Influence of Firm and Industry Leverage on Stock Returns in Western Europe

Influence of Firm and Industry Leverage on

Stock Returns in Western Europe

ABSTRACT

This paper investigates the relation between leverage and stock returns for a sample of Western European equities during the period 2000-2012. Following previous studies this paper separates the level of external financing in an industry from that in a particular firm. Opposed to other authors this paper does provide evidence for a significant negative relation between industry gearing and stock returns. In line with previous studies evidence for a negative and significant relation between firm gearing and stock returns is found.

JEL classification: D22, G30, G32

Keywords: Abnormal returns, Capital structure, Gearing, Leverage, Industry

Student Number: s1795546

Name: Gido de Jong

Address: Marktstraat 10A, 9712PC

City: Groningen

University: University of Groningen

Faculty: Faculty of Economics and Business Specialization: MSc International Financial Management

Supervisor: Dr. H. Gonenc

Co-assessor: Dr. H von Eije

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1. Introduction

The seminal work by Modigliani and Miller (1958, henceforth MM) introduced the proposition that the expected return on equity should increase with the amount of debt in a firm’s capital structure under the assumption that corporate taxes are in place. Their theory recognizes the tax benefits of interest payments. This means that issuing bonds effectively reduces the tax liability of a firm. In other words, the actual rate companies have to pay for bonds they issue is less than the nominal rate of interest because of the tax savings. To this day this is one of the main principles of modern corporate finance.

However the empirical evidence on the relation between financial leverage, also known as gearing, and stock returns is scare and contradictory (Korteweg, 2004). Some authors find evidence for a positive relation, while others find evidence for a negative relation between gearing and stock returns. There have even been authors that find no significant evidence for a relation between gearing and stock returns.

The work of Muradoglu and Sivaprasad (2012) introduces industry in the relation between gearing and stock returns. They argue like Tucker and Stoja (2011) that the costs and benefits of debt financing will vary across industries, and firms most likely set their gearing ratios in relation to the norm for their industry. The introduction of industry into the relation between gearing and stock returns makes it possible to account for industry influences. This makes it possible to give a better and more accurate estimation of the relation between gearing and stock returns.

- 3 - there are many differences between firms based on the Continental and Anglo-Saxon capitalistic system (Cernat, 2002). First, this paper will make an addition to literature by changing the dataset to continental Western Europe and test if the findings of Muradoglu and Sivaprasad (2012) still hold notwithstanding the aforementioned differences in corporate finance. Second, this paper will look at differences between Western European countries, by comparing the results for each individual country. This makes it possible to make an international comparison of the relation between gearing and stock returns.

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2. Literature Review

For years not much was known about the influence of capital structure on the value of a firm. Modigliani and Miller (1958) made major contribution to this subject. Based on the theory of MM (1958) it was understood that capital structure is highly relevant in determining the value of a firm. Basically in Proposition 1 MM (1958) explains that the rate of return on real assets, and not the different types of securities that are issued affect the value of the firm. MM (1958) Proposition 2 states that though the value of a levered firm remains constant the cost of equity increases due to the risk of debt. Those propositions do hold in a world without taxes and bankruptcy risk. When taxes are introduced the MM (1958) theory recognizes that interest payments have tax benefits, and that issuing bonds effectively reduces the tax liability of a firm. Paying dividends on equity however does not reduce the tax liability. This implies that all firms should choose maximum debt levels, however this theory does not predict behavior of firms in the real world. This chances when MM (1958) introduce financial distress risk. It follows that investors would demand a higher premium on the stock of a company with high debt levels. This implies that a firm’s capital structure decisions involve a tradeoff between the tax benefits of debt and the costs of financial distress. This theory is frequently called the tradeoff or static tradeoff theory of capital structure (Ross et al., 2011). In other words, there is an optimal level of debt for each individual company, which can be seen as the target level of debt for the firm. However, since financial distress costs cannot be expressed in a precise way, no formula has yet been developed to determine a firm’s optimal debt level exactly.

