Country Valuation Differences?
Evidence from the United States, Japan, Germany and the United Kingdom.
By JORIS B. NIJBOER
S1346431
Grade: 8
University of Groningen
Faculty of Economics and Business
MSc Business Administration – Finance
Thesis Supervisor: Dr. Ing. N. Brunia
Do Fundamentals Explain
Cross-Country Valuation Differences?
Evidence from the United States, Japan, Germany and the United
Kingdom.
JORIS B. NIJBOER
∗ABSTRACT
This study analyses the international differences in valuation multiples across countries. Valuation multiples both at the equity and enterprise level are used to test whether fundamentals explain international differences in valuation multiples. In fully integrated financial markets these differences wouldn’t exist because in such a market fundamentals explain the differences in the valuation of firms and there are no country-specific effects. The major findings of this study are that differences in valuation multiples are present and that these differences are not fully explained by fundamentals and thus country-specific effects are existent. The presence of these country-specific effects indicates that markets are not fully integrated. The results are robust in the sense that country-specific effects are present within most industries and in most of the years.
Keywords: Valuation Multiples, Integrated Financial Markets, Fundamentals, Country-specific effects.
Throughout the last semesters of my study at the University of Groningen, I studied the Master of Science in Business Administration with the specialization in Finance. This thesis is the final part of my Master. It wasn’t always easy to get to the point where I am right now. After months, many alterations, and periods of blood, sweat and tears, I finally finished my thesis.
With this preface, I would like to take the opportunity to thank a number of people for their help and support. In the first place my mentor Dr. Ing. Nanne Brunia for his constructive criticism and the interesting, nice and useful moments during our meetings. Furthermore, I am very grateful to my parents Hans and Jolande and my sister and brothers Karlijn, Tristan and Elmar, who always where interested in me and my study and supported me in finishing my Master.
Finally I would like to thank my girlfriend Marlou for her love and care. It was not always easy to live with me during the stressful periods of my study in general and this final thesis in particular, but you always have supported me.
Raalte, 25 June 2009
PREFACE………...……… III.
TABLE OF CONTENTS………... IV.
I. INTRODUCTION………. 1.
II. VALUATION MULTIPLES AND FUNDAMENTALS………... 3.
II.I Valuation multiples………. 5.
II.II Fundamental factors……… .8.
III. DATA……….……….13.
IV. METHODOLOGY………. 19.
IV.I . Tests for cross-country differences in valuation multiples………..………... 19.
IV.II. Estimation of the equity and enterprise value model...………...…… 19.
V. EMPIRICAL RESULTS……… 22.
V.I . Test results for cross-country differences in valuation multiples………... 22.
V.II. Can fundamentals explain differences in valuation multiples?………..…………... 25.
VI. CONCLUSIONS………..……….. 29.
REFERENCES………... 31.
APPENDIX 1……….. 34.
APPENDIX 2……….. 35.
I. INTRODUCTION
This paper analyzes the valuation multiples of firms from the United States, Japan, Germany and the United Kingdom to test whether there are international differences in the valuation of firms. In fully integrated financial markets these differences wouldn’t exist because financial markets are assumed to be internationally perfect. According to Karolyi and Stulz (2002) in internationally perfect financial markets identical firms have the same value regardless at which market the firm is listed. Markets where assets have the same price regardless of where they are traded are said to be integrated, while markets where the price of an asset depends on where it is traded are said to be segmented. Thus in a fully integrated capital market fundamentals explain differences in the valuation of firms and there are no country-specific effects in valuation. The assumption of fully integrated financial markets and the assumption that the value of the firm is driven by fundamentals is one of the basic assumptions for valuation (Koller et al., 2005). However, empirical studies with respect to cross-listings by Foester and Karolyi (1999) and Koedijk and Van Dijk (2004) and studies with respect to differences in equity premiums by Jorion and Goetzmann (1998), among others, indicate that the concept of fully integrated capital markets can be questioned. The result might be that identical firms not have identical values in different countries. To examine whether identical firms have identical values and fundamentals explain differences in valuation multiples an approach similar to the ones of King and Segal (2003 and 2008) is used in this study. Where King and Segal (2003 and 2008) focus only on the US and Canada, this study focus on the US, Japan, Germany and the UK because these countries have by far the largest capital markets in the world but are also quite different (Choi and Meek, 2005)
Although the literature with respect to cross-listings and the equity premium indicate that financial markets are not fully integrated, Jordan and Majnoni (2002) find that differences between countries become smaller and financial markets become more integrated. Mittoo (2003) finds that the level of integration is increasing over time. Given these findings the assumptions of integrated financial markets by Koller et al. (2005), among others, is a reasonable assumption because the valuation of a company is future orientated.
firms (Damodaran, 2002). When differences in valuation multiples are not fully explained by fundamentals a multiple analysis, where a firm’s multiples are compared to those of comparable firms, overlooks the country-specific effects and incorporating these country-specific effects might result in more accurate valuations.
Because theoretically in an internationally perfect market identical firms have identical values the first question that rises is whether there are differences in valuation multiples across counties. When these differences are present the question rises whether those differences can be explained by fundamentals. To test whether differences in valuation multiples across countries are explained by fundamentals two models are developed. One model focuses on equity multiples, the other model focuses enterprise value multiples. The model incorporates fundamental factors as explanatory variables. The choice of the variables is based on valuation literature of Liu et al. (2002), King and Segal (2008) and Damodoran (2002) where these variables are identified as value drivers of the firm. The major findings of this study are that differences in valuation multiples are present and that these differences are not fully explained by fundamentals and thus country-specific effects are existent. The presence of these country-specific effects indicates that markets are not fully integrated. Firms from the United States are valued at a premium with respect to firms from the other countries. Firms from Japan are valued at a premium with respect to firms from Germany and the United Kingdom in spite of the fact that Japanese firms are less profitable. The presence of differences in the valuation between firms from the United States and Japan and between firms from Germany and the United Kingdom is less obvious. Although industry membership explain some of the international differences in valuation multiples, these results are robust in the sense that country-specific effects are present within most industries. Furthermore, country-specific effects are present in most of the years and in only a few years fundamentals explain differences in valuation multiples. Although the country-specific effects are time varying, there is no recognizable pattern that the extent of these effects is increasing or decreasing over time.
II. VALUATION MULTIPLES AND FUNDAMENTALS
To test whether differences in valuation multiples across countries are explained by fundamentals and whether country-specific effects exist two models are developed. The first model is the equity value model and the second is the enterprise value model. Valuation multiples are used as dependent variables in the models and the explanatory variables are fundamental factors and a series of dummy variables. The models are specified in (1) and (2) and the definitions of the variables with their expected signs are presented in table I.
