On the relationship of stewardship and valuation –

Empirical evidence from German firms

Abstract

This empirical study examines the association between the stewardship and

valuation roles of financial reporting. While analytical literature based on agency

theory has hinted at differences between the two objectives, recent empirical

studies based on US data suggest a positive relationship. This paper contributes to

the literature by providing evidence from a context that differs from the US with

regard to the corporate governance setting. We present empirical findings on the

relationship using data from German firms between 2006 and 2013, all preparing

their financial statements according to International Financial Reporting Standards

(IFRS). We find a positive empirical relation between our proxies of valuation

and stewardship in univariate and multivariate settings, the latter including

firm and corporate governance factors. Our study might be of interest for the

International Accounting Standards Board (IASB), which currently is reconsidering

the importance of stewardship in its conceptual framework, as we provide support

for the standard-setter’s assessment of a generally positive relationship of the two

roles of financial reporting information.

Keywords: Decision usefulness, IFRS, stewardship, valuation

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1 Introduction

Financial reporting serves multiple purposes. In particular, the role of financial reporting

information in informing capital market participants’ investment decisions (valuation

usefulness) and its role in providing incentives for employed managers (stewardship

usefulness) have repeatedly been analysed in the accounting literature. Bushman and

Smith (2001, p. 270) maintain that “it is widely accepted that reported earnings play a

fundamental role in both determining stock prices, and in evaluating and compensating

executives. However, the relation between the governance and value relevance of earnings

is not well understood.” While some theoretical literature has analysed the relationship of

stewardship and valuation (e.g. Gjesdal, 1981; Paul, 1992), “there is very little evidence

on whether there is any association between these two roles” (Banker et al., 2009, p.

648; also see O’Connell, 2007). We concur with Bushman, Engel, and Smith (2006) that

analysing the relations among different roles of accounting information is essential to

gain a better understanding of the forces that shape accounting and reporting practices.

In this vein, the present paper aims at investigating empirically the research question of

how stewardship and valuation are related.

To address this research question we use data from German firms listed in the

regulated market of the German stock exchange. We use hand-collected data on CEO

and executive board compensation and accounting and market data from Compustat and

Datastream for the period between 2006 and 2013. As German listed firms are required

to apply IFRS since 2005, our study analyses the relation of the two purposes based

on IFRS financial statement information. While recent studies have investigated the

relationship of stewardship and valuation using US data (Bushman et al., 2006; Banker

et al., 2009), we are not aware of any study analysing the relationship for a setting

where IFRS are applied. As the importance of stewardship in US-GAAP has been less

pronounced than in IFRS (e.g. Whittington, 2008; Zeff, 2013), an analysis of empirical

data based on IFRS provides additional insights into the relationship. Moreover,

and more importantly, conceptual and analytical literature has emphasized that the

relationship of the two objectives might be affected by the local corporate governance

and capital market setting (Walker, 2010; Kuhner and Pelger, 2015). Therefore, findings

for the US context that reveal a positive relation between proxies for the two objectives

(Bushman et al., 2006; Banker et al., 2009), might not hold in different environments.

Given the context-specific roles of financial accounting, it is at least not immediately

obvious that the relation is similar for settings outside the US. In contrast to the US,

in Germany board and ownership structures differ substantially in that there exists a

separate supervisory board that is, among other things, responsible for executive pay

contracts and managerial ownership is rather low, while families and strategic investors

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play a more important role (Engelen, 2015). Furthermore, in Germany management

pay has used to be relatively low compared to other countries (Conyon and Schwalbach,

2000) and the relation of pay to performance has been found to be rather weak (Rapp

and Wolff, 2010). Thus, the specific features of corporate governance make the German

context an appropriate setting to analyse the relationship between the stewardship and

valuation roles of financial reporting information.

Our study follows the approach by Bushman et al. (2006) in analysing the relationship.

Therefore, we use three regression models. In the first model, we measure the valuation

coefficient of earnings and adopt the approach from prior literature (e.g. Barth et al.,

1999; Engel, Hayes, and Wang, 2003; Bushman et al., 2006; Banker et al., 2009). The

second model examines the stewardship coefficient of earnings and the third model

combines both coefficients to analyse the association between valuation and stewardship.

Similar to prior studies, we apply cash compensation and use total compensation for

robustness tests. In addition to extant literature, we do not solely focus on the CEO

compensation but also consider the compensation received by the executive board as a

whole.

Our empirical analyses indicate that, in our sample of German firms, stewardship and

valuation are positively related. This finding pertains to the cash and total compensation

of CEOs, and is visible in the average compensation of executive board members. We use

several robustness checks, which in general provide support for the positive relationship.

In a further step, we explore whether the relationship is affected when we control for firm

factors, such as size, leverage and market-to-book ratio, and corporate governance factors,

in particular firm ownership. Our results show that the positive association between

valuation and stewardship remains both for CEO and average board compensation.

Thus, firm or corporate governance factors do not seem to alter the relationship for our

sample.

