or the extraction of private benefits of control. Large blockholders are also likely to avoid financing decisions that threaten their control over the firm. The presence of a second large shareholder beyond the first one has also been shown as being a potential mean of enhancing price informativeness and reducing wealth diversion and thereby reducing the costs of external finance.
We therefore group firms with regard to the potential impact of their ownership structure on the cost of external finance. Consequently, we are able to test whether the well-documented persistent impact of market timing on capital structure is due to firms’ indifference towards their capital structure or to high adjustment costs preventing them from continuously balancing the benefits and costs of debt financing to maintain their leverage at an optimal level or range.
We estimate the impact of ownership structure on the current prevalence of past timing activities by interacting our ownership measures with the historical market to book ratio. We then apply a dynamic system-GMM procedure to our panel data to estimate the annual rate of adjustment of current leverage towards the target. Our results are strongly consistent with the costly adjustments hypothesis, with firms responding more or less quickly to shocks that affect their capital structure conditional on the extent of adjustment costs they are likely to face. Firms with high levels of ownership concentration display slower annual adjustment speeds
We finally provide evidence of asymmetric adjustments occurring at a greater speed when firms have above target debt levels than when they have below target debt levels. In addition, we show that ownership concentration results in slower adjustment patterns, whether adjustments are meant to increase or to decrease firms’ debt levels.
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28 Table 1
Variables and Abbreviations
Variable Abbreviation Description
Book equity E
Total asset minus total liabilities and preferred stock plus deferred taxes and convertible debt
Book debt D Total assets minus book equity
Market equity Share price times common shares outstanding Market value of assets
Total assets minus book equity plus market equity
Book Leverage Bleverage Ratio of book debt to total assets Market Leverage Mleverage Ratio of book debt to market assets
Market-to-Book ratio MB Market value of assets divided by total assets
Amount of equity issues e
Change in book equity minus the change in balance sheet retained earnings
Amount of newly retained
earnings ∆RE Change in retained earnings
Amount of net debt issues d Residual change in assets
Ratio of net equity issues e/A
Change in book equity minus the change in balance sheet retained earnings divided by assets Newly retained earnings ∆RE/A Change in retained earnings divided by assets Ratio of net debt issues d/A Residual change in assets divided by assets
Profitability Profitability
Earnings before interest, taxes and depreciation divided by assets
Size Size Logarithm of net sales
Asset tangibility PPE
Net plant, property and equipment divided by assets
R&D expenses RD
Research and development expenses divided by assets and replaced by zero when missing
Missing R&D RDD
Dummy variable that takes the value of one when RD is missing and zero otherwise
Non-debt tax shields DEPR Ratio of depreciations to total assets
External finance-weighted
market to book ratio MBefwa
is t s t r ir ir is is it efwa MB d e d e B M
0 0 ,29 Table 1 (Cont’d)
Variable Abbreviation Description Deviation from
target leverage Deviation
Distance of actual leverage from the target (Target leveraget - Leveraget-1)
Overlevered firms 1Above
Dummy variable equal to one when the firm is overlevered (Deviation<0) and zero otherwise
Underleverd firms 1Below
Dummy variable equal to one when the firm is underleverd (Deviation>=0) and zero otherwise Cash flow rights of
the first shareholder %First Cash flow rights of the first shareholder
Majority Shareholder First50
Dummy variable equal to one in the presence of a majority shareholder and zero otherwise
Shareholder at the
1/3 level First33
Dummy variable equal to one in the presence of a first shareholder at the 1/3 level and zero otherwise
Second Shareholder Second10
Dummy variable equal to one in the presence of a second shareholder at the 10% level when the first shareholder holds at least 1/3 of the cash flow rights.
30
Table 2.
Descriptive Statistics. Financial Variables
Book Leverage % Market Leverage % Ln(Sales) Tangibility % Profitability % MB R&D % Depreciation%
Full Sample
N 3290 3289 3290 3290 3290 3290 3290 3290
Mean 52.065 39.824 4.500 19.020 7.533 1.760 2.523 5.593
Median 54.117 38.667 4.419 12.176 9.673 1.367 0.000 4.112
SD 21.197 22.010 1.660 19.212 15.780 1.222 6.466 6.259
Firms with a majority blockholder
N 1015 1014 1015 1015 1015 1015 1015 1015
Mean 56.211 44.358 4.857 21.366 9.908 1.610 1.538 5.403
Median 59.126 45.261 4.792 15.970 10.741 1.299 0.000 4.078
SD 20.751 22.122 1.592 18.772 13.490 1.003 5.326 6.052
Firms without a majority blockholder
N 2275 2275 2275 2275 2275 2275 2275 2275
Mean 50.215 37.803 4.340 17.974 6.473 1.826 2.962 5.678
Median 52.006 35.358 4.203 10.099 9.164 1.412 0.000 4.142
SD 21.137 21.661 1.665 19.318 16.594 1.302 6.870 6.349
Equality of mean test *** *** *** *** *** *** *** *
Firms with a single blockholder (>=1/3 cash flow rights)
N 1107 1106 1107 1107 1107 1107 1107 1107
Mean 55.066 42.914 4.767 19.438 9.510 1.645 1.878 5.203
Median 57.462 42.722 4.708 14.518 10.361 1.316 0.000 4.027
SD 20.923 22.243 1.619 17.433 13.480 1.043 6.021 5.420
Firms with a second blockholder (>=10% cash flow rights) beyond the reference blockholder (1/3 cash flow rights)
N 506 506 506 506 506 506 506 506
Mean 54.047 42.723 4.396 19.170 7.601 1.622 1.162 6.451
Median 56.695 41.312 4.364 12.881 10.190 1.340 0.000 4.504
SD 22.345 22.556 1.581 19.518 15.672 1.080 3.893 8.529
31
Table 3.
Descriptive Statistics. Ownership Concentration.
%First is the first shareholder share ownership. %Second is the percentage of cash flow rights held by the second shareholder. %First (%Second) equals to zero if the first (second) shareholder holds less than 5% of the firm cash flow rights. First50 is set to one when the firm has a majority shareholder (holding more than 50% of the cash flow rights). First33 is set to one when the firm's reference shareholder holds at least one third of the cash flow rights. Second10 is set to one when the firm has a second reference blockholder (10% level) beyond the first one (1/3 of the cash flow rights)
COUNTRY %First %Second First50 First33 Second10
Switzerland N 94 94 94 94 25 Mean 24.20 8.98 0.16 0.27 0.24 Median 18.55 8.39 0 0 0 SD 17.63 6.87 0.37 0.44 0.44 Germany N 1028 1028 1028 1028 472 Mean 34.41 9.98 0.26 0.46 0.31 Median 30.58 8.36 0 0 0 SD 19.95 8.69 0.44 0.50 0.46 France N 1020 1020 1020 1020 616 Mean 41.32 9.97 0.38 0.60 0.39 Median 39.67 9.22 0 1 0 SD 20.14 8.35 0.49 0.49 0.49 Italy N 527 527 527 527 377 Mean 46.31 7.17 0.54 0.72 0.20 Median 51.00 6.34 1 1 0 SD 19.34 7.74 0.50 0.45 0.40 UK N 621 621 621 621 123 Mean 22.54 9.60 0.09 0.20 0.33 Median 16.53 9.23 0 0 0 SD 15.15 5.83 0.29 0.40 0.47 Total N 3290 3290 3290 3290 1613 Mean 35.93 9.42 0.31 0.49 0.31 Median 32.22 8.51 0 0 0 SD 20.65 7.97 0.46 0.50 0.46