The equity model is specified as:
The enterprise model is specified as:
Section II.I is about the valuation multiples that are incorporated in the model. Section II.II is about e fundamentals that are identified in the asset-pricing literature and that might explain differences valuation multiples. jt it it jt it it
ROE
DFX
GROWTH
PB
RF
VM
=
α
+
β
1+
β
2+
β
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β
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β
7 (1) j j j i s i it itjt
SIZE
LEV
IND
YEAR
SHARPE
β
β
δ
λ
β
β
+
+
+
+
+
+
∑
∑
= = 2007 1992 8 1 10 9 8 jt it it jt it itROA
DFX
GROWTH
PB
RF
VM
1 2 5 6 7 it iCTRY
+
ε
11α β
+
+
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+
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=
j j i i it itjt
SIZE
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IND
YEAR
CTR
SHARPE
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+
+
+
+
+
8 9 10∑
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2007 11 (2) it i j sY
+
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= =1 1992The rationale for including these variables in the model will be discussed in the next sections.
Variables used in Empirical Analysis
e cont d in prise model and their hypothesized signs. ere + is significantly p e, - is sign
Hypothes
TABLE I
This tabl ains the variables use both the equity and enter
Wh ositiv ificantly negative, 0 is insignificant, +/- is significant; either positive or negative.
le Variable ized sign Description of variab
it
VM
N/A er equity or enterprise price earnings and ity and enterprise model respectively ssetsjt - The change in the trade weighted foreign exchange rate
it
- The risk free rate
jt ratio Equity m - Enterprise m del: + ies j
EAR
+/- A series of year dummies 0 Country dummy variable The valuation multiple, eith market to book in the equ itROE
+ The return on equity itROA
+ The return on aDFX
GROWTH
+ Sales growth
it
PB
+ The plowback ratiojt
RF
SHARPE
+ The Sharpe itSIZE
+ Firm sizeit
LEV
odel:o The leverage ratio i
IND
+/- A series of industry dummY
i
CTRY
The country dummy makes it able to test whether firms from a particular country are valued at a discount or premium compared to the other countries. The hypothesis that all firms, controlled for fundamental factors, are equally valued across countries is tested by examining whether the sign of the country dummy is statistically significant. When fundamentals explain the differences in valuation multiples, the coefficient of the country dummy is insignificant. With a significant positive (negative) coefficient a particular country is valued higher (lower) than the other countries in the sample and a country-specific effect is present.
ith their extended dataset of y confirm their earlier results that Canadian firms are traded at a
quity markets remain segmented.
use of multiples allows a comparison of the value of firms that are denominated in different currencies.
lly high current earnings that on average will fall in the future are indicated by low PE multiples. On the other
en a discount across a range of valuation measures, despite the fact that Canadian firms have lower cost of equity and are more profitable. In their (2008) paper they go further and also examine Canadian cross-listings and they focus more on financial market integration. W
118,000 firm-year observations the discount and that US and Canadian e
II.I Valuation Multiples
A range of valuation multiples are included as dependent variables in the models. The equity value model uses equity multiples and the enterprise model uses enterprise multiples. Multiples are frequently misunderstood or misapplied because of the fact that when one uses average multiples from a set of comparable firms to value a particular firm, one ignores the possible differences in fundamental factors such as growth rates, profitability and capital structure (Koller et al., 2005). However, multiples can be very useful in valuation if they are applied correctly and when they are corrected for differences in fundamental factors such as in the models (1) and (2). In addition, the
This is useful because in this study firms from countries with different currencies are compared. One of the most well known valuation multiples is the price earnings (PE) ratio. The PE ratio is a widely used measure for the relative valuation of a firm’s equity. Both in cross-section and over time the value of stocks are evaluated by their PE ratios like in the models used in this study. Additional to this interpretation, the PE ratio is interpreted as an earnings growth indicator Penman (1996), as a risk measure (Ball, 1978), and as an earnings capitalization rate (Graham et al., 1962). Beaver and Morse (1978) show that the PE multiple indicates mean reversion of earnings (also known as the Molodovsky effect). Mean reversion means that firms that exhibit above average profitability in one period can be expected to experience a fall in earnings in the future, as new entrants are attracted into the business and profit margins are reduced through greater competition. Over time competition will erode all returns in excess of the opportunity cost of capital with no firm being able to sustain above-average earnings growth (King and Segal, 2003). According to Penman (1996) these unusua
hand, high PE multiples can indicate depressed earnings that probably will rise in the future.
ons, the separation into groups by Danielson and Dowdell (2001) also have useful interpretations with respect to firm characteristics in a particular country. For “value” and “glamour” stocks (Lakonishok et al, 1994). According to Bodie et al. (1989) analysts interpret the MB ratio as a margin of safety, because the comparison of price to liquidation value. The question arises whether firms that trade at a high PE ratio also have a high MB ratio. The PE ratio uses a number from the income statement and the MB uses a number from the balance sheet. According to Penman (1996) these numbers are related. Each ratio is a transformation of the other by the ratio of two accounting numbers: the (approximate) book rate of return on equity. He finds that PE ratios indicate future growth in earnings which is positively correlated with expected future return on equity and negatively related to the current return on equity and that the MB ratio only reflects expected future return on equity. The economic interpretation of Penman (1996) is that the only the MB ratio is an appropriate indicator of future earnings growth because the MB ratio is unaffected by current profitability. Furthermore, Penman (1996) argues that because a given level of the PE ratio can be associated with alternative combinations of current and expected return on equity, current return on equity is not a good indicator of the PE ratio. Danielson and Dowdell (2001) also focus on the relation between PE and MB multiples and develop a model that can be used to gain insight about the entire set of expectations of a firm: the return-stages model. This model writes a firm’s stock price as a function of three levels of future return on equity. With this model they show how the MB and PE ratio can help to answer three questions: (i) Whether investors expect the firm’s book rate of return on equity to increase or decrease in the future, (ii) whether the firm is expected to earn a future return on equity in excess of the cost of equity and (iii) whether the firm is expected to earn excess returns from its assets in place, new investments or both. To answer these questions they separate firms into four groups based on their multiples: growth firms (high PE; high MB), mature firms (low PE; high MB), turnaround firms (high PE; low MB) and declining firms (low PE; low MB). Additional to the interpretation of this study where firms with high PE and MB ratios are interpreted as firms with high valuati
example a country where PE and MB ratios are high, this can be interpreted as a country where firms have a high profitable growth.
its operating performance compares to the expectations defined by their model. According to the studies by
k of financial distress increases companies are more likely to lose customers, employees and suppliers. Finally, when the firm’s debt is not default free and firms take future operating performance (or excess returns in their specification) tend to follow the patterns described by the return-stages model and that a firm’s stock return depends on how
Penman (1996) and Danielson and Dowdell (2001) differences in PE and MB are justified by differences in return on equity and the expected future operating performance of firms.
h the EV/EBITDA multiple it is generally assumed that debt that has no verifiable market value is worth its book value. Given the unavailability of data with respect to the market
be approximated by the total assets less operating liabilities, which is by definition equal to the book rmore, this proxy is consistent with the measures of income used in this analysis.
perturbations of these industries. Another finding by Roll (1992) is that most of the stock markets are on more debt, the cost of debt will increase because investments of debt holders become more risky (Grinblatt and Titman, 2002).