Apart from the academic literature, accounting objectives, and in particular the role

of stewardship, have recently been discussed by accounting standard-setters in revisions

of their conceptual frameworks. In 2010, the IASB and the FASB decided to drop

stewardship as a separate objective in their framework because standard-setters see it

as a part (or a sub-objective) of the valuation objective (Pelger, 2016). In its second

revision of the framework, following criticism by constituents, the IASB now intends

to put more emphasis on stewardship as a part of the objective of financial reporting,

but still maintains that there is no need to state stewardship separately as it can be

encompassed in the valuation objective (IASB, 2015, 1.3, BC1.6-10). As this statement

by the IASB is not necessarily in line with academic research (Cascino et al., 2014),

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the present paper intends to contribute to the current debate by presenting empirical

findings on the relationship between stewardship and valuation drawing on a sample of

firms preparing their financial statements according to IFRS. As our results suggest that

there is a positive relationship between the two roles of financial reporting information

and that this relationship is not affected by controls for firm and governance factors, we

provide support for the standard-setters’ statement that valuation useful information is in

general also useful for stewardship purposes. However, our findings do not preclude that

these objectives might demand different accounting information in specific circumstances.

In the following section, we provide some background on the objectives of financial

reporting and an overview of the theoretical and empirical literature. Section 3 introduces

our research design and our sample data. Section 4 presents our empirical results, while

section 5 offers some conclusions.

2 Literature review

2.1 Theoretical background

Starting with Gjesdal (1981), several analytical studies have analysed the relationship of

the two objectives. Gjesdal (1981) formally distinguishes between the value of accounting

information for decision-making by investors and for solving the incentive problem

between agent and principal. Starting comparison with the use of the Blackwell theorem

combining this approach with an agency model, leads Gjesdal (1981) to conclude that

the rankings of different accounting systems might differ for the two purposes.

Paul (1992) compares stock-based and accounting-based compensation contracts in a

linear agency model. He shows that different accounting signals are aggregated by the

capital market in a way that is not necessarily optimal for contracting. He analytically

distinguishes the two objectives in that from a stewardship perspective, accounting

should be informative about managerial effort, while from a valuation perspective

accounting should rather provide information on the stochastic (and thus uncertain)

part of firm value. This difference between the two objectives was also delineated by

Lambert (2001), while Heinle and Hofmann (2011) extend the agency setting to the

availability of soft (non-contractible) information. They show that the disclosure of

such information is positive from a valuation perspective but entails negative effects for

stewardship purposes. While all these studies regard their findings as corroborating the

difference (or trade-off) between the two objectives, it is to stress that the information

settings in these models not necessarily capture real-world complexities in information

provision and use. Nonetheless, the general take-away from the analytical literature

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is that there are constellations in which the two objectives do require different information.

A recent analytical paper by Kuhner and Pelger (2015) analyses the relationship in

an agency setting and reveals that, in their model, the two objectives react similarly to

changes in accounting quality parameters (relevance, reliability) but differ in their reaction

with respect to opportunities for earnings management offered by the accounting system.

Important for our study, the authors stress that the relation of the two objectives hinges

on the context, that is, parameters of the manager, the firm and characteristics of the

economy or the corporate governance system as a whole. First, we take up these findings

as a motivation why a study of the association of stewardship and valuation in a different

geographical setting than the US might be warranted. Second, we control for firm-specific

factors and corporate governance factors in our multivariate analysis of the relationship

of stewardship and valuation.

2.2 Empirical background

Bushman et al. (2006) investigate how accounting information are used for valuation

and incentive contracting. They refine the analytical model by Paul (1992) to specify

their null hypothesis that there is no relation between the two objectives. The authors

find that firm and industry specific valuation and compensation earnings coefficients are

significant positively related with respect to changes between two periods of 15 years

(before 1986 and between 1986 and 2000). Their cash compensation data was taken

from the annual Forbes study and contains 141 to 3,134 firm-years per industry for a

30-year period from 1970 to 2000. They study 28 different industries defined on the basis

of two-digit Standard Industry Classification (SIC) codes. As the CEO compensation

measure they apply cash compensation, following Core, Guay, and Verrecchia (2003)

defined as the salary plus bonus payments.

The approach of Bushman et al. (2006) combines a univariate and a multivariate

analysis. Their univariate analysis estimates valuation and compensation earnings

coefficients firm and industry specific. Firm specific means that for each firm there are

at least 20 annual observations during the sample period. The firm specific analysis

estimates one valuation coefficient for each of these 379 firms with an average of 26

observations per firm. Industry specific signifies the creation of an industry specific

valuation coefficient (for each of the 28 industries) controlling for year fixed effects. The

univariate analysis first regresses cumulative market adjusted stock returns on earnings

to determine the valuation coefficient of earnings and second quantifies the compensation

coefficient of earnings by regressing CEO cash compensation on earnings controlling for

public performance information with the proxy of stock market returns. Bushman et al.

