firm systematic risk: Evidence from Europe
University of Groningen
Faculty of Economics & Business
MSc Finance
Student name: Pavel Borschjov
Student number: 3591948
Supervisor: Dr. A. Dalò
Date: June 4, 2020
Abstract
1. Introduction
Berle and Means (1932) are the pioneers of describing the separation of ownership and control for firms in the United States. In their book, the authors showed how the dispersion of ownership can lead to the abuse of power by managers. The implications of separation of ownership and control are more commonly known as the agency problem, which states that managers are more inclined to exploit their managerial position and firm’s resources to benefit themselves rather than benefitting the shareholders of the firm (Jensen and Meckling, 1976). As the ownership becomes more dispersed, outside investors will find it harder to monitor the managers of the firm. A more condensed ownership structure is believed to mitigate this problem or when the management also holds a stake in the firm (Hoang et al., 2017). Additionally, it is expected that the agency problem could also be alleviated when there is a large shareholder (i.e. blockholder) that could exert his voting rights to influence the decision making of the management and in turn affect the operations of the firm. Since shareholders are mainly concerned with value creation of the firm, they would want the management to make value enhancing decisions on their behalf, which in turn should be reflected in firm’s performance. Moreover, it is possible that different types of ownership structure would have distinct effects on firm’s performance. Additionally, it is plausible that different types of owners will have different levels of risk appetite. Therefore, in addition to firm performance, this paper examines whether ownership structure affects firm systematic risk. More specifically, this paper aims to answer the following research question:
Is firm performance and systematic risk affected by ownership structure of a firm?
In order to measure the effects of ownership structure on firm performance and firm systematic risk, ownership structure is broken down into two elements. Namely, ownership concentration and the identity of the largest owner. This paper shows evidence for a U-shaped relationship between ownership concentration and firm performance and that the identity of the shareholder is relevant for firm performance when the largest shareholder is a corporation or the general public. Furthermore, there is no robust evidence to infer a relationship between ownership structure and firm systematic risk.
The paper is organized as follows. Section 2 will cover the existing literature on and state the hypotheses to be tested. Section 3 describes the dataset and the methodology used to perform the analysis. Section 4 illustrates and describes the main results. Section 5 concludes the main findings of the paper.
2. Literature review
Japan benefit from the transfers of resources by more profitable firms. It is likely that different types of owners have different effects on the performance of firms and that the ownership structure differs across countries and therefore across continents. Firms that are owned by families are expected to profit from a larger efficiency in their production process as opposed to firms that are not owned by families (Gorriz and Fumas, 1996). However, this need not to be true for all countries. Lauterbach and Vaninsky (1999) conclude that firms in Israel, that are largely family-owned and managed, appear to be less efficient in generating profits as opposed to non-family owned and manged firms. Additionally, they counterintuitively conclude that firms tend to perform better if they are not managed by the owners. This appears to be counterintuitive since agency theory suggests that firms with less asymmetrical information are believed to perform better opposed to firms where there is a separation between the agent and principal and the agent has to be properly incentivized to align his or hers goals with the goals of shareholders. As ownership structure tends to vary cross-country and across types of owners it is also likely that the ownership structure varies across industries. Financial services industry in the United Kingdom tend to show different results on the relationship between ownership structure and firm performance. Large shareholding groups accompanied by increased control seem to positively affect firm performance whereas increased ownership concentration shows a negative relationship, suggesting that a standalone measure of ownership structure is not always the appropriate measure for firm’s performance (Mudambi and Nicosia, 1998). As for corporate ownership structure, it is also possible to allow for different measurements of firm performance. The study conducted by Tawfeeq and Alabdullah (2018) analyses the relationship between the two variables for firms in Jordan, where firm performance is measured as market share of the company in order to avoid possible income smoothing by firms. Furthermore, the authors state that the issue associated with the measurement of firm performance invites researchers to conduct additional studies in this field in order to contribute to the already existing literature on the relationship between the two variables. Therefore, the first hypothesis that this paper tests is:
𝐻 : Ownership concentration does not affect firm performance.
results were obtained by De Miguel et al., (2004) who find an inverted U-shape relationship between ownership structure and firm performance for Spanish firms. Additionally, the authors find empirical evidence that confirms the convergence-of-interest effect, where the market value of the firm increases with managerial ownership. This converge-of-interest effect is also supported by Morck et al., (1988), who examined the relationship of managerial ownership and market value for large firms in the U.S and find that this relationship is non-linear. The second hypothesis that this paper tests is:
𝐻 : The shareholder’s identity does not affect firm performance.