- 5 - the negative relation between gearing and returns is due to the sensitivity of high levered firms to financial distress risk. Garlappi and Yan (2011) examine the link between distress risk and asset returns by introducing gearing in an equity valuation model. They find that gearing helps in explaining stock returns especially for firms with high default probabilities. The first who look into industry in this relation are Muradoglu and Sivaprasad (2012). They take into account the industry characteristics and the sector a firm belongs to, in examining the relation between gearing and stock returns. By doing this they recognize the fact that the costs and benefits of debt financing will vary across industries and firms most likely set their gearing ratios in relation to the norm for their industry (Tucker and Stoja, 2011). Beattie, Goodacre and Thomson (2006) argue that besides business risk, asset structure and growth opportunities, also industry specific characteristics drive gearing ratios. Where many previous studies investigate the tax implications of debt financing on the cost of capital Muradoglu and Sivaprasad (2012) do not look at the tax implications the main objective of their paper is to undertake a direct test of Proposition 2 of MM (1958) by separating the effect of industry gearing from firm gearing. Their paper is based on a dataset containing information on the U.K. stock market.

- 6 - Different methods have been used by previous authors to measure firm performance. Two measures that have been used most are accounting profits and stock returns. For example Hamada (1972) uses accounting profits. The limitation of using accounting profits to measure firm performance is that accounting profits are not forward looking compared to stock returns. Since, stock returns integrate expectations of investors about future value of the firms. Stock returns as a measure of firm performance are used by Bhandari (1988), which uses inflation adjusted returns as an improvement over accounting profits in measuring firm performance. Still, the returns are not adjusted for stock market movements in this measurement. More recent studies by Dimitrov and Jain (2008), Korteweg (2010) and Muradoglu and Sivaprasad (2012) use risk adjusted returns that are adjusted for all market movements. Muradoglu and Sivaprasad (2012) argue that by observing firms’ gearing decisions, the market learns about the performance of the firm, potentially beyond what can be learned from profits or other accounting variables.

- 7 - more than 50% of total outstanding shares owned. This differs markedly from Germany which is an exponent country of the Continental capitalism. The Continental model is characterized by close coordination between political parties, trade unions and industry associations. And it is generally associated with a neocorporatist pattern of interest intermediation, featuring strong voice for organized labor in policy making (Cernat, 2002). Unlike the Anglo-Saxon system the Continental capitalism is based on a prominent role of banks in corporate finance and control. In this model it is quite common that banks own significant proportions of shares in their portfolios as a way to control the economic activities of their major clients (Dittus and Prowse, 1996). It is also often found that bank representatives are found on the boards of directors of companies they offered large loans. These organizational features and bank versus enterprise interaction creates a more secure economic environment that allows firms to seek higher profits on the long-term. Which is opposite to the short-term view imposed by stock markets on Anglo-Saxon companies (Smyser, 1992).

- 8 -

3. Hypotheses

This study will make a distinction between firm gearing and industry gearing and use both as separate predictors of stock returns. This will enable an analysis for variation in gearing within and across industries. Since the empirical evidence in literature ignores industry gearing besides firm gearing mainly mixed evidence is provided on the relation between firm gearing and stock returns (Muradoglu and Sivaprasad, 2012). This study will analyze the relation between gearing and stock returns using book gearing. This is because book gearing denotes the cash flows generated by the financing activity (Schwarz, 1959) and they refer to assets already in place whereas market values represents the present value of future growth opportunities and hence represents assets not yet in place (Myers, 1977).

- 9 - The second hypothesis is about industry gearing and states: the market incorporates information about industry gearing in stock prices. Industry gearing is a measure of financial risk in a particular industry. Firms in industries that use higher gearing to finance their assets bear higher risks to equity holders. This will lead to a higher rate of return required by equity holders of firms in those industries. As a result it may be expected that stock returns will be higher in industries with high gearing.

MM (1958) conduct tests in two different industries since each industry represents its own risk for investors. Also Schwarz (1959) explains that the optimal capital structure of a firm varies for firms in different industries mainly because asset structures and stability of earnings vary with different types of production. Schwarz (1959) divides total risk into external risk and internal risk, external risk is to a large extend determined by nature of the industry a firm belongs to, internal risk is the financial risk determined by the capital structure of the firm. Bradley et al. (1984) find strong industry influences across firm gearing ratios. They suggest that a good proxy of business risk is the industrial classification, since 54% of the variation in firm gearing is explained by industry classification. From this follows the third hypothesis which states: the relation between gearing and returns can differ across industries.