Like the PE multiple even the EV/EBITA multiple might include some non-operating items. The EBITA’s reported in company’s accounts are operating profits from an accounting perspective. To obtain an economic perspective of the firm, Koller et al. (2005) recommend several adjustments to the accounting data. These adjustments require detailed information from the notes of the companies’ annual reports. Given the scale of this research it is not feasible to make these adjustments. Furthermore, company account databases do not include information about EBITA and the use of EBITA’s is impossible and so the enterprise to EBITDA multiple is incorporated in the model as enterprise variant of the equity PE multiple. EBTIDA multiples are widely used by analysts because depreciation is a non-cash item that reflects sunk costs and not future investments. According to Koller et al. (2005) EBITDA might be superior to EBITA because valuation is based on future cash flows but not in all cases due to the reinvestment problem that is not recognized in the EBITDA multiple. Wit
value of debt, following Koller et al. (2005) the book value of debt is used as a proxy for the market value.
To overcome the shortcomings with respect to capital structure of the equity MB ratio a variant of Tobin’s Q ratio will be used as enterprise alternative to the traditional MB multiple. The ratio is defined as the ratio of the market value of a firm to the replacement cost of its assets. Due to the availability of data in the financial statements of firms it is not feasible to calculate the replacement cost of the firm’s assets. Following Chung and Pruitt (2004) the replacement costs of the assets will
value of debt and equity. Furthe
II.II Fundamental Factors
odel might be explained by industry membership, country-specific effects might also be present
sized sign of these profitability is positive, because a higher profitability ould be associated with a higher valuation multiple. However, a negative sign indicates the mean-influenced by exchange rates, which will be discussed later with respect to exchange rate exposure. Because in addition to the fundamental factors industry membership is identified as an important factor in explaining valuation differences, the model incorporates a series of industry membership dummies. The hypothesized significant coefficients of these dummies indicate that industry membership explains part of the differences in valuation multiples. Another point of interest is whether within an industry fundamentals explain the differences in valuation multiples and whether there are country-specific effects. Where some of the differences in multiples in the aggregated m
within industries. To test for differences within industries the models will be estimated by industry.
Expected future profitability is an important factor in justifying differences in valuation. The return on assets (ROA) is a widely used measure of profitability, another measure of profitability focuses on the return on shareholders’ equity (ROE). Both profitability measures are incorporated in the model. With respect to the enterprise model the ROA is included and in the equity model ROE is used. Like with the equity multiples, also the return on equity is misleading when assessing the operating performance of the firms. Although expected future profitability is relevant from a valuation perspective, this is not feasible because the databases do not include relevant data. Following Damodaran (Website) and King and Segal (2003 and 2008) historical data is used as proxy for future measures. The hypothe
sh
reversion of earnings.
s. he hypothesized negative relation between the valuation multiple and the change in the foreign Firms with foreign operations are subject to fluctuations in exchange rates which are reflected in the value of these firms. Following King and Segal (2003) the year-over-year change in the foreign exchange rate is included in the model because it might possibly affect the relative valuation of firm T
exchange rate indicates that an appreciation of the country’s currency lowers the firm’s valuation.
he expected future growth rate in sales approximated by historical rates and thus might be imperfect and so the plowback ratio as other
st of equity is a function f a risk-free rate, the covariance of the stock and the market portfolio (the beta), and the spread of positive because it is assumed that firms only retain earnings when they have internal value creating investment opportunities. Because in the equity model three related variables are included: growth, the plowback ratio and the return on equity, the question rises whether these variables should be included together in one model. Following the suggestions of Damodaran (2002) these variables are included together in the equity model. The reason is that the inclusion of more variables for one effect can be useful because some proxies may be imperfect. T
is
proxy for future growth can have added power in the model.
The majority of financial models for firm valuation rely on discounting future cash flows that accrue to shareholders in the form of capital gains or dividends at some discount rate. These future cash flows depend on factors that are specific to a particular company: profitability, growth, and payout policy. These cash flows are discounted at a rate that reflects the riskiness of these cash flows: the cost of capital. The result is that systematic differences in cash flows or the cost of equity between firms justify different valuations. The cost of equity can be estimated by different methods. The most common method is the Capital Asset Pricing Model (CAPM) initially developed by Sharpe (1964) and Lintner (1965). The CAPM is a single factor pricing model, where the co
o
the return of the market portfolio over the risk free rate (the risk premium).
s at valuation of the firm increases when the stock market rises. However, a negative sign indicates
e firm because it makes equity investments riskier. In the equity models the hypothesized sign is negative because higher leverage might cause a higher cost of equity and thus lower equity multiples.
portfolio will lead to different values of beta for a stock. Differences between the return and the volatility of the benchmark portfolio might result in systematic differences between firms traded at different stock markets which wouldn’t exist in a world with fully integrated capital markets (Bekaert and Harvey, 1995). To capture the differences in risk-premiums across countries the Sharpe ratio is included in the model. According to King and Segal (2003), the Sharpe ratio also captures any premiums in valuation that might be the results of elements from the field of behavioral finance such as the irrational exuberance in the late nineties and market sentiment. The Sharpe captures the resulting effects that a rise in the overall stock market theoretically should lead to a rise in the valuation of individual companies. The hypothesized sign for this variable is positive and indicate th
that the higher risk premium result in a higher cost of capital and thus lowers the value of the firm.