Most of the literature mentioned so far failed to relate ownership structure to risk measures of the firm. Firm performance tends to be closely related to the riskiness of the firm. By applying the generalized method of moments (GMM) approach on a sample of 569 listed firms in Canada, Gadhoum and Ayadi (2003) find a negative relationship between the risk of a firm and its ownership structure. They argue that this relationship is mainly driven by the variance and the aggregate voting rights by the five largest shareholders of a firm. The explanation that the authors provide for this relationship, is that when controlling shareholders hold a large share of a firm’s outstanding equity are prone to avoid risky projects which are more attractive to minority shareholders because there is an incentive to protect their family’s dynasty an personal utility. That is, shareholder with a larger stake in a firm have relatively more to lose compared to minority shareholders. This argument is also supported by Pedersen and Thomsen (2003). Therefore, the expectation is that ownership concentration is negatively related to firm systematic risk. In conjunction with firm performance, it is also interesting to examine the relationship between ownership structure and firm systematic risk. Hence, the third and fourth hypotheses that this paper tests are:
𝐻 : Ownership concentration does not affect firm systematic risk. 𝐻 : The shareholder’s identity does not affect firm systematic risk.
3. Research design
3.1 Data
publicly listed firms that are incorporated in Europe. Firm betas are estimated using monthly stock returns over a period with at least 24 observations.
3.2 Empirical Model
In order to examine the relationship between ownership structure and firm performance, a multivariate linear regression is employed where firm performance is measured by Tobin’s Q. In addition to linear relationship, this study incorporates quadratic terms in the regression model to allow for non-monotonic relationship between ownership structure and firm performance, which is in line with previous literature (see, e.g., Hoang, et al., 2017; Morck, et al., 1988; De Miguel, et al., 2004).
Furthermore, this paper examines whether the identity of the largest shareholder affects firm performance. Table 1 presents the various types of shareholders for firms in the sample. Owner identity is divided into seven categories, namely corporate, family, financial company, bank, institutional, public and insurance company ownership since these identities make up more than 90% of the sample. This is similar to the approach used by Thomsen and Pedersen (2000). The authors argue that the identity of owners (in contrast to standalone ownership concentration) not only captures the degree to which managerial decision can be influenced but also has implications for firm strategy and risk preference.
Table 1 Shareholder identity for firms across the sample period
Shareholder identity Frequency Percentage (%)
Corporate 6187 27.7
One or more named individuals or families 4641 20.78
Financial company 2800 12.54
Bank 2629 11.77
Mutual and pension fund, nominee, trust, trustee 1792 8.02
Public 1370 6.13
Insurance company 897 4.02
Private equity firm 533 2.39
Public authority, state, government 392 1.76
Venture capital 357 1.6
Other unnamed shareholders, aggregated 334 1.5
Foundation, research Institute 257 1.15
Self-ownership 66 .3
Employees, managers, directors 36 .16
Unnamed private shareholders, aggregated 33 .15
Hedge fund 9 .04
As argued by Demsetz and Villalonga (2001), ownership structure is endogenous, and it is difficult to find a suitable instrumental variable to account for this endogeneity (Himmelberg et al., 1999). Therefore, this study employs a two-way error component model using lagged values of ownership concentration and shareholder identity as instruments:
𝑄 𝛽 𝛽 𝑂 𝛽 𝑂 𝛽 𝑇 𝛽 𝑋 𝜀 (1)
𝜀 𝜇 𝜆 𝜈 (2)
where 𝑄 denotes Tobin’s Q measure of firm 𝑖 at time 𝑡, 𝑂 is the ownership concentration,
which is measured as the percentage of outstanding shares held by the largest shareholder of
firm 𝑖 at time 𝑡 1, 𝑇 is the identity of the largest investor in firm 𝑖 at time 𝑡 1, 𝑋 is the
𝑘th control variable for firm 𝑖 at time 𝑡, 𝜀 is the error term that includes country- and sector-fixed effects, 𝜇 , time-sector-fixed effects, 𝜆 and stochastic disturbance term, 𝜈 that varies across firms and time.