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4.1.

Data

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4.2.

Methodology

The methodology used in this paper follows the methodology described in the study of Muradoglu and Sivaprasad (2012). The capital gearing definition is used to represent the gearing of firms. The gearing of a firm which is normally expressed as a percentage represents the total debt to total financing of the firm. As argued before the book value of gearing is used as the relevant measure. Gearing is defined in equation (1). Gearing ratios used in this study will be obtained for each company from Datastream as of May of year t.

- 12 - Stock returns for each company are estimated monthly, using the percentage change in consecutive closing prices. The way of calculating abnormal returns is shown in equation (2).

with,

representing the abnormal returns of stock i in month t,

representing the monthly return of stock i in month t, and

representing the monthly return of stock m in month t, represented by the market portfolio.

Cumulative average abnormal returns (CAARs) are calculated over a twelve month period, t-tests are used to test for differences from zero (Brown and Warner, 1985; MacKinlay, 1997). The following equations are used for calculating the CAARs and t-statistic:

∑ ∑ where,

is the cumulative abnormal returns for stock i over a twelve month period denoted as T, represents cumulative average abnormal returns for period T,

_{, and}

- 13 - The next step is to determine if CAARs can be explained by the gearing ratio of a firm. This is done by estimating regressions. The specific control variables used in the estimations are as aforementioned: market risk, firm size, price-earnings ratio and market-to-book ratio.

The equations (6) and (7) are estimated in the full sample of firms, and for each industry sector.

where,

is defined as in equation (4), α represents a constant, is measured as defined in

equation (1). is the log of total market value measured as of the beginning of May of year t.

is the market-to-book ratio, represents the ratio of price-to-earnings both measured as

of the beginning of May of year t. rate is the average monthly ECB interest rate as of the beginning of May year t till the end of April of year t + 1. is the market risk estimated as the

beta coefficient over the preceding year using monthly data as of the beginning of May of year t. is the error term. And is calculated by observing the median of the gearing of all

individual firms in each industry sector in May of year t.

- 15 -

5. Results

5.1.

Descriptive statistics

Initial analyses of the variables indicated non-normality for all variables, which indicates outliers. The winsorizing technique is used to adjust the sample. Especially the distribution of the variables CAAR, Size, Market-to-Book and Price-earnings included outliers. For those variables 90% winsorization is used to set all observations below the 5th percentile to the 5th percentile, and all observations above 95th percentile to the 95th percentile. The winsorization level applied is chosen, since it gave the best results and it is also commonly used in finance studies (Gosh and Vogt, 2012). The variables Gearing, Industry Gearing, Interest and Risk are not winsorized since there distribution contained not specific outliers, or their distribution was in line with the nature of the variable. All observations reported hereafter will be based on the sample after winsorization. The descriptive statistics of 1212 year-end observations for a sample 118 companies for the period January 2001 till December 2012 are shown in Table 1. The descriptive statistics are given for each variable; all variables are specified as aforementioned in the data and methodology section.

Table 1 Descriptive statistics of 1212 year-end observations for a sample 118 companies for the period January

2001 till December 2012.Descriptive statistics are given for each variable. All variables are defined as described in the data and methodology section. Figures for CAAR, Gearing, Industry Gearing, Market-to-Book, Price-earnings and Interest are in percentages, Size and Risk are normal numbers.