III. DATA
The annual reports and market valuation data of the firms in the sample are obtained from DATASTREAM. All companies listed at the 31th of December 2007 at one of the major indices of the countries are included in the sample. For the United States all constituent firms of the S&P 500 index are included. The constituent firms of the Nikkei 500 for Japan, the DAX 50 for Germany and FTSE 350 for the United Kingdom are included. The sample size is limited to the period 1992-2007 due to the availability of all relevant company account data in DATASTREAM and the initial sample includes 1400 firms. Following King and Segal (2008) financial firms are excluded because their balance sheet and income statement items are not comparable to other firms. All firm-year observations with negative or zero assets and negative book value of equity or an incomplete set of necessary data are excluded. In the variables used in the empirical analysis outliers are present. Following the suggestions by Liu et al. (2002) and a similar study by King and Segal (2008), firm-year observations of all included variables of the model out of the 2.5%-97.5% range are removed from the sample, to reduce the impact of these outliers on the final results. For instance, the PE multiple might explode to infinity due to small earnings and thus have substantial impact on the average multiple of the sample. The outliers that are removed are distributed across all countries and industries in the sample.
The final sample, with a complete set of data, consists of in total 13,102 firm-year observations and includes 1096 firms. For all firms in the sample industry membership codes (ICBIC) are obtained from DATASTREAM. The distribution of included firms across industries and countries is presented in Table II.
TABLE II
Distribution of firms across industries and countries
This table contains the number of firms that are member of a particular industry in a particular country.
ICBIC industry classification
United States Japan Germany United Kingdom Total
Oil and Gas 34 8 0 21 63
Basic Materials 24 67 8 20 119 Industrials 80 127 7 66 280 Consumer Goods 59 90 9 27 185 Health Care 48 17 2 9 76 Consumer Services 65 51 4 62 182 Utilities 34 19 2 8 63
Technology and Telecom 62 44 4 18 128
Daily index returns of the S&P 500, the Nikkei 500, the FTSE 350 and the DAX 50 index and the yields of three month government securities for each country are obtained from DATASTREAM. Exchange rate information is gathered from DATASTREAM. To calculate the year-over-year change in the foreign exchange rate a trade weighted US Dollar exchange rate index is used for US firms and for the other countries trade weighted foreign exchange rates denominated in US Dollars are used. To transform variables in Dollars for each country an end of the year exchange rate is obtained.
For each firm-year a series of valuation multiples and company and market specific variables are calculated following model (1) and (2). The definitions of these variables are presented in Table III.
TABLE III
Definition of variables
aThis table contains the variables used in the empirical analysis, their abbreviations and their definitions
Variable Definition
Equity price earnings share price/earnings per share
Equity market to book market value of equity/book value of equity
Enterprise price earnings enterprise value/EBITDA
Enterprise market to book enterprise value/(total assets-operating liabilities
Return on equity net income/book value of equity
Return on assets (net income + interest-interest tax shield)/(total assets-operating liabilities)
Change in exchange rate yearly change in the trade weighted foreign exchange rate.
Growth two year-average growth rate in sales
Plowback ratio 1-((dividends paid + share repurchases)/net income)
Risk free rate Yield of a three month government security
Sharpe ratio (return on market index-risk free rate)/standard deviation
Firm size log(total assets) for US firms
log(total assets * end of the year FX rate) for the other firms
Financial leverage total debt/market value of equity
a I
n DATASTREAM total debt includes operating liabilities, the debt used in this analysis is net of operating liabilities.
Due to the nature and the large scale of this study a trailing PE ratio will incorporated in the model. A shortcoming of the trailing PE ratio is the fact that this multiple is not forward looking. Forward looking multiples are consistent with the principles of valuation, not backward-looking multiples based on historical performance. Empirical evidence of Liu et al. (2002) shows that forward looking multiples are more accurate predictors of value. For a large sample of companies in the United States they find that measured versus their industry, the historical earnings to price – the inverse of the PE- multiple of a company had 1.6 times the standard deviation of one year forward PE. They also find that forward looking multiples led to better pricing accuracy. The median pricing error was 23 percent for historical multiples and 18 percent for one year forecasted earnings. The pricing error reduced even further by the use of a two year earnings forecast. A study by Kim and Ritter (1999) find that multiples based on forecasted earnings outperformed those based on historical earnings to predict the price of 142 IPO’s. The pricing error reduced from 55 percent to 43.7 percent to 28.5 percent when using historical, one-year-forward and two-year-forward multiples. Although empirical evidence shows that forward looking multiples yield better accuracy than historical multiples, the accuracy of analyst forecast is questioned in O’Brien (1988) and Crichfield et al. (1978). Because for the majority of firms in this large-scale study forecasted earnings are unavailable the trailing PE multiples will be incorporated in the model. When forward looking data is unavailable and historical data has to be used Koller et al. (2005) recommend to use recent data and eliminate one-time events.
Table IV presents descriptive statistics of the valuation multiples. The skewness is positive for all the multiples. This indicates that the distribution has a long right tail. The kurtosis measure shows that the distribution of the most multiples is leptokurtic relative to the normal distribution. The Jarque-Bera is a test statistic for testing whether the multiples are normally distributed. For all the multiples a significant Jarque-Bera statistic leads to reject the null hypothesis of a normal distribution.
TABLE IV
Descriptive statistics of valuation multiples
This table contains descriptive statistics for the valuation multiples, the dependent variables in the model, for the United States, Japan, Germany and the United Kingdom. The multiples are over the 1992-2007 period
and the table includes ratios on both equity and enterprise level. Mean Median Maximum Minimum
Standard
Deviation Skewness Kurtosis
Jarque- Bera United States Equity PE 22.01 18.86 83.12 2.57 12.41 1.26 3.65 1270* Equity MB 6.56 3.96 75.01 0.49 6.23 1.2 3.25 1319* Enterprise PE 11.09 9.78 50.03 0.69 5.58 1.82 2.98 1229* Enterprise MB 2.22 1.70 15.76 0.53 1.55 1.25 2.54 1212* Japan Equity PE 29.50 25.25 86.99 2.58 16.56 1.17 2.74 1039* Equity MB 2.21 1.79 49.1 0.25 2.09 1.32 3.24. 1298* Enterprise PE 10.40 9.20 95.20 0.22 5.41 1.65 3.56 2101* Enterprise MB 1.40 1.49 19.63 0.59 0.71 1.24 3.24 1162* Germany Equity PE 16.44 14.36 66.42 2.82 9.26 1.93 3.52 192* Equity MB 4.36 2.33 50.57 0.64 6.02 1.52 4.21 136* Enterprise PE 8.06 5.004 43.71 0.84 8.36 2.35 3.65 286* Enterprise MB 1.45 1.15 7.47 0.87 1.06 1.98 3.86 208* United Kingdom Equity PE 17.36 15.25 78.01 2.29 10.00 1.24 2.98 599* Equity MB 4.59 2.95 73.49 0.14 7.09 1.88 3.24 1384* Enterprise PE 9.22 8.24 42.05 0.37 4.99 1.91 3.32 1433* Enterprise MB 1.78 1.22 10.56 0.45 1.01 1.57 3.41 978* *significant at the 0.01 level; ** significant at the 0.05 level; ***significant at the 0.10 level
The descriptive statistics of the fundamental factors that are incorporated in the models are presented in table V. Table V shows that the Jarque-Bera statistic is significant for all the variables that are used in the model. This indicates that the distribution of these variables is not normal. This can result in problems with testing procedures which will be discussed in the methodology section.