(2006) employ a Pearson and Spearman rank correlation table with the estimated firm

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and industry specific valuation and compensation earnings coefficients indicating the

association between valuation and stewardship weights placed on earnings. Additionally,

a multivariate analysis estimates the relation between the valuation and compensation

earnings coefficients controlling for growth opportunities, regulation, noise in earnings

and other public performance information. These analyses reveal a significant positive

firm and industry specific relationship for the whole period and the two sub-periods.

Banker et al. (2009) conduct an empirical study that concentrates on the relationship

between the value relevance and incentive contracting relevance of earnings and cash

flows for US data between 1993 and 2003. Their sample contains 7,076 CEO-year

observations and uses CEO cash compensation in line with Bushman et al. (2006). Their

first regression estimates pay-sensitivity and value relevance of earnings and cash flows.

In a second regression, Banker et al. (2009) use cross-sectional firm and yearly regressions

to analyse the relationship between pay-sensitivity and value relevance coefficients of

earnings. They find significant positive coefficients supporting that the compensation

weight on earnings is increasing in the value relevance of earnings. The same holds for

cash flows. Thus, the main result of their paper is that value relevance of performance

measures plays a major role in the use of accounting performance measures for CEOs’

evaluation.

In a related study, Gassen (2008) analyses the relation of valuation and stewardship

by using a different approach for a dataset of US firms between 1990 and 2005. First,

instead of using the concept of value relevance, that is employed by Bushman et al.

(2006) and Banker et al. (2009), he uses an event study that focuses on the effect

that the publication of financial statement information has one share price in a certain

(short) time frame. Second, Gassen (2008) does not use compensation data to approach

the stewardship perspective, but instead models a market for stewardship information,

where the supply of stewardship information is proxied by conditional conservatism

(asymmetric timeliness of earnings) and the demand for stewardship information by

the importance of non-equity stakeholders. Gassen (2008) finds a negative relation

between the valuation relevance of earnings and his proxies for stewardship. However, it

is to note that his definition of stewardship is not directly following the principal-agent

setting between owners and employed managers, usually considered by the academic

literature (and the standard-setters) when referring to stewardship. Instead, his definition

of stewardship rather reflects a more general contracting perspective (Watts, 2003),

considering other stakeholders than owners. Thus, he provides evidence that valuation

useful information might not necessarily be useful for such a broader notion of contracting.

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The effect of mandatory IFRS adoption on value relevance in relation to equity

markets shows different evidence for European countries. Devalle, Onali, and Magarini

(2010) analysed IFRS data with respect to their valuation relevance and find an increase

in value relevance of earnings and a decrease in value relevance of book value of equity

with the adoption of IFRS in Germany and France. In contrast, the adoption of IFRS in

the UK results in an increase in value relevance of earnings and book value of equity, while

the IFRS adoption in Italy and Spain results in a decrease in both measures. Their sample

contains 3,721 firms and observes a period between 2002 and 2007. Choi, Peasnell, and

Toniato (2013) investigate the accuracy of analysts’ forecasts for IFRS adoption between

2003 and 2007 in the UK and state that value relevance is significantly higher under IFRS.1

A few studies have analysed IFRS data with respect to their stewardship relevance. In

particular, these studies focus on the effect of IFRS adoption on the relation of accounting

measures and CEO/executive compensation. For instance, Voulgaris et al. (2014) examine

the effect of IFRS adoption in the UK on the type of performance measures firms use

to evaluate and compensate their managers. Their data is hand-collected and includes

3,000 UK firms over eight years (2002-2009), four years before and after IFRS adoption

each. Their findings indicate that IFRS adoption results in a decrease in the use of

accounting numbers for managerial performance measurement purposes. These findings

support the arguments by Watts (2006) saying that IFRS are not appropriate for the use

in managerial incentive contracts because of the discretion offered by its standards, and

in particular by fair value measurement.

Another study with European data, by Ozkan, Singer, and You (2012) analyses the

mandatory adoption of IFRS in Europe and the contractual usefulness of accounting

information in executive information during the period 2002–2008. They distinguish

between two sub-periods excluding the year of IFRS adoption 2005. One sub-period

contains the pre–mandatory adoption period (2002-2004) and the other represents the

post–mandatory adoption period (2006-2008). Ozkan et al. (2012) focus on the change in

executive cash compensation.2 Their sample includes 13,505 executive-year observations

for 3,046 firm-years of 892 publicly listed companies from 15 European countries. In a

first estimation, they analyse if compensation committees base executive compensation

more on an accounting measure of the firm’s performance after mandatory IFRS

adoption. In a second regression, they explore if the general use of accounting based on

relative performance evaluation from the pre- to post-IFRS adoption period has changed.

In a third regression, they estimate the sensitivity of executive pay-to-stock-return

1For a summary see Bruggemann, Hitz, and Sellhorn (2013).2Their paper investigates cash compensation because contractual usefulness of accounting performance

measures, which is the focus of their analysis, refers to the cash component rather than to the equitycomponent of executive compensation.