CAAR Gearing Industry Gearing Size Market-to-Book Price-earnings Interest Risk

Mean 0.77 38.50 39.05 3.95 2.50 19.59 2.35 0.94 Median 0.73 37.72 36.52 3.96 2.09 17.00 2.31 0.90 Maximum 3.94 94.61 62.97 5.19 6.15 53.37 4.33 3.51 Minimum -2.17 0.21 22.75 2.42 0.74 6.80 0.81 -1.36 Std. Dev. 1.65 19.64 8.56 0.59 1.48 11.46 1.16 0.69 Skewness 0.12 0.18 0.87 -0.27 1.03 1.57 0.16 0.30 Kurtosis 2.30 2.73 2.90 2.82 3.23 5.14 1.68 3.28 Jarque-Bera 27.79 10.26 153.83 15.86 216.42 730.81 93.53 21.99 No. of Observations 1212 1212 1212 1212 1212 1212 1212 1212

- 16 - their study they also find higher correlation between firm gearing and industry gearing. The higher correlation between those two variables could be explained by the nature of the variables. Since industry gearing is based on the median of the average individual firm gearing levels. The problem with high correlation between variables is the possibility of multicollinearity. But following Brooks (2008) a correlation of 33.3% between two variables is not considered as a problem with multicollinearity. However the correlation is taken into account since high correlation between variables can have an influence of the inference from a model.

Table 2 present some descriptive statistics of the firm gearing in the different industry sectors. Since in the data sample not all industries were represented and some industries had only a few representative firms. In this paper firms from different US SIC sectors are combined to form one industry. Combinations are based on firm gearing levels and relatedness of the original US SIC industry sectors. However most industry classifications are based on the original US SIC classification obtained from Orbis.

Table 2 Descriptive statistics of the firm gearing for each industry sector. Agriculture, Mining & Construction

contains al firms with SIC codes ranging from 0000 – 1999. Manufacturing contains al firms with SIC codes ranging from 2000 – 2999. Industrials are all firms with SIC codes ranging from 3000 – 3999. In sector Transportation, Communication, Electric & Gas are all firms with SIC codes ranging from 4000 – 4999. And the sector Retail, Trade & Services contains al firms with SIC codes range 5000 – 5999 and 7000 – 8999. All figures are in percentage. Agriculture, Mining & Construction Manufacturing Industrials Transportation, Communications,

Electric & Gas

Retail, Trade & Services Mean 32.12 32.96 37.88 49.86 41.95 Median 30.56 33.14 37.42 51.66 41.59 Maximum 72.34 81.84 86.22 93.16 94.61 Minimum 0.46 0.21 0.53 0.59 0.42 Std. Dev. 18.01 16.25 17.80 20.06 23.27 No. of Observations 117 338 374 193 190

- 17 - median firm gearing. The mean and median firm gearing with values of 32.12 and 30.56 respectively are the lowest in the Agriculture, Mining and Construction sector.

In table 3 the sample is divided per country and shows the mean, median and number of observations for each variable.

Table 3 Mean, Median and Number of Observations for 1212 year-end observations for a sample 118 companies for the period January 2001 till December 2012.The numbers are given for each variable and divided per country. All variables are defined as described in the data and methodology section. Figures for CAAR, Gearing, Industry Gearing, Market-to-Book, Price-earnings and Interest are in percentages, Size and Risk are normal numbers.

CAAR Gearing Industry

Gearing Size

Market-to-Book

Price-earnings Interest Risk

Austria Mean 1.06 38.74 39.73 3.28 2.04 16.47 2.27 0.88 Median 0.97 37.64 36.68 3.25 1.67 14.00 2.09 0.88 No. of Observations 124 124 124 124 124 124 124 124 Belgium Mean 0.79 40.22 39.78 3.62 2.15 16.64 2.36 0.70 Median 0.75 36.93 36.15 3.69 1.68 14.25 2.31 0.66 No. of Observations 132 132 132 132 132 132 132 132 France Mean 0.56 41.58 39.48 4.19 2.36 20.36 2.36 1.03 Median 0.52 42.19 36.52 4.18 2.06 17.50 2.31 1.00 No. of Observations 311 311 311 311 311 311 311 311 Germany Mean 0.95 41.09 39.07 4.08 2.08 20.00 2.38 1.06 Median 0.92 41.85 36.52 4.08 1.77 17.40 2.31 1.03 No. of Observations 265 265 265 265 265 265 265 265 Luxembourg Mean 0.92 14.25 37.36 3.23 1.27 16.43 2.33 0.84 Median 0.61 9.68 36.06 2.58 0.99 9.50 2.31 0.78 No. of Observations 35 35 35 35 35 35 35 35 The Netherlands Mean 0.63 39.74 38.62 3.86 3.21 21.32 2.35 1.00 Median 0.65 38.58 35.51 3.93 2.78 17.65 2.31 0.94 No. of Observations 184 184 184 184 184 184 184 184 Switzerland Mean 0.72 30.52 37.90 4.30 3.54 20.93 2.34 0.75 Median 0.66 28.35 35.34 4.24 3.35 19.40 2.09 0.69 No. of Observations 161 161 161 161 161 161 161 161