TABLE V
Descriptive statistics of fundamental factors
This table contains descriptive statistics for fundamental factors that are used as explanatory variables in the model. The statistics are for the United States, Japan, Germany and the United Kingdom over the 1992-2007
period. Mean Median Maximum Minimum
Standard
Deviation Skewness Kurtosis
IV. METHODOLOGY
In this section the methodology that is used to examine the valuation multiples of firms from the United States, Japan, Germany and the United Kingdom is discussed. In the first part the methodology with respect to the univariate test for differences in median valuation multiples is discussed. The second part deals with the methodology with respect to the estimation of the equity and enterprise value models.
IV.I. Tests for differences valuation multiples
To test whether firms in the four countries are valued differently for each set of firms median equity and enterprise MB and PE ratios are calculated and the differences between the multiples of one individual country and the three other countries are computed. For instance, the difference in the median equity MB of Japanese firms is subtracted from the median MB of the firms from the other countries to determine the differences in the equity MB multiples.
To test the significance of these differences in valuation multiples non-parametric tests are used because the descriptive statistics show that the distribution of the variables is not normal. The non-parametric test is used because this test does not rely on the assumption of normality when testing the null hypothesis of no differences in valuation multiples. The non-parametric hypothesis test chosen is the Mann-Whitney U test for two independent samples: the country of interest versus the other countries. The Mann-Whitney U tests the null hypothesis that the medians of two samples are the same. The test statistic U approaches the normal distribution quite rapidly as the number of sample observations increases. Because the sample size is large enough (n>10), following Newbold et al. (2003), the test will be approximated by the normal distribution.
The same test is used to test for differences in the fundamentals, because these factors can possibly explain differences in the valuation multiples.
IV.II. Estimation of the equity and enterprise value model.
by considering the interaction of the explanatory variables. Where the univariate tests only reveal that the multiples and fundamental factors are different, the regression model relates the differences in valuation multiples directly to t fundamental variables. When in addition to the fundamental variables the country dummies also have significant coefficients, it can be concluded that other factors than fundamentals explain the differences in valuation multiples and that a country-specific effect is present.
The equity and enterprise value models are estimated by Ordinary Least Squares (OLS). Because the data contains both time-series as well as cross-sectional dimensions a panel estimation method is used. A panel estimation method differs from normal time-series and cross-sectional regression because it brings both dimensions together. Following King and Segal (2008) the regressions are run with random effects. The regressions are run with random effects because a number of variables are time invariant such as industry membership and the nationality of firms and consequently a fixed effects model cannot be estimated. To check whether valuation differences are time-varying the models are also estimated for each year. To deal with the differences within industries, the models are estimated at the industry level.
V. EMPIRICAL RESULTS
In the first part of this section the results of the tests for differences in valuation multiples are discussed. The second part deals with the estimation results of the equity and enterprise value models.
V.I. Test results for cross-country differences in valuation multiples.
In table VI the results of the non-parametric test for differences in valuation multiples are presented and in table VII the results of the test for differences in fundamentals factors are shown. Table VI shows that the results of the enterprise multiples are unambiguous. For all countries the sign is equal for the enterprise PE and enterprise MB ratios. This not the case for the equity multiples except for Germany. Table VI shows that US firms have significantly higher enterprise multiples than the other countries. These higher multiples can be explained by differences in fundamentals: Consistent with the literature, the results in table VII show that US firms are significantly more profitable, have a higher growth, retain more earnings, have a higher leverage and are larger than the median firm from the other countries. Furthermore, the risk-free rate is lower in the US. The differences in the signs of equity multiples cannot be explained by fundamentals. The higher equity MB multiple indicates according to Penman (1996) expected future return on equity, which cannot be explained by the negative sign of the equity PE ratio. The equity PE ratio is related to both current return and expected return on equity. Given the higher current return on equity of US firms and the expected future return on equity as indicated by the equity MB ratio, fundamentals cannot explain the lower equity PE ratio.
TABLE VI
Differences in valuation multiples over the 1992-2007 period
This table contains the differences in valuation multiples between a particular country and the other countries, over the 1992-2007 period. The statistical significance is based on the normal approximation of the Mann-
Whitney U test.
United States Japan Germany United Kingdom
Equity PE -1.11* 7.78* -5.84* -6.93*
MB 1.89* -2.01* -0.19* 0.51*
Enterprise PE 1.20* 0.59* -3.81* -0.74*
MB 0.35* 0.05* -0.30* -0.52*
*significant at the 0.01 level; ** significant at the 0.05 level; ***significant at the 0.10 level
the other countries. Furthermore, given the low current return on equity and the high equity PE ratios for Japan, the lower equity MB ratios cannot be explained by the findings of Penman (1996). The return-stages model of Danielson and Dowdell (2001) also cannot explain the Japanese equity multiples. The higher PE multiple and the lower MB multiple indicate that the median Japanese firm is a turnaround firm but this is in contrast with the higher growth of Japanese firms.
TABLE VII
Differences in fundamental factors over the 1992-2007 period.
This table contains the differences in, market and country specific factors between a particular country and the other countries over the 1992-2007 period. The statistical significance is based on the normal approximation of
the Mann-Whitney U test.
United States Japan Germany United Kingdom
ROE 0.10* -0.13* 0.05* 0.07* ROA 0.03* -0.07* 0.02* 0.03* DFX 0.01 -0.01 0.00 0.01 GROWTH 0.05* 0.06* -0.01*** -0.04* PB 0.07* 0.05* -0.10* -0.16* RF -0.01* 0.04* 0.01* 0.04* SHARPE 0.01 -0.01 0.03* 0.01 SIZE 0.34* -0.02 1.01* -0.44* LEV 0.01* 0.05* -0.04* -0.04*
*significant at the 0.01 level; ** significant at the 0.05 level; ***significant at the 0.10 level
The results with respect to all the valuation multiples for German firms are unambiguous. The median German firm has significantly lower multiples than the other countries but is more profitable. The low multiples can be partly explained by the model of Danielson and Dowdell (2001) because in their model the median German firm is characterized as a declining firm (low PE and low MB), which is confirmed by the lower growth and plowback ratio. The findings of Penman also might explain the low multiples for German firms because the valuation multiples indicate lower expected future return on equity in spite of the higher current profitability.