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performance and the change in this sensitivity after the adoption of IFRS. Ozkan

et al. (2012) find that European companies increased the use of accounting-based

pay-performance sensitivity as compensation committees placed a higher weight on

accounting earnings in compensation contracts when the adoption of IFRS entailed a

major accounting change.

All in all, our presentation of the theoretical and empirical literature has shown that,

on the one hand, there is the expectation based on analytical results that optimal valuation

information does not automatically give rise to optimal information for stewardship

purposes. However, there is less theoretical evidence on how pervasive the possible

conflict between the two purposes actually is. Recent analytical literature suggests that

the (firm and governance) context plays a role for the nature of the relationship. On

the other hand, several empirical studies that have explicitly focused on the relationship

between stewardship and valuation, using compensation data from the US, have shown

significant positive associations. Thus, for our empirical analysis we do not form any

specific expectation regarding the relationship.

3 Research design

3.1 Empirical model

Our study investigates the association between the stewardship and valuation roles

of financial reporting. Following Bushman et al. (2006), we examine this relation

in a three-step regression model. We establish a model that estimates two single

regressions interrelated with a third regression. The first regression estimates valuation

relevance (VEC), the second regression estimates stewardship relevance (CEC), while

the third regression combines both estimated coefficients and estimates the valuation on

stewardship coefficient (VSC). We use industry and firm specific regressions, controlling

for year fixed effects in our industry specific regressions. Each regression estimates

VECs, CECs and VSCs for each industry/firm with aggregated firm year observations

(industry/firm specific).

The first regression estimates the effect of earnings (EARN) on the market adjusted

stock returns (MRET) for each industry/firm. EARN defines the change in net income

between year t and t-1 deflated by the market capitalization at the end of year t-1.

The coefficient that results from a change in EARN by one unit on MRET is saved as

the valuation earnings coefficient for each industry/firm. This signifies that the firms’

valuation earnings coefficients are aggregated within an industry/for each firm.

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The same approach is applied for regression (2). The second equation describes the

regression we run to estimate the industries’/firms’ compensation earnings coefficients,

again aggregating firm years within an industry or for one specific firm. The

compensation earnings coefficients represent stewardship relevance by estimating the

association between a change in EARN and the percentage change in CEOs’/executives’

cash compensation (C) controlling for the 12-months cumulative stock market return of

i’s fiscal year (RET).

After running the first two regressions we obtain industry and firm specific valuation

and compensation earnings coefficients that are included as independent and dependent

variables in the third regression. The third regression then estimates the association

between valuation relevance (VEC) and stewardship relevance (CEC), aggregated by

industries/firms. Our resulting association (VSC) indicates the relationship between

stewardship and valuation based on an industry/firm specific estimation. This

industry/firm specific aggregation is necessary to have sufficient variation in the year

observations within an industry/for each firm to estimate a valuation and compensation

earnings coefficient. Different industry/firm specific valuation and compensation earnings

coefficients are important for the variation in the third regression stage and for estimating

an overall association between the two objectives.3

We extend our model to a multivariate setting in that we include firm and ownership

variables in our third regression model (3). Following Kaserer and Moldenhauer (2008),

our third regression model (3) controls for different ownership variables. We use

free float, a distinction between inside and outside investors and six different groups

of shareholders. The third equation denotes these variables with CorpGovi. Our

six shareholder groups are manager, family, bank, strategic, institutional and other

investors. Inside investors comprise manager and family, while outside investors include

bank, strategic, institutional and other investors. In line with Engelen (2015) and

Rapp and Wolff (2010), we include firm variables for growth opportunities (market

to book ratio), risk (leverage) and firm size (logarithm of total assets). All firm and

ownership variables are medians of firm/industry i that consider i’s firm year observations.

In our first regressions we distinguish between three different calculations for the

market adjusted stock return (MRET) that differ in including/excluding the reaction

of the market on earnings in financial reporting. Our main results concentrate on the

12-months market adjusted stock returns calculated from the beginning to the end of the

3In contrast, if we ran the first and the second regression over the whole sample, we would obtainexactly one valuation earnings coefficient from the first regression and one compensation earningscoefficient from the second regression. A third regression would thus not provide any meaningful results.

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fiscal year t (MRET-12m) according to Bushman et al. (2006). For robustness tests we

apply two other market adjusted stock return calculation: MRET-15m and MRET-12m3.

With MRET-15m that uses 15-months market adjusted stock returns calculated from

the beginning of the fiscal year t and ending 3-months after the end of the fiscal year t,

we follow Perotti and Wagenhofer (2014). With MRET-12m3 that describes 3-months

shifted 12-months returns beginning and ending 3-months after the start/end of the fiscal

year, we follow Perotti and Wagenhofer (2014) and Banker et al. (2009).