It also makes sense from an economic point of view to include lagged version of ownership concentration and identity in the model since the assumption is that firm performance in the current period is dependent on the ownership structure in the previous period.
Table 2 presents an overview of the sectors and the number of firms per sector that are used in this study. The sample is dominated by firms residing in the manufacturing sector (36.56%) followed by the information and communication (14.45%) and wholesale and retail trade (8.53%) sectors. Firms in these sectors tend to have a substantial amount of tangible assets relative to total assets and hence it would be expected that Tobin’s Q for firms residing in these sectors will be lower compared to firms with less tangible assets (holding market value of the firm constant). Additionally, it is probable that the relationship between ownership structure and firm value is spurious since Tobin’s Q values and ownership structure are sector or industry specific (McConnell and Servaes, 1990; Demsetz and Lehn, 1985). It is therefore appropriate to control for unobserved sector-specific heterogeneity. Controlling for sector-specific effects is in line with previous literature (see, e.g., Lemmon and Lins, 2003; Himmelberg et al., 1999; Morck et al., 1988).
Table 2 Firms by sector
Sector category Frequency Percentage (%)
Manufacturing 1222 36.56
Information and communication 483 14.45
Wholesale and retail trade; repair of motor vehicles and motorcycles
285 8.53
Professional, scientific and technical activities 264 7.9
Real estate activities 228 6.82
Mining and quarrying 183 5.48
Administrative and support service activities 117 3.5
Other service activities 98 2.93
Transportation and storage 76 2.27
Electricity, gas, steam and air conditioning supply 70 2.09
Human health and social work activities 62 1.86
Arts, entertainment and recreation 54 1.62
Accommodation and food service activities 43 1.29
Agriculture, forestry and fishing 30 .9
Water supply; sewerage, waste management and remediation activities
19 .57
Education 10 .3
Public administration and defence; compulsory social security 8 .24
Total 3342 100
Sector classification is based on the statistical classification of economic activities in the European Community (NACE Rev. 2 main section).
Table 3 shows the distribution of firms across countries. The majority of firms in the sample are located in the U.K. (33.42%) followed by Sweden (17.47%) and France (11.04%). Based on the sector composition in table 2, the expectation would be that the majority of firms (especially in the manufacturing sector) would be located in Germany. However, the reason that this is not the case is that the majority of German firms were traditionally financed with (bank) debt and private equity rather than public equity. The opposite is true for firms in the U.K., which is also evident from table 3 below. This difference could also be accredited to the fact that shareholder rights in U.K. tend to be more favourable compared to Germany (La Porta et al., 1998). Firm performance and the extent of ownership concentration is likely to be affected by cultural aspects of a country (Holderness, 2017). The dispersion of firms across countries in the sample therefore suggests that country-fixed effects should be controlled for in the model.
Table 3 Firms by country
Country of company Frequency Percentage (%)
This study uses a sample of firms located in different countries across Europe and across different sectors. In order to obtain meaningful results, it is therefore essential to measure the relationship between firm performance, firm systematic risk, and ownership structure independently from the sector a firm operates in and the country in which the firm is located. Therefore, country and sector fixed effects are controlled for in equation (1) by including sector and country dummies. In addition to country and sector fixed effects, this study also controls for time fixed effects in order to encapsulate all variables that change over time but do not vary cross sectionally such as inflation, economic growth, technological advancement, and the change in accounting standards.