TABLE VIII
Regression of valuation multiples on fundamentalsa
This table contains the estimation results of models (1) and (2) .The models are estimated by OLS with random effects
United States Japan Germany United Kingdom Hypothesized sign
Equity PE Constant 58.08* 44.50* 53.36* 66.01* N/A ROE 18.64* 15.42* 18.01* 17.08* + DFX -11.47* -10.65* -11.14* -10.99* - Growth 2.89* 4.83* 3.34* 3.37* + Plowback ratio -12.71* -12.12* -12.28* -14.17* + Risk-free rate -1.32* -0.11 -1.13* -0.37* - Sharpe ratio 6.92* 4.41* 6.13* 6.51* + Firm size -2.67* -1.57* -1.93* -3.78* + Leverage 2.58* -0.15 1.60** 2.04** - Country 2.23* 6.76* -5.60* -9.87* 0 R-squared 18% 19% 17% 22% Equity MB Constant 2.89* 4.98* -1.19** 0.92 N/A ROE 13.21* 12.59* 13.99* 14.15* + DFX -1.97* -2.35* -1.86* -1.84* - Growth 0.86** 0.68** 1.46* 1.47* + Plowback ratio -0.56* -0.00 -0.03 -0.26 + Risk-free rate -0.01 -0.41* -0.18* -0.30* - Sharpe ratio 0.01 -0.29 0.78** 0.72** + Firm size -0.25* -0.04 0.36* 0.05 + Leverage -2.99* -2.46* -3.67* -3.67* - Country 2.72* -3.71* -1.18* 1.58* 0 R-squared 19% 20% 17% 18% Enterprise PE Constant 17.35* 15.12* 13.77* 17.32* N/A ROA 3.74* 6.30* 6.12* 6.16* + DFX -2.16* -2.16* -1.97 -2.07* - Growth 2.18* 2.42* 2.46* 2.48* + Plowback ratio -0.25 -0.05 -0.03 -0.37** + Risk-free rate -0.35* -0.29* -0.22* -0.04*** - Sharpe ratio 0.19 0.55* 0.74* 0.57*** + Firm size -1.22* -0.87* -0.69* -1.21* + Leverage 1.66* 1.76* 1.43* 1.72* + Country 1.72* 0.97*** -3.25* -2.32* 0 R-squared 26% 27% 27% 28% Enterprise MB Constant 1.48* 1.20* 0.97* 1.57* N/A ROA 12.13* 12.56* 12.63* 12.55* + DFX -0.25* -0.25* -0.24* -0.22* - Growth 0.35* 0.38* 0.40* 0.40* + Plowback ratio -0.10* -0.05** -0.05** -0.14* + Risk-free rate -0.06* -0.05* -0.04* -0.00 - Sharpe ratio 0.03 0.09 0.11 0.11 + Firm size -0.09* -0.04* -0.02*** -0.11* + Leverage -0.54* -0.53* -0.54* -0.54* + Country 0.30* 0.17* -0.19* -0.50* 0 R-squared 54% 53% 54% 55%
*significant at the 0.01 level; ** significant at the 0.05 level; ***significant at the 0.10 level
The results show that part of the differences in valuation multiples can be explained by fundamentals. However, this analysis controls only indirectly for differences in fundamental factors. In the multivariate analysis in the next section there will be controlled directly for these factors and their interaction by the estimation the equity and enterprise value models.
V.II. Can fundamentals explain differences in valuation multiples?
In this section the results of the multivariate regression analysis will be discussed. The first part of this section deals with the impact of fundamentals on the dependent variables, in the second part the country-specific effects are discussed and in the final part industry and year effects are discussed. The estimation results of the models are presented in table VIII. The results of the Jarque-Bera tests indicate that some of the errors are not normally distributed. Because there are already observations removed from the data set to reduce the impact of outliers, further removing of observations reduces the sample dramatically, making it difficult to draw inferences about the results. Furthermore, not all errors are characterized by non-normality, so further removing of observations is no viable solution. The majority of the coefficients have the hypothesized sign. Focusing on the return on equity and return on asset variables, the positive relation is consistent with the theory that higher profitability result in a higher value. The coefficient of the foreign exchange rate variable is negative and significant in all the models. This implies that an appreciation of the country’s currency lowers the value of the firm. The growth variable is significantly positive for all the models. This indicates that growth firms have higher valuations. The coefficient of the risk-free rate in all models has a negative sign, and is for the majority of specifications significant. This indicates that firms in a country with a higher risk free rate have lower valuations. This is consistent with the asset pricing literature that firms with a higher cost of equity, which can be caused by a higher risk-free rate, have lower value because the future cash flows are discounted at a higher rate to arrive at the current share price. The sign of the coefficient of the Sharpe ratio is significantly positive in only nine specifications of the model. The positive coefficient indicates that a higher return on the overall stock market is associated with a higher valuation.
larger firms in the sample have lower valuations than smaller firms and this finding is consistent with the findings of King and Segal (2008). The sign of the coefficients with respect to leverage differs across the PE and MB multiples and not as hypothesized across the equity and enterprise multiples. With the PE multiples the sign is positive and mostly significant. The coefficient with respect to the MB multiples is negative and significant. These findings are consistent with the findings of King and Segal (2008) and Damodoran (website).
The coefficients of the country dummies in table VIII are all significant and their signs are summarized in table IX. Table IX also summarizes the results of the non-parametric tests.
TABLE IX
Summary results with respect to the univariate tests and the regression on fundamentals
This table contains a summary of the results presented in table VI and table VIII. Panel A shows the signs of the differences in median valuation multiples. Where – stands for significantly negative, O for not significant and + stands for significantly positive. Panel B shows the signs of country dummies of table VIII and their
significance.