Empirical model

1. First regression estimates valuation relevance (VEC):

MRETi = α0 + V ECi · EARNi + α1D(t) + εi (1)

2. Second regression estimates stewardship relevance (CEC):

Ci = β0 + CECi · EARNi + β1RETi + β2D(t) + εi (2)

3. Third regression estimates the effect of VEC on CEC (VSC):

CECi = γ0 + V SC · V ECi + γ1Firmi + γ2CorpGovi + εi (3)

where:i = Industry (two-digit SIC-codes) or firm

MRETi = The 12-months cumulative market adjusted stock market return of i’s fiscal year;

RETi = The 12-months cumulative stock market return of i’s fiscal year;

EARNi = The change in deflated earnings between year t and t-1 of i;

Ci = The percentage change in compensation between year t and t-1 of i;

V ECi = Valuation earnings coefficient of firm i or industry i;

CECi = Compensation earnings coefficient of firm i or industry i;

V SCi = Valuation on stewardship coefficient of firm i or industry i;

Firmi = Firm variables of firm i or industry i;

CorpGovi = Governance variables of firm i or industry i;

D(t) = Year dummies for fixed year effects.

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3.2 Sample and data

We use hand-collected data and data from the commercial databases Compustat Global

and Datastream Worldscope for firm years between 2006 and 2013. The hand-collected

data contains executive compensation and corporate data. The data from the commercial

databases provides financial statement and stock market information for the firms in our

sample.

The executive compensation information was derived from the annual reports of

German listed stock corporations. The dataset on compensation differentiates between

the Chief Executive Officer’s (CEOs’) compensation and the average board members’

compensation that is defined as the board members’ compensation (including the CEO)

divided by the total number of board members. Furthermore, the dataset distinguishes

between annual cash compensation and total compensation. Cash compensation is the

sum of the fixed salary, fringe benefits and short term incentives (bonus), while total

compensation also includes long term (equity) incentives. Following other empirical

studies (e.g. Bushman et al., 2006; Banker et al., 2009), we focus on cash compensation

and use total compensation for robustness tests. In line with Bushman et al. (2006),

we exclude firm years with a CEO change (Panel A) and include these observations for

robustness tests (Panel B). Firm year observations where we have no information about

a CEO change are also excluded in Panel A and included in Panel B. We focus on an

industry specific (two-digit SIC-specific) and a firm specific analysis of up to 844 German

firm year observations.

-Insert Table 1 here-

Table 1 explains our sample selection. In our analysis we focus on firms listed in

the main indices of the German stock exchange, DAX, MDAX, TecDAX and SDAX, in

the fiscal years 2006 to 2013. The total number of firms that form part of these indices

between 2006 and 2013 is 1280 (160 per year). From this number of firms we excluded

foreign firms, firm year observations with incomplete compensation data, firms applying

US-GAAP and firm year observations with a lack of data availability (missing earnings,

stock market returns, total assets or market capitalization). This reduction leads to

844 firm year observations (159 firms) with available compensation data for the board

members. Another 146 firm year observations lack individualized CEO compensation

data. Therefore, we have 698 firm year observations with the percentage change in CEO

compensation between year t and t-1 for our empirical analysis for Panel B. Our main

analysis observes firm years without a CEO change and firm years where we have no

information about a CEO change. Therefore, our main sample selection Panel A contains

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658 firm year observations for the percentage change in average board compensation

and 615 firm year observations for the percentage change in CEO compensation. Solely

focussing on CEO compensation data may thus give rise to a bias, considering the

self-selection of firms not publishing individual compensation of board members. While

it is, in general, mandatory to present separate compensation for the members of the

management board, companies do have the opportunity to avoid this individualised

disclosure if the annual shareholder meeting supports this proposal with a majority of at

least 75%.4 Therefore, our analysis also uses the average board compensation in order to

check whether this possible bias influences our results regarding the valuation-stewardship

relationship.

Our industry classification follows the two-digit Standard Industrial Classification

(SIC). Table 2 represents the arranged industries naming the two-digit SIC codes. Table

3 provides an overview of our sample data presenting the variable abbreviation, the

description and the source. The difference in NICON between year t and t-1 is deflated

by the market capitalization at the beginning of year t (MCAP) and represents our

independent variable EARN. Our data allows us to calculate cash and total compensation

of the CEO and the average among board members (AVG). Dividing the cash/total

compensation by the number of board members results in the average cash/total

compensation among board members including the CEO. Leverage, firm size and market

to book value are additional firm variables. All share variables and free float indicate

different groups of shareholders that we include for ownership control purposes.

-Insert Table 2 and Table 3 here-

3.3 Descriptive statistics and correlations

Table 4 provides summary statistics of the main sample variables. We present an

industry and firm specific overview that includes 698 firm year observations for the

CEOs’ compensation and 844 for the board members’ compensation. This leads us to

fifteen industry and at least 144 firm specific observations for each variable. This table

provides an overview of Panel B that includes firm year observations with a CEO change

and with no information about a CEO change. Panel A in contrast excludes these firm

year observations. Leverage, firm size, market to book value, free float and all share

variables are medians of i’s industry/firm year observations.

4Hitz and Werner (2012) analyse factors that explain companies’ decisions to avoid individualiseddisclosure of compensation.