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Table 4 Mean, Median and Number of Observations for 1212 year-end observations for a sample 118 companies for the period January 2001 till December 2012.The numbers are given for each variable and divided per country. All variables are defined as described in the data and methodology section. Figures for CAAR, Gearing, Industry Gearing, Market-to-Book, Price-earnings and Interest are in percentages, Size and Risk are normal numbers.

CAAR Gearing Industry Gearing Size

Market-to-Book

- 19 - In table 4 the sample is divided per year and the mean, median and number of observations for each variable is reported. It is observed that CAARs are fluctuating over time, and gearing is decreasing of the years, industry gearing follows this trend. The mean and median size of the firms in the sample is growing over time, for the other variables no specific trends are observed over the sample period.

5.2.

Analysis

The coefficient estimates of the cross-sectional regressions are reported in Table 5. The regressions are estimated in the full sample comprising 8,484 observations for 118 firms. For the full sample the regression indicates a negative and significant coefficient for firm gearing. In other words, this indicates a negative and significant relation between CAARs and firm gearing. A one percent increase in firm gearing would lead to a 0.02 percent decrease in CAARs. The coefficient estimates for Size and Market-to-Book, Price-earnings and the Constant ( ) are also significant. The coefficient estimates for Interest and Risk are not significant for the full sample.

Table 5 Regression results for firm gearing. This table reports the results of the cross-sectional regressions on

CAARs and gearing, size, market-to-book ratios, price-earnings ratios, interest and risk. Regressions are made first on the full sample and second on the industry sectors. All variables are specified as mentioned before in the data and methodology section. Coefficients for all estimates are reported, t-statistics are reported in parenthesis. Adjusted R-squared and Number of Observations are shown for each separate regression. Values with * are significant at 10% level, values with ** are significant at 5% level, values with *** are significant at 1% level.

Gearing Size Market-to-Book

Price-earnings Interest Risk Constant

Adjusted R-squared Obs. Full Sample -0.0219 1.7919 0.1528 0.0154 0.1451 -0.2164 -5.4210 0.2566 1212 (-8.330)*** (5.256)*** (3.602)*** (3.353)*** (2.527) (-1.378) (-3.737)***

Agriculture, Mining & Construction -0.0026 -0.7190 0.8964 -0.0215 1.6641 0.0776 -1.9776 0.3454 117 (-0.143) (-0.588) (4.477)*** (-1.446) (7.910)*** (0.324) (-0.489) Manufacturing -0.0083 1.9037 0.0310 0.0061 0.8276 -0.1838 -7.9722 0.4405 338 (-1.784)* (3.689)*** (0.304) (0.729) (15.085)*** (-1.099) (-3.817)*** Industrials -0.0310 1.3521 0.2873 0.0092 -0.0634 -0.1807 -2.9304 0.3219 374 (-2.464)** (2.566)** (1.483) (0.772) (-0.261) (-1.205) (-1.618) Transport, Communications, Electric & Gas -0.0220 0.6777 0.3020 0.0337 -1.6229 -0.4527 3.5980 0.2419 193 (-1.373) (0.578) (2.516)** (2.423)** (-2.005)** (-1.364) (1.027)

- 20 - Estimations are repeated for all industry sectors and de coefficient estimate of gearing remains negative. Although the negative relation between CAARs and firm gearing holds for all industry sectors, the relation is not significant in the Agriculture, Mining and Construction sector and also not in the Transportation, Communications, Electric and Gas sector. For all other industry sectors the coefficient estimates for firm gearing are negative and significant. For the Size and Market-to-Book coefficient estimates that are significant for the full sample, the results for the industry sectors are changing. For the Size variable the coefficient is positive in four of the five industry sectors and in three of those the estimate is significant. The Market-to-book coefficients are positive in all sectors and significant for two of the five industries. The coefficient estimates for the other variables give mixed results for the different industry sectors. Some coefficients become significant for certain industry sectors, however no coefficient stays negative or positive for all industries. The coefficient for Risk is not significant in any industry sector. The estimations are repeated with removing some variables that are not significant. The significant coefficient estimates do not change significantly by doing this, which indicates that there is no multicollinearity between variables.