Dependent variable
United States Japan Germany United Kingdom
PANEL A: UNIVARIATE TESTS FOR DIFFERENCES IN VALUATION MULTIPLES
Equity PE - + - -
Equity MB + - - +
Enterprise PE + + - -
Enterprise MB + + - -
PANEL B: REGRESSION ON FUNDAMENTALS
Equity PE + + - -
Equity MB + - - +
Enterprise PE + + - -
Enterprise MB + + - -
The fit of the regressions for the four models varies with an R-squared from 18 to 55 percent. This indicates that the explanatory power of the regression is highest for the enterprise models. The F-statistics (not reported) in the regression analysis are all significant. This means that the null hypothesis that all slope coefficients are equal to zero will be rejected.
The estimation results of the models indicate some significant industry and year effects (not reported) and thus part of the differences in multiples can be explained by these factors. To test whether the significant country dummies are the result of industry effects there will be tested for international differences within industries by estimating the models at the industry level. To examine whether the international differences in valuation multiples are time-varying the models are estimated per year. The regression results with respect to the country dummy variables are presented in table X and are based on the results presented in appendix 2 and appendix 3.
TABLE X
Summary of estimation results per industry and year.
This table contains the summary OLS estimation results of models (1) and (2) with heteroscedastic consistent standard errors and are estimated per industry and year. These results are based on the estimation results presented in appendix 2 and appendix 3. Panel A focuses at the industry level, and panel B focuses on year effects. Per country the number of industries (years in panel B) where the country dummy has a particular sign are presented. Where – stands for significantly negative, O for not significant and + stands for significantly positive. For instance, in the US the country dummy variable has significantly negative coefficient with respect
to the EQPE multiple in 3 out of 8 industries.
Dependent variable
United States Japan Germany United Kingdom
- O + - O + - O + - O +
PANEL A: ESTIMATION AT INDUSTRY LEVEL
Equity PE 3 0 5 2 0 6 6 1 0 8 0 0
Equity MB 0 1 7 7 1 0 5 2 0 5 3 0
Enterprise PE 0 2 6 2 3 3 7 0 0 6 2 0
Enterprise MB 0 2 6 2 2 4 7 0 0 6 2 0
SUM 3 5 24 13 6 13 25 3 0 25 7 0
PANEL B: ESTIMATION AT YEARLY LEVEL
Equity PE 3 0 12 4 1 10 13 0 2 9 0 6
Equity MB 1 2 12 8 0 7 12 1 2 10 0 5
Enterprise PE 2 2 11 6 0 10 15 0 0 10 0 5
Enterprise MB 0 0 15 6 1 7 8 2 5 12 0 3
SUM 6 4 50 24 2 34 48 3 9 41 0 19
multiples are more explained by fundamentals and the evidence for a country-specific effect is not overwhelming. Looking at Germany and the United Kingdom the results show that for the majority of industries firms from these countries trade at negative multiples and fundamentals only explain differences in some industries for some multiples.
VI. CONCLUSIONS
This study analyses the differences in valuation multiples across countries. Valuation multiples both at the equity and enterprise level are used to test whether fundamentals explain international differences in valuation multiples. In fully integrated financial markets these differences wouldn’t exist because in fully integrated financial markets fundamentals explain differences in the valuation of firms and there are no country-specific effects.
The univariate tests indicated that the valuation multiples differ internationally. Firms from the United States and Japan have higher valuation multiples than German and United Kingdom firms. The same tests are performed to analyze the contribution of fundamental factors in explaining these differences. Univariate test results show that differences in valuation multiples are partly explained by differences in fundamental factors, but there is not controlled for these factors directly and the interaction between those factors. The equity and enterprise value models control for these factors directly and the major finding is that fundamentals do not fully explain differences in valuation multiples and that there is a systematic country-effect.
Firms from the United States are valued at a premium with respect to firms from the other countries. Firms from Japan are valued at a premium with respect to firms from Germany and the United Kingdom in spite of the fact that Japanese firms are less profitable. The presence of differences in the valuation between firms from the United States and Japan and between firms from Germany and the United Kingdom is less obvious. Although industry membership explain some of the international differences in valuation multiples, these results are robust in the sense that country-specific effects are present within most industries. Furthermore, country-specific effects are present in most of the years and in only a few years fundamentals explain differences in valuation multiples. Although the country-specific effects are time varying, there is no recognizable pattern that the extent of these effects is increasing or decreasing over time. Because fundamentals do not fully explain the differences in valuation multiples and the resulting country-specific effects it can be concluded that the financial markets of the countries are not fully integrated, because in fully integrated markets these differences will not exist.
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APPENDIX 1.
Correlation matrices
TABLE 1.1
Correlation Matrix by Country
This table contains Pearson correlation coefficients of the independent variables of models (1) and (2). The variables are over the 1992-2007 period. panel A is for the US, panel B for Japan, panel C is for Germany and
panel D focuses on the United Kingdom.
ROE ROA FX GROWTH PB RF LEV SIZE SHARPE
PANEL A: UNITED STATES
ROE 1 ROA 0.28* 1 DFX 0.04 0.03 1 GROWTH -0.00 0.07 0.00 1 PB 0.05 0.17 0.03 0.02 1 RF 0.06 0.07 0.01 0.05 -0.02 1 LEV 0.33* -0.12 -0.03 0.00 -0.19* -0.00 1 SIZE 0.08 -0.13 0.04 -0.13** -0.08 -0.09 0.25* 1 SHARPE -0.02 -0.02 -0.49* 0.02 -0.02 0.00 0.08 -0.02 1 PANEL A: JAPAN ROE 1 ROA 0.24* 1 DFX -0.01 0.02 1 GROWTH 0.11 0.21* -0.04 1 PB 0.13 0.24* -0.03 0.11* 1 RF 0.00 -0.04 -0.07 -0.12 -0.11 1 LEV 0.29* -0.31* -0.00 -0.01 -0.10* 0.03 1 SIZE 0.06 -0.10 -0.04 -0.09 -0.04 -0.02 0.15* 1 SHARPE 0.02 0.03 -0.27* 0.07 0.07 -0.33* -0.04 0.05 1 PANEL A: GERMANY ROE 1 ROA 0.45 1 DFX -0.03 0.00 1 GROWTH 0.10 0.02 -0.02 1 PB 0.12 0.13 -0.02 0.14 1 RF -0.10 -0.03 0.07 -0.04 -0.08 1 LEV 0.51* -0.09 -0.07 0.21* 0.01 -0.20* 1 SIZE 0.09 -0.11 -0.10 0.04 -0.02 -0.06 0.35* 1 SHARPE 0.09 0.01 -0.30* 0.02 0.05 -0.20* 0.00 0.16 1
PANEL D: UNITED KINGDOM
ROE 1 ROA 0.49* 1 DFX 0.00 0.01 1 GROWTH -0.00 -0.00 -0.1 1 PB 0.06 0.08 -0.04 0.04 1 RF 0.07 0.07 0.11 -0.00 0.02 1 LEV -0.04 -0.06 -0.01 -0.01 -0.08 -0.04 1 SIZE -0.04 -0.08 -0.13 -0.07 -0.08 -0.11 0.16* 1 SHARPE 0.05 0.05 -0.23* -0.00 0.06 0.09 -0.02 0.01 1 *significant at the 0.01 level
APPENDIX 2.