In addition to firm performance, this paper also examines the relationship between ownership structure and firm’s market risk denoted by the 2-year monthly beta of the firm. Equation (1) is modified such that the dependent variable becomes the monthly beta of firm 𝑖 at time 𝑡. The model becomes,
𝐵 𝛽 𝛽 𝑂 𝛽 𝑂 𝛽 𝑇 𝛽 𝑋 𝜇 𝜀 (3)
where 𝐵 denotes the 2-year monthly beta of firm 𝑖 at time 𝑡. The remainder of the terms in equation (3) are analogous to the terms in equations (1) and (2).
The decision for using the fixed effects estimation technique is supported by the results obtained from the Hausman (1978) test which is presented in table 1A in appendix A to this paper. The results suggest that the null hypothesis, stating that the regressors are not correlated with the composite error term, is rejected at the 1% significance level for the model in equation (1) and at the 5% significance level for the model in equation (3).
3.3 Variables
there is a relationship between dividend policy and firm performance. The payout ratio is calculated as the percentage of net income that is paid out to investors as dividend.
Several variables in the sample data are characterized by outliers. These outliers correspond to values that are rarely observed in practices under normal business conditions. Such outliers are likely to impact the descriptive statistics and regression results. To alleviate the impact of outliers on the results, the data for Tobin’s Q, monthly beta, sales, shareholders’ funds, capital intensity, and dividend payout are winsorized at 1% and 99% level.
4. Data analysis
4.1 Descriptive statistics
ownership concentration (O) in Europe seems to be moderate with the largest shareholder owning on average about 29 percent of a firm’s outstanding shares. This indicates that blockholders on average are also ultimate beneficial owners, that is, they hold more than 25%
of a company’s shares.1 This observation is in line with the mean value for ownership
concentration illustrated by McConnel and Servaes (1990). However, other studies report a mean value for ownership concentration for the largest shareholder between 50% and 62,5% (see, e.g., Konijn et. al., 2011; Kapopoulos and Lazaretou, 2007; Hoang et. al., 2017; Fauzi and Locke, 2012). This shows that ownership concentration tends to vary across countries. Furthermore, the sample data shows that the average ownership concentration across sectors tends to be around 30%. The beta (B) variable has a mean of 0.712 with a standard deviation of 0.798, implying that the average firm in the sample is expected to move less than one-for-one with the domestic market.
Table 4 Descriptive statistics and correlation matrix
Panel A
Variable N Mean Median Std. Dev. Min. Max.
Q 21,389 1.302 0.732 1.762 0.019 13.61 O 15,636 28.74 24.61 22.02 0.010 100 B 18,683 0.712 0.665 0.798 -2.857 4.399 DTE 23,433 0.698 0.365 1.918 -9.874 15.99 LIQ 25,277 2.265 1.115 4.906 0.001 99.40 DIVPAY 9,271 77.89 47.51 129.8 2.691 1,442 CAPINT 23,642 10.61 1.320 46.03 0.220 468.3 SIZE 23,636 11.53 11.61 2.986 2.715 18.10 R&D 22,982 2.391 0 8.467 -77.43 99.94 Panel B Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (1) Q 1.000 (2) O -0.115 1.000 (3) B 0.012 -0.067 1.000 (4) DTE -0.146 -0.028 0.034 1.000 (5) LIQ 0.100 0.033 -0.046 -0.107 1.000 (6) DIVPAY -0.045 -0.024 0.006 0.022 0.006 1.000 (7) CAPINT -0.072 0.057 -0.042 0.013 0.319 0.001 1.000 (8) SIZE -0.133 -0.051 0.252 0.143 -0.272 -0.001 -0.222 1.000 (9) R&D 0.197 -0.003 0.060 -0.076 0.038 -0.006 -0.062 0.041 1.000 The variables are: Tobin’s Q (Q), ownership concentration in % (O), 2-year monthly beta (B), debt-to-equity ratio (DTE), liquidity ratio (LIQ), dividend payout in % (DIVPAY), capital intensity (CAPINT), the natural log of sales (SIZE), R&D expenses as a percentage of operating revenue in (R&D).