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-Insert Table 4 here-

Table 5 shows the correlation coefficients of the median variables according to Table

4. We can observe positive and highly significant correlation coefficients. The change

in cash compensation is not significantly correlated with the stock return (MRET

and RET) for CEOs’ firm specific and for average boards’ industry specific median

variables. Intervening variables can exist and disrupt the relation between these variables.

-Insert Table 5 here-

4 Results

4.1 Valuation and stewardship relevance

Table 6 presents the results for the first regressions (equation (1)) using industry and

year fixed effects. Although we are not using industry fixed effects in our industry

specific regressions that we run for saving the coefficients and that lead to the third

industry specific regression, we use industry fixed effects in Table 6, where we present

regressions without the aggregation for illustrative purposes. The results for equation (1)

with industry and year fixed effects indicate a significant and positive valuation earnings

coefficient for all market adjusted stock return measures. In line with Bushman et al.

(2006) that use 12-months market adjusted returns, we can state a significant positive

association of earnings on market adjusted stock returns.

-Insert Table 6 here-

Table 7 displays the results of the second regression stage as explained in equation (2)

including industry and year fixed effects. We find highly significant positive compensation

earnings coefficients for all compensation measures. The 12-months stock market return

has a positive effect on the percentage change in compensation. Our results are consistent

with Bushman et al. (2006), indicating positive values for the compensation earnings

coefficients (CECs).

-Insert Table 7 here-

Table 8 provides a summary statistic of the estimated industry and firm specific

valuation and compensation earnings coefficients. The estimated industry valuation

earnings coefficients lie between -344.12 and 1720.92 for the 12-months market adjusted

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returns. The industry compensation (stewardship) earnings coefficients for the CEOs’

cash compensation are between -771.26 and 22614.45. After deleting the industry

Agriculture, because we have only 7 firm year observations (one firm) representing this

industry, we include 837 instead of 844 firm year observations in our empirical model.

The firm specific valuation and stewardship coefficient lie in a higher range than the

industry specific ones. At least six firm year observations estimate on firm specific

coefficient. The third regression stage uses these estimated values to regress valuation

and stewardship coefficients on each other to obtain a valuation on stewardship coefficient

(VSC).

-Insert Table 8 here-

4.2 Effect of valuation on stewardship relevance

In Table 9, we show the Pearson and Spearman Rank correlation table to compare the

relation of our proxies for stewardship and valuation. While the Pearson correlation

analyses linear relationships, the Spearman rank correlation table examines monotonic

relationships. The correlation table indicates a positive relation. The Pearson and

the Spearman rank correlation coefficients are positive significant for the CEO’ cash

compensation for our industry specific regressions. Our firm specific regressions

show significant positive correlation coefficients for the CEOs’ and average boards’

compensation in the Pearson correlation table.

-Insert Table 9 here-

Table 10 summarizes the industry and firm specific associations between valuation

and stewardship relevance of earnings. Our industry and firm specific findings indicate a

significant positive relationship between valuation and compensation earnings coefficients

for CEOs’ cash compensation. Furthermore, our firm specific regressions indicate a

significant positive relationship for average boards’ cash compensation. We cannot

support these findings for industry specific boards’ cash compensation. Figures 1 and 2

illustrate the industry and firm specific relationship between valuation and stewardship.

-Insert Table 10, Figure 1 and Figure 2 here-

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4.3 Multivariate analysis

Table 11 presents our firm specific results for the third regression stage controlling for

different shareholder types, firm size, leverage and market to book ratio. The association

between valuation earnings coefficients and stewardship earnings coefficients remains

significant positive, while including control variables for ownership, growth opportunities,

risk and firm size.

The first and the second column of Table 11 summarize the results controlling for

free-float, firm size, leverage and market to book value. The third and the fourth column

of Table 11 indicate results controlling for inside and outside investors’ shares. The fifth

and the sixth column of Table 11 show the results controlling for different shareholder

groups’ shares instead of inside and outside investors’ shares. These six shareholder

groups are manager, family, strategic, bank, institutional and other shareholder, while

inside shareholder are managers or family and outside shareholders are strategic, bank,

institutional or other. All control variables are medians for firm i of i’s firm year

observations.

With CEOs’ and average boards’ cash compensation we can support our prior results

of a positive association between valuation and stewardship coefficients of earnings in

a multivariate setting. These results hold for our industry specific regressions with the

use of CEOs’ cash compensation that we provide in Table 12. In line with prior results

for Panel A, average boards’ cash compensation remains not significant in our industry

specific univariate and multivariate analyses. This might be the case because we have

more aggregation of firm year observations on industry level in contrast to firm level basis.

Furthermore, we can observe that free float is significantly positively influencing

stewardship coefficients of earnings (CEC), while an increase in outside, family or

strategic investors significantly decreases stewardship relevance (CEC). For average

boards’ cash compensation CEC is higher in the regression model that includes free

float. Therefore, free float might result a stronger connection between accounting and

compensation. In contrast, this connection is weaker with a high degree of outside

investors.