Regression of valuation multiples based on industry membership.
TABLE 2.1
Regression of valuation multiples on fundamentals based on industry membership
This table contains the estimation results of models (1) and (2. Only the coefficients of the country dummies and the goodness of fit statistics are presented. The models are estimated based on industry membership.
Industry Dependent variable
United States Japan Germany United Kingdom
Coefficient R2 Coefficient R2 Coefficient R2 Coefficient R2
Oil and Gas
Equity PE 4.54* 039 -4.22* 0.37
X
-5.11* 0.38 Equity MB 0.91* 0.26 -1.22* 0.26 -0.83 0.26 Enterprise PE 0.53 0.27 -0.07 0.27 -0.81*** 0.27 Enterprise MB 0.32* 0.31 0.19* 0.29 -0.31* 0.29 Basic Materials Equity PE -1.51* 0.43 8.34* 0.44 -6.24* 0.39 -6.63* 0.39 Equity MB 1.50* 0.60 -1.54 0.60 -2.93* 0.59 -0.77** 0.58 Enterprise PE -0.86 0.26 2.69* 0.28 -3.02* 0.27 -0.33 0.25 Enterprise MB 0.05 0.57 0.04 0.57 -1.75* 0.56 0.14 0.57 Industrials Equity PE -1.45* 0.30 11.90* 0.35 -7.98* 0.30 -8.13* 0.32 Equity MB 3.99* 0.29 -6.20* 0.65 -3.08* 0.25 -0.51 0.25 Enterprise PE 0.59* 0.25 0.78** 0.35 -3.46* 0.26 -1.21* 0.26 Enterprise MB 0.43* 0.45 0.33* 0.43 -0.53* 0.42 -0.37* 0.43 Consumer Goods Equity PE 1.20*** 0.32 12.80* 0.37 -5.91* 0.32 -10.61* 0.35 Equity MB 4.17* 0.51 -3.89* 0.49 -2.71* 0.47 -3.24* 0.48 Enterprise PE 1.44* 0.27 0.33*** 0.25 -3.04* 0.26 -2.38* 0.27 Enterprise MB 0.65* 0.46 0.39* 0.42 -0.31* 0.40 -0.70* 0.43 Health Care Equity PE 3.45* 0.28 1.01* 0.27 -12.99* 0.31 -5.80* 0.28 Equity MB 4.71* 0.44 -6.12* 0.44 -4.71* 0.41 -2.10* 0.41 Enterprise PE 5.11* 0.30 -5.15* 0.25 -9.56* 0.26 -3.58* 0.27 Enterprise MB 1.20* 0.52 0.89* 0.49 -1.70* 0.46 -1.09* 0.47 Consumer Services Equity PE 3.01* 0.31 6.92* 0.32 -6.81 0.31 -8.79* 0.35 Equity MB 2.64* 0.36 -3.26* 0.35 0.28 0.34 -2.18* 0.35 Enterprise PE 1.70* 0.28 0.61 0.25 -5.79* 0.28 -2.33* 0.28 Enterprise MB 0.66* 0.43 -0.50* 0.39 -0.72* 0.38 -0.62* 0.40 Utilities Equity PE -5.20* 0.32 16.09* 0.42 -0.65 0.29 -2.61** 0.29 Equity MB -0.00 0.52 -0.39 0.52 -0.08 0.52 0.40 0.52 Enterprise PE 0.19 0.29 0.04 0.29 -2.04* 0.31 0.06 0.29 Enterprise MB -0.02 0.48 0.05 0.48 -0.10** 0.48 0.33 0.48 Telecom and Technology Equity PE 3.45* 0.28 -1.02* 0.27 3.50* 0.28 -7.77* 0.29 Equity MB 2.24* 0.46 -1.58* 0.46 1.18* 0.46 -1.92* 0.46 Enterprise PE 2.90* 0.24 -3.52* 0.23 2.09* 0.21 -3.08* 0.22 Enterprise MB 0.59* 0.42 -0.62* 0.42 1.29* 0.42 -0.99* 0.43* significant at the 0.01 level ** significant at the 0.05 level *** significant at the 0.1 level
Note: X= no observations of German firms in the Oil and Gas industry in the sample.
APPENDIX 3.
Regression of valuation multiples on yearly basis
TABLE 3.1
Regression of valuation ratios on yearly basis
This table contains the estimation results of models (1) and (2). Only the coefficients of the country dummies and the goodness of fit statistics are presented. The models are estimated on yearly basis.
Industry Dependent variable
United States Japan Germany United Kingdom
Coefficient R2 Coefficient R2 Coefficient R2 Coefficient R2
TABLE 3.1 continued 2005 Equity PE 4.70* 0.20 -0.89 0.20 -1.49* 0.20 1.13* 0.20 Equity MB 5.04* 0.23 2.02* 0.23 -1.61* 0.23 -1.22* 0.23 Enterprise PE 2.61* 0.35 1.23* 0.34 -0.89*** 0.35 -1.23* 0.35 Enterprise MB 1.52* 0.42 -0.89** 0.42 0.48** 0.42 -0.36** 0.42 2006 Equity PE 3.73* 0.19 14.56* 0.19 2.52* 0.19 -3.18* 0.19 Equity MB 2.30* 0.25 8.92* 0.25 -1.55* 0.25 -1.96* 0.25 Enterprise PE 0.14 0.39 5.77* 0.39 -1.00* 0.39 -1.27* 0.39 Enterprise MB 2.09* 0.56 4.20* 0.56 -1.39* 0.56 -1.78* 0.56 2007 Equity PE 9.48* 0.27 -1.89* 0.27 -2.14* 0.27 -2.85* 0.27 Equity MB 3.11* 0.29 -6.14* 0.29 -7.06* 0.29 -6.71* 0.29 Enterprise PE 3.82* 0.36 2.34* 0.36 -2.85* 0.37 -4.25* 0.36 Enterprise MB 0.89* 0.58 0.56* 0.58 -1.22* 0.58 -3.29 0.57
* significant at the 0.01 level ** significant at the 0.05 level *** significant at the 0.1 level .