Panel B in Table 4 presents the correlation matrix for the same set of variables used under panel A. The variables are not highly correlated with one another which suggest the presence of rather low collinearity between the variables. Hence, redundancy of variables is mitigated. It is worthwhile to note that the correlation coefficient between ownership concentration and Tobin’s Q is negative. This finding is in line with previous studies (see, e.g., De Miguel et. al., 2004; Demsetz and Villalonga, 2001; Konijn et. al., 2011). This result hints that the two variables tend to move in opposite direction, which is contradictory to the expectation described earlier where one would expect a higher ownership concentration to result in higher firm performance. Furthermore, it would be expected that sales and firm performance would move in the same direction. A firm whose sales experience growth should reflect positive firm performance. However, this relationship tends not to hold for the sample used in this study.
4.2 Empirical results
Table 5 presents the regression results using equation (1) in conjunction with the pooled OLS and fixed effects estimation techniques where Tobin’s Q is the dependent variable.
Table 5 Pooled OLS and Fixed Effects regression models for Tobin’s Q
DTE -0.11769*** -0.10557*** -0.07592*** -0.07707*** (0.023) (0.022) (0.021) (0.021) DIVPAY -0.00051*** -0.00052*** -0.00055*** -0.00053*** (0.000) (0.000) (0.000) (0.000) LIQ 0.04408* 0.04272* 0.04629** 0.04594** (0.017) (0.017) (0.015) (0.015) CONST 2.75130*** 2.04665*** 1.21037*** 0.96412*** (0.141) (0.154) (0.167) (0.172)
COUNTRY FE No Yes Yes Yes
SECTOR FE No No Yes Yes
TIME FE No No No Yes
N 5085 5085 5085 5085
adj. R2 0.105 0.152 0.207 0.217
F 25.00587 25.06534 28.70933 25.46685
Notes: Variables that are preceded by “L.” indicate a one-period lagged version of itself. Furthermore, the variables are: ownership concentration in % (O), the square of ownership concentration in % (O2), natural log of sales (SIZE), capital intensity (CAPINT), R&D intensity (R&D), debt-to-equity ratio (DTE), dividend payout in % (DIVPAY), liquidity ratio (LIQ), constant term (CONST). The dummy variables for shareholder identity are corporate ownership (CORP), financial company ownership (FIN), insurance company ownership (INSUR), institutional ownership (INST), family ownership (FAM), (general) public ownership (PUB). The dummy for bank ownership is omitted to prevent perfect multicollinearity and acts as the base category.
Standard errors in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001
The coefficients for the one-period lagged dummies of shareholder identity are insignificant, even at the 10% significance level except for corporate ownership (L.CORP) and public ownership (L.PUB). Corporate ownership is negative and significant in models 1 and 2 at the 5% level and at the 10% level in models 3 and 4. The coefficient for the one-period lagged dummy for public ownership is positive and significant at the 10% significance level in models 1 and 2 and becomes significant at the 5% level once sector fixed effects are accounted for, as illustrated in models 3 and 4. This result indicates that the identity of the owner is not relevant for firm performance unless it is a corporation or the general public. Corporations typically have the know-how and the experience on how to manage a firm and are likely to employ this in firms they own (Pedersen and Thomsen, 2003). The negative coefficient of corporate ownership on firm performance implies that on average, keeping all else constant, firms are expected to show lower performance if the largest shareholder is a corporation relative to a firm where the largest shareholder is a bank (since bank acts as the base category). This finding is in line with the results obtained by Thomsen and Pedersen (2000), who argue that corporate owners tend to put more emphasis on non-profit goals compared to financial institutions. Public ownership is different from the other owner identities in the sense that it denotes a large pool of individual investors that are not blockholders on their own but rather collectively. This makes it harder for the individual investors to directly influence managerial behaviour and firm performance. However, the individual investors could exert their indirect influence by buying or selling the shares in a company which in turn will affect the market capitalization of the firm. Therefore, a potential explanation for the positive coefficient for public ownership is that individual investors are likely to invest in firms whose performance is expected to increase and therefore driving up the market capitalization, which is then reflected in a higher value for Tobin’s Q (holding all else equal). The second null hypothesis is rejected at the 5% significance level for shareholders that are identified as the general public and at the 10% significance level for shareholders that are identified as a corporation. This finding is only partially in line with the study conducted by Thomsen and Pedersen (2000), who find a statistically significant relationship between shareholder identity and firm performance for large firms in Europe. The results for shareholder identity in this study are also similar to the results obtained by Gedajlovic and Shapiro (2002), who find a statistically significant relationship between firm performance and shareholder identity (decomposed in financial and nonfinancial firms) for Japanese firms.