-Insert Table 11 and 12 here-

15

4.4 Robustness tests

We run several additional regressions to verify our results. These results are summarized

in Tables 13, 14 and 15. Our industry and firm specific regressions with MRET-12m

and total compensation support our significant positive findings for MRET-12m and

cash compensation (Table 13). Table 14 uses Panel B instead of Panel A. Panel B also

includes firm year observations with a CEO change and firm years for which we have no

information about a CEO change. We cannot support our prior findings on an industry

specific basis but on a firm specific one with CEOs’ total compensation. The industry

specific findings indicate a significant positive relationship for average boards’ total

compensation, while our firm specific findings indicate a significant negative association

for average boards’ total compensation. These additional findings suggest that the

association tends to be stronger when CEO changes are taken into account and not

included in our regression models.

Table 15 presents industry and firm specific findings for Panel A and B and with

market adjusted return over 15 months (MRET15m) and 12 months by 3 months shifted

(MRET12m3). Panel A’s industry and Panel A’s and B’s firm specific regressions

support our prior findings of a significant positive association for CEOs’ cash and

total compensation and for both market adjusted return measures. Average boards’

compensation only shows significant positive associations for our firm specific regressions

in Panel A and B but not for our industry specific regressions. Again we can observe

one negative significant finding for average boards’ total compensation in Panel B with

MRET12m3. This again shows the importance to consider CEO changes when analysing

the relationship of stewardship and valuation.

-Insert Tables 13, 14 and 15 here-

5 Conclusion

In this paper, we empirically investigate the association between the stewardship and

valuation roles of financial reporting. Observing aggregate valuation and stewardship

coefficients for industries and firms with all firms using IFRS, we find that stewardship

and valuation relevance of earnings are significantly positively related applying CEOs’

cash and total compensation data from the period 2006 to 2013 based on industry and

firm specific regressions and on three different market adjusted stock return measures.

For CEOs’ cash compensation, our firm specific findings are robust to a different panel

that includes years with a CEO change and years with no information about a CEO

change. Furthermore, we can confirm our positive findings for CEOs’ and boards’ cash

compensation in firm specific regression models that additionally control for ownership,

16

growth opportunities, risk and firm size. With average boards’ cash compensation we

can find support on firm level only. To sum up our findings, we find generally supportive

evidence for a positive association between stewardship and valuation relevance of

earnings for a sample of firms from the German setting.

Extending the scarce empirical research literature on the current debate about the

relationship between stewardship and valuation, our results show evidence for firms

preparing their financial statements according to IFRS, as one of the first studies analysing

this relationship outside the US. Our study provides support for prior results that were

based on US data (Bushman et al., 2006; Banker et al., 2009). We note that our data

not only extends current research by focusing on a different geographical area but also

considers compensation of the whole management board and is not limited to CEO

compensation data. At least in general, this should enable a more comprehensive view

on the relation between stewardship and valuation relevance. Moreover, in a multivariate

setting we include firm and governance variables to control for context in our analysis of

the relationship. As we show that these factors do not generally alter the relationship, this

provides further evidence on a positive relationship between stewardship and valuation in

our sample.

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19

A Tables

Table 1Construction of the sample

Sample selectionDAX, MDAX, TecDAX and SDAX firms during the fiscal year 2006 and 2013 1280

Less foreign firm year observations 80Less firm year obs. that are eliminated due to missing/incomplete compensation data 49Less firm year obs. applying US-GAAP 30Less firm year obs. with lack of data availability 91Less firm year obs. with missing lagged board compensation data 186

Panel B: Total firm year obs. with available change in average board compensation 844Less firm year obs. with missing lagged CEO compensation data 146

Panel B: Total firm year obs. with available change in CEO compensation 698

Panel B: Total firm year obs. with available change in average board compensation 844Less firm year obs. with a CEO replacement or with no information about a CEO replacement 186

Panel A: Total firm year obs. with available change in average board compensation 658

Panel B: Total firm year obs. with available change in CEO compensation 698Less firm year obs. with a CEO replacement or with no information about a CEO replacement 83

Panel A: Total firm year obs. with available change in CEO compensation 615

Table 2Industry membership (SIC codes-9 different SIC-groups)

One-digit SIC code Description of industry code Frequency01 Agriculture 715,16 Construction 1420,23,24,25,27 Manufacturing - Products (Food, Apparel, Furniture etc.) 3828,29 Manufacturing - Chemicals, Petroleum Refining 11130,32 Manufacturing - Plastic Products, Stone etc. 4033,34,35 Manufacturing - Metal, Machinery 14936 Manufacturing - Electronic 3737,38 Manufacturing - Transportation, measuring equipment 7042,45 Transportation - Motor freight, by air 2147,48,49 Transportation - Services, Communications 7350,51,52,53,59 Trade - Wholesale and Retail 6160,61,62 Finance 5663,64 Insurance 2065,67 Real Estate 5173,75,79,80,87 Services 8099 Public Administration 19