In Table 6 the results are reported if one additional explanatory variable is added to the regression. The variable that is added is the industry gearing level which is calculated by observing the median of the gearing of all individual firms in each industry sector in May of year t.

- 21 - (1958) especially for their full sample and the Utilities sector. Only the Agriculture, Mining and Construction sector and the Retail, Trade and Services sector provide significant evidence for this relation.

Table 6 Regression results for industry gearing. This table reports the results of the cross-sectional regressions

on CAARs and gearing, industry gearing, size, market-to-book ratios, price-earnings ratios interest and risk. Regressions are made first on the full sample and second on the industry sectors. All variables are specified as mentioned before in the data and methodology section. Coefficients for all estimates are reported, t-statistics are reported in parenthesis. Adjusted R-squared and Number of Observations are shown for each separate regression. Values with * are significant at 10% level, values with ** are significant at 5% level, values with *** are significant at 1% level.

Gearing Industry

Gearing Size

Market-to-Book

Price-earnings Interest Risk Constant

Adjusted R-squared Obs. Full Sample -0.0192 -0.0312 1.6971 0.1749 0.0143 0.3357 -0.1954 -4.5677 0.2602 1,212 (-6.566)*** (-4.041)*** (4.982)*** (4.283)*** (3.110)*** (4.474)*** (-1.251) (-3.065)*** Agriculture, Mining & Construction -0.0026 0.0344 -0.7190 0.8964 -0.0215 1.5350 0.0776 -2.3716 0.3454 117 (-0.143) (1.906)* (-0.588) (4.477)*** (-1.446) (9.321)*** (0.324) (-0.571) Manufacturing -0.0083 -0.0253 1.9037 0.0310 0.0061 0.8636 -0.1838 -7.3083 0.4405 338 (-1.784)* (-1.607) (3.689)*** (0.304) (0.729) (16.435)*** (-1.099) (-3.053)*** Industrials -0.0310 0.0052 1.3521 0.2873 0.0092 -0.1106 -0.1807 -2.9990 0.3219 374 (-2.464)** (0.260) (2.566)** (1.483) (0.772) (-0.750) (-1.205) (-1.545) Transport, Communications,

Electric & Gas

-0.0220 -1.7271 0.6777 0.3020 0.0337 -1.3489 -0.4527 90.0244

0.2419 193 (-1.373) (-2.665)*** (0.578) (2.516)** (2.423)** (-1.902)* (-1.364) (2.841)***

Retail, Trade & Services

-0.0333 0.0558 2.4338 -0.0554 0.0183 0.1930 0.3621 -11.1309

0.2442 190 (-2.886)*** (2.804)*** (3.008)*** (-0.445) (1.437) (0.939) (1.257) (-5.136)***

- 22 - and significant for the full sample. For the different industry sectors the sign and significance levels are the same as reported in table 4.

The results presented here lead to the conclusion that the relation between CAARs and firm gearing is negative and significant for the full sample, and most of the industry sectors. The relation between CAARs and industry gearing is not that obvious, for the full sample it is negative and significant; however this is not for all individual industry sectors.

Additional regressions are run to analyze differences between years or countries. The results for those regressions are reported in table 7 till 10. Table 7 and 8 contain the results of the regressions per country. Table 7 reports the results of the regression with only firm gearing as the gearing variable, table 8 reports results of regressions including both firm and industry gearing.