Table 6 shows the results that are obtained from the model in equation (3). As discussed previously, this model has exactly the same independent variables as the model in equation (1), except for the dependent variable which is the firm’s 2-year monthly beta.
Table 6 Pooled OLS and Fixed Effects regression models for firm’s 2-year beta
Independent Variable (1) (2) (3) (4) POLS FE FE FE L.O -0.00102 -0.00072 -0.00072 -0.00082 (0.001) (0.001) (0.001) (0.001) L.O2 -0.00000 0.00000 0.00000 0.00000 (0.000) (0.000) (0.000) (0.000) L.CORP -0.01480 -0.01986 -0.01780 -0.01491 (0.019) (0.020) (0.019) (0.019) L.FIN 0.02693 0.01775 0.00918 0.00830 (0.029) (0.029) (0.029) (0.029) L.INSUR 0.07003 0.05745 0.05461 0.05635 (0.037) (0.037) (0.038) (0.037) L.INST -0.04135 -0.03645 -0.04222 -0.04307 (0.035) (0.035) (0.035) (0.034) L.FAM 0.10682 0.02714 0.02790 0.03283 (0.055) (0.057) (0.058) (0.058)
L.PUB -0.04902 -0.09006 -0.06448 -0.06070 (0.050) (0.052) (0.055) (0.056) SIZE 0.07038*** 0.07537*** 0.07342*** 0.07251*** (0.004) (0.004) (0.004) (0.004) CAPINT 0.00046 0.00075* 0.00081* 0.00091* (0.000) (0.000) (0.000) (0.000) R&D 0.00765** 0.00747** 0.00702* 0.00729** (0.003) (0.003) (0.003) (0.003) DTE -0.00784 -0.00552 -0.00190 -0.00083 (0.007) (0.007) (0.007) (0.007) DIVPAY 0.00007 0.00007 0.00007 0.00007 (0.000) (0.000) (0.000) (0.000) LIQ 0.00806 0.01019* 0.01047* 0.01082* (0.004) (0.005) (0.005) (0.005) CONST -0.22728*** -0.23708** -0.47760*** -0.41959*** (0.061) (0.081) (0.115) (0.117)
COUNTRY FE No Yes Yes Yes
SECTOR FE No No Yes Yes
TIME FE No No No Yes
N 4865 4865 4865 4865
adj. R2 0.070 0.100 0.111 0.116
F 29.30146 24.38160 17.91567 15.99445
Notes: Variables that are preceded by “L.” indicate a one-period lagged version of itself. Furthermore, the variables are: ownership concentration in % (O), the square of ownership concentration in % (O2), natural log of sales (SIZE), capital intensity (CAPINT), R&D intensity (R&D), debt-to-equity ratio (DTE), dividend payout in % (DIVPAY), liquidity ratio (LIQ), constant term (CONST). The dummy variables for shareholder identity are corporate ownership (CORP), financial company ownership (FIN), insurance company ownership (INSUR), institutional ownership (INST), family ownership (FAM), (general) public ownership (PUB). The dummy for bank ownership is omitted to prevent perfect multicollinearity and acts as the base category.