Total 847

20

Table 3Data description

Variable Description Source

MCAP The natural logarithm of market capitalization DatastreamMRET-15m Market-adjusted RET 15-months DatastreamMRET-12m Market-adjusted RET 12-months DatastreamMRET-12m3 Market-adjusted RET 12-months (3-months later shifted) DatastreamLEV Leverage (Total debt to common equity) DatastreamFirm size Logarithm of total assets DatastreamMTB Market to book ratio DatastreamBVPS Book value per share DatastreamNICON Net income (loss) - consolidated CompustatRET-12m Stock return (Change in Total Return Index) 12-months DatastreamSIC Standard Industry Classification code DatastreamFIX-BOARD Fixed salary board (in thousand) hand-collectedFB-BOARD Fringe benefits board (in thousand) hand-collectedSTI-BOARD Short term incentives board (in thousand) hand-collectedLTI-BOARD Long term incentives board (in thousand) hand-collectedFIX-CEO Fixed salary CEO (in thousand) hand-collectedFB-CEO Fringe benefits CEO (in thousand) hand-collectedSTI-CEO Short term incentives CEO (in thousand) hand-collectedLTI-CEO Long term incentives CEO (in thousand) hand-collectedNOBM Number of board members hand-collectedShare-Man Percentage of shares managers are holding of total firm shares hand-collectedShare-Fam Percentage of shares family is holding of total firm shares hand-collectedShare-Bank Percentage of shares banks are holding of total firm shares hand-collectedShare-Strat Percentage of shares strategic investors are holding of total firm shares hand-collectedShare-Inst Percentage of shares institutional investors are holding of total firm shares hand-collectedShare-Other Percentage of shares other investors are holding of total firm shares hand-collectedFree Float Percentage of shares holding in free float of total firm shares hand-collectedCC-CEO Percentage change in CEOs’ cash compensation calculatedTC-CEO Percentage change in CEOs’ total compensation calculatedCC-AVG Percentage change in average boards’ cash compensation calculatedTC-AVG Percentage change in average boards’ total compensation calculatedEARN Difference in NICON t and t-1 deflated by MCAP of year t-1 calculated

21

Table 4Summary statistics

Variable Obs Mean Std. Dev. Min MaxIndustry specificCC-CEO 15 0.02 0.05 -0.09 0.11TC-CEO 15 0.04 0.04 -0.05 0.11CC-AVG 15 0.01 0.04 -0.06 0.08TC-AVG 15 0.04 0.03 -0.03 0.10EARN 15 4.76e-06 8.36e-06 -0.00001 0.00002MRET-12m 15 -0.06 0.10 -0.30 0.06MRET-15m 15 -0.04 0.10 -0.30 0.14MRET-12m3 15 -0.04 0.08 -0.19 0.07RET-12m 15 0.08 0.10 -0.16 0.20Free Float 15 0.54 0.12 0.41 0.78Share-Insider 15 0.02 0.05 0 0.12Share-Outsider 15 0.19 0.07 0.10 0.36Share-Manager 15 0.001 0.004 0 0.02Share-Family 15 0 0 0 0Share-Bank 15 0.02 0.10 0 0.04Share-Strategic 15 0 0 0 0Share-Institutional 15 0.08 0.03 0.03 0.13Share-Other 15 0.02 0.08 0 0.31Leverage 15 1.00 1.14 0.15 4.76Firm size 15 8.29 1.62 6.85 12.32Market to book value 15 1.63 0.41 0.87 2.22Firm-specificCC-CEO 144 0.02 0.15 -0.51 0.65CT-CEO 144 0.06 0.16 -0.51 0.65CC-AVG 159 0.04 0.22 -0.46 2.06CT-AVG 159 0.06 0.18 -0.49 1.05EARN 159 -9.92e-06 0.0001 -0.0004 0.0001MRET-12m 159 -0.02 0.30 -0.93 1.30MRET-15m 159 -0.03 0.35 -1.04 1.55MRET-12m3 159 -0.05 0.32 -0.99 1.44RET12m 159 0.12 0.30 -0.79 1.44Free Float 183 0.51 0.21 0.06 1Share-Insider 182 0.16 0.23 0 0.94Share-Outsider 182 0.23 0.20 0 0.89Share-Manager 182 0.08 0.18 0 0.94Share-Family 182 0.08 0.17 0 0.71Share-Bank 182 0.06 0.17 0 0.89Share-Strategic 182 0.01 0.06 0 0.80Share-Institutional 181 0.10 0.11 0 0.61Share-Other 182 0.06 0.13 0 0.71Leverage 157 1.47 4.19 -1.81 37.56Firm size 183 7.61 2.08 4.11 14.51Market to book value 179 2.18 1.78 -2.79 10.49

CC (TC) defines the %-change in cash (total) compensation between year t and t-1.AVG is the average board compensation. MRET names the market-adjusted stockreturn, while RET names the stock returns