Table 7 Regression results for firm gearing. This table reports the results of the cross-sectional regressions on CAARs and gearing, industry gearing, size, market-to-book ratios, price-earnings ratios interest and risk. Regressions are run for each separate country. Belgium and Luxembourg are combined in one regression since the number of observations for Luxembourg is too small to run a separate regression. All variables are specified as mentioned before in the data and methodology section. Coefficients for all estimates are reported, t-statistics are reported in parenthesis. Adjusted R-squared and Number of Observations are shown for each separate regression. Values with * are significant at 10% level, values with ** are significant at 5% level, values with *** are significant at 1% level.

Gearing Size Market-to-Book

Price-earnings Interest Risk Constant

- 23 - The estimated coefficients differ across the countries, remarkable result is that the coefficient for Interest is highly significant in all countries. France and Germany have the most observations as individual countries, and show changing results across the variables. However the coefficient estimates for firm gearing and industry gearing are significant and negative for both countries. This is in line with the results reported in table 5 and 6. There is no evidence for a significant positive relation between CAAR and industry gearing as reported by MM (1958) and Muradoglu and Sivaprasad (2012). The coefficient estimates for the Netherlands in table 7 are all significant except the coefficient for Risk. In table 8 industry gearing and Risk are insignificant for the Netherlands, all other variables are significant again. The coefficient estimates for other countries does not show a particular trend. The coefficients differ between positive and negative and show different levels of significance or are not significant at all.

Table 8 Regression results for industry gearing. This table reports the results of the cross-sectional regressions on CAARs and gearing, industry gearing, size, market-to-book ratios, price-earnings ratios interest and risk. Regressions are run for each separate country. Belgium and Luxembourg are combined in one regression since the number of observations for Luxembourg is too small to run a separate regression. All variables are specified as mentioned before in the data and methodology section. Coefficients for all estimates are reported, t-statistics are reported in parenthesis. Adjusted R-squared and Number of Observations are shown for each separate regression. Values with * are significant at 10% level, values with ** are significant at 5% level, values with *** are significant at 1% level.

Gearing Industry Gearing Size Market-to-Book

Price-earnings Interest Risk Constant

- 24 -

Table 9 Regression results for firm gearing. This table reports the results of the cross-sectional regressions on CAARs and gearing, industry gearing, size, market-to-book ratios, price-earnings ratios and risk. Interest is left out of this regression since this created collinearity problems. Regressions are run for each year separately. All variables are specified as mentioned before in the data and methodology section. Coefficients for all estimates are reported, t-statistics are reported in parenthesis. Adjusted R-squared and Number of Observations are shown for each separate regression. Values with * are significant at 10% level, values with ** are significant at 5% level, values with *** are significant at 1% level.

Gearing Size Market-to-Book

Price-earnings Risk Constant

- 25 - Tables 9 and 10 give the regression results for firm and industry gearing divided respectively. The regression is estimated for each year separately. It is important to note that Interest is left out of these regressions since including this variable in the regressions created collinearity problems. Dummies for industry and country effects are included in those regressions. The coefficient estimates of firm gearing are mostly negative but only significant for the years 2007 and 2012. However there are some years indicating a positive relation between firm gearing and stock returns. In the years 2005 and 2006 a positive and significant relation between CAAR and industry gearing is reported. This is the only significant evidence that is found for the positive relation between stock returns and industry gearing based on the regressions divided per year.

- 26 -

Table 10 Regression results for industry gearing. This table reports the results of the cross-sectional regressions on CAARs and gearing, industry gearing, size, market-to-book ratios, price-earnings ratios and risk. Interest is left out of this regression since this created collinearity problems. Regressions are run for each year separately. All variables are specified as mentioned before in the data and methodology section. Coefficients for all estimates are reported, t-statistics are reported in parenthesis. Adjusted R-squared and Number of Observations are shown for each separate regression. Values with * are significant at 10% level, values with ** are significant at 5% level, values with *** are significant at 1% level.