Standard errors in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001
The results in table 6 show that the variables of interest, namely ownership concentration and the dummies for shareholder identity are all statistically insignificant even at the 10% significance level. This result implies that ownership concentration and the identity of the largest shareholder are not relevant for firm systematic risk, even when country- sector- and time-fixed effects are accounted for. Therefore, the third and fourth hypotheses are not rejected at the conventional significance levels implying no relationship between ownership structure and firm systematic risk when proxied by 2-year monthly beta.
This appears to be somewhat contradictory, as one would expect larger more established firms to be less risky compared to similar firms of a smaller size. A possible explanation for this result could be that larger firms are able to bear more risk and therefore undergo riskier projects compared to smaller firms. Firms with higher values of capital intensity are less efficient in employing their assets to generate revenue compared to similar companies with lower values of capital intensity, holding all else equal. In turn, this is likely to positively contribute to firm systematic risk because a firm with more assets (especially when fixed assets make up a large part) is typically subject to more fixed costs which impacts the profitability of the firm and these type of costs often do not vary with the amount of revenue produced. This could imply the positive coefficient for capital intensity. The coefficient for R&D intensity is also positive and significant at the 5% significance level (except in model 3), implying that when firms spend more on R&D, their systematic risk increases. When a firm spends money on R&D to improve their product and services, it is not certain that the investment will pay off and hence making it more risky which is likely to be reflected in the systematic risk of the firm. Technological firms have on average higher R&D intensity but also a higher beta compared to firms in the utility sector. Finally, the positive coefficient for liquidity ratio indicates a positive relationship with firm systematic risk. This finding is counterintuitive since a firm with higher degree of liquidity is associated with a lower degree of insolvency. However, the liquidity ratio is not a perfect measure of firm’s liquidity in the sense that it not only includes cash and cash equivalents, which are free, but also inventory and accounts payables which are far from being risk-free (Riahi-Belkaoui, 1998). Therefore, a higher liquidity ratio could positively affect firm systematic risk.
4.3 Robustness analysis
This section provides further analysis to establish the robustness of the results presented above. The dependent variable in equation (1) becomes the return on assets (ROA) and the dependent variable in equation (3) becomes the 5-year monthly beta. The robustness analysis is performed using the full fixed-effects model. That is, including country- sector- and time-fixed effects. The results are presented in table 1B in appendix B to this paper. Since Tobin’s Q is not a perfect proxy for firm performance, it is sensible to repeat the regression analysis using a different proxy for firm performance. ROA is chosen as an alternative proxy for firm performance because, unlike ROE, it considers the liabilities of the firm and therefore measures firm’s performance based on the entire financing structure. This approach is in line with Himmelberg et. al., (1999). Ideally, the same approach would be followed for firm systematic risk. However, it more difficult to find a suitable proxy for firm systematic risk. Therefore, as a robustness check, 5-year monthly beta is used as a proxy for firm systematic risk.
for the one-period lagged dummy variables for shareholder identity remain insignificant. Public ownership (L.PUB) is now insignificant even at the 10% significance level. Corporate ownership (L.CORP) is now statistically significant at the 5% level. This is in contrast with the results obtained by Thomsen and Pedersen (2000), when ROA is used as a proxy for firm performance. Moreover, the coefficient for one-period lagged dummy variable for financial corporation ownership (L.FIN) is now statistically significant at the 10% level implying that a lower firm performance is associated with financial company ownership compared to bank ownership. As for the control variables, capital intensity (CAPINT) now becomes only statistically significant at the 10% level, R&D intensity (R&D) becomes statistically significant at the 5% level and liquidity (LIQ) becomes only statistically significant at the 10% level. Furthermore, the (adjusted) R squared value (0.140) is now lower implying that the variables in the model explain an even smaller portion of the variation in firm performance when measured as the ROA instead of Tobin’s Q. In general, this shows that ownership concentration and shareholder identity are relevant for firm performance, however these variables are not the only relevant factors in explaining the variation in firm performance.
the coefficients on ownership concentration become more statistically significant. Furthermore, the adjusted R square is now slightly higher although still implying that a large portion in the variability of firm systematic risk remains unexplained by the variables used in the model.