Gearing Industry Gearing Size Market-to-Book

Price-earnings Risk Constant

- 27 -

6. Conclusion

This study examines the relation between gearing and stock returns. This is done by analyzing the relation at the firm and industry level. The findings of this study show that CAARs decrease when firm gearing increases. The relation between industry gearing and CAARs is more ambiguous; however for the full sample a negative and significant relation is shown. Although in some of the sub samples the relation is positive and significant. The main results imply that for Western European countries based on the continental capitalistic system, firm and industry gearing have a significant negative effect on stock returns. With respect to the hypotheses stated earlier in this paper the following conclusions can be made. The first hypothesis stated as: the market incorporates information about firm gearing in stock prices. This hypothesis is confirmed based on the results found in this study. This is in line with other authors like Korteweg (2004), Penman et al. (2007) and Muradoglu and Sivaprasad (2012). The second hypothesis is stated as: the market incorporates information about industry gearing in stock prices. Based on MM (1958) this would mean that stock returns would be higher for firms in industries with high average gearing levels. This hypothesis is not confirmed by the results of this study. No convincing statistical evidence is found for a positive relation between CAARs and industry gearing. The relation is only positive and significant for some individual industries, years and countries. The main results for the relation between industry gearing and CAARS are negative and significant. The third hypothesis states: the relation between gearing and returns can differ across industries. In this study evidence is found to confirm this hypothesis. Using cross-sectional regressions for different industry sectors it is proven that differences across industries exist.

- 28 - This paper extended the work of Muradoglu and Sivaprasad (2012) by following their method and investigating if their findings hold for a sample of firms from a different capitalistic system. In this way this paper contributes to the asset pricing literature by analyzing the capital structure. First by looking at firm gearing, and second by taking the effect of industry on gearing into account. This is a new way of looking at the relation between stock returns and capital structure which was done for the first time by Muradoglu and Sivaprasad (2012). The distinction between firm level gearing and industry level gearing is important to acknowledge the fact that debt requirements can differ along industries. For example, some heavy industries could require higher gearing levels. However each individual company may have its own unique preferences for a particular capital structure. Giving equal weights to the role of firm gearing and industry gearing in their relation to stock returns was never done in literature, before Muradoglu and Sivaprasad (2012). This paper made an addition to their work by changing the sample. This was recommended by Muradoglu and Sivaprasad (2012) who recognized the fact that their sample was limited to one country as one of the most important weaknesses. This study includes a sample of seven Western European countries which makes it possible to compare the results of this paper with Muradoglu and Sivaprasad (2012). But also make comparison of individual Western European countries possible. In this way this paper overcomes the main weakness of Muradoglu and Sivaprasad (2012).

This study has also its limitations. Most important is the fact that only data from recent years and large cap firms is used in constructing the sample. Future research could add to this study by using a sample with data from a longer period and including middle and small cap firms. Where this study focusses only on the relation between abnormal returns and gearing, future research could look at the possibility of predicting stock returns using gearing. Future research can extend the model used in this study by taking into account other economic, political and legal differences across firms, industries and countries.

- 29 - paper provides evidence for a difference between firms in Western Europe and firms in the U.K. when looking at the relation between gearing and stock returns. Moreover when looking at individual Western European countries differences in the relation between gearing and stock returns are observed. By showing this relations this paper does provide useful information to large cap firms in Western Europe. The results of this paper can help managers when they have to make decisions about a firm’s debt level.

Acknowledgements

- 30 -

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Appendices

Appendix 1

Variable, Datastream code and Definition

Variable Datastream code Definition

Cumulative average abnormal returns

PI Cumulative average abnormal returns for stock i over period t.

Gearing (WC08221), 731 Ratio of book value of debt divided by total

financing of the firm.

Industry gearing Median of the gearing of all individual firms in

each industry sector in May of year t.

Size MV The log of total market value measured as of

the beginning of May of year t.

Market-to-Book MTBV Company closing share price multiplied by the

amount of shares, divided by book value as of the beginning of May of year t.

Price earnings ratio PE Ratio of price to earnings as of the beginning of

May of year t.

Risk Market risk estimated as the beta coefficient

over the preceding year using monthly data as of the beginning of May of year t

Interest rate EMYLVAM The average monthly ECB interest rate as of the

beginning of May year t till the end of April of year t + 1.

Appendix 2

Correlation Matrix

CAAR Gearing Industry

Gearing Size

Market-to-Book

Price-earnings Interest Risk