5. Conclusion
This study examines the relationship between ownership structure, firm performance, and firm systematic risk using a sample of public firms incorporated in Europe over a period of nine years. By using the fixed effects estimation technique, this study finds a statistically significant (non-linear) relationship between firm performance, which is proxied by Tobin’s Q, and ownership structure which is divided into ownership concentration and the identity of the largest shareholder. This study also finds that the relationship between ownership concentration and firm performance is characterized by a U-shape, implying that firm performance decreases as ownership concentration increases for lower values of ownership concentration and increases as ownership concentration increases for higher values of ownership concentration. This finding implies two things. Firstly, as the stake of the largest shareholder increases, managers could be induced to take actions that will negatively affect firm performance to discourage the largest shareholder from acquiring a larger stake in the firm. Secondly, once the shareholder manages to acquire a significant stake (moving past the inflection point), he or she will be able to exert influence on managerial decision making such that firm performance is positively affected. This relationship between ownership concentration and firm performance also holds when firm performance is proxied by the ROA. Additionally, ownership identity is only relevant in cases where the largest shareholder is a corporation or the general public. Corporations have the know-how and experience that they can employ in firms in which they are the largest shareholder or utilize certain synergies, which in turn is likely to affect firm performance. Corporate ownership becomes more significant when firm performance is proxied by ROA. The public ownership is distinct from other owner identities in the sense that it is an aggregate block of individual investors and hence does not represent a single entity or a person. A possible explanation for the relevance of public ownership for firm performance is that individual investors can buy or sell shares in the company and therefore indirectly reflect their opinion on managerial competence, this in turn is reflected in the market capitalization of the firm. However, public ownership becomes insignificant when ROA is used as a proxy for firm performance.
with caution as the explanatory power of the variables used in this study only explains a small fraction of the variation in firm performance and systematic risk.
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Appendix A: The Hausman specification test
Table 1A Results from the Hausman (1978) specification test
Equation 1 Coef.
Chi-square test value 207.009
P-value .000
Equation 3 Coef.
Chi-square test value 30.057
Appendix B: Robustness analysis
Table 1B Regression results for ROA and 5-year monthly beta of the firm
Independent Variable (1) (2)
ROA 5-yr Beta
L.O -0.07541*** -0.00277** (0.015) (0.001) L.O2 0.00098*** 0.00003* (0.000) (0.000) L.CORP -0.72968** -0.00439 (0.234) (0.015) L.FIN -0.80257* 0.02003 (0.347) (0.022) L.INSUR 0.79807 0.04859 (0.505) (0.030) L.INST -0.83713 -0.04044 (0.458) (0.027) L.FAM -0.17033 0.06237 (0.714) (0.043) L.PUB 1.26933 -0.00006 (0.837) (0.045) SIZE -0.25865*** 0.07258*** (0.063) (0.004) CAPINT -0.02561* 0.00089** (0.010) (0.000) R&D 0.11616** 0.00183 (0.037) (0.002) DTE -0.71258*** -0.00371 (0.144) (0.007) DIVPAY -0.00962*** -0.00002 (0.001) (0.000) LIQ 0.25413* 0.00962** (0.109) (0.004) CONST 10.05626*** -0.12074 (1.539) (0.104)
COUNTRY FE Yes Yes
SECTOR FE Yes Yes
TIME FE Yes Yes
N 5102 3963
adj. R2 0.140 0.213
F 21.57964 21.42950
dummy variables for shareholder identity are corporate ownership (CORP), financial company ownership (FIN), insurance company ownership (INSUR), institutional ownership (INST), family ownership (FAM), (general) public ownership (PUB). The dummy for bank ownership is omitted to prevent perfect multicollinearity and acts as the base category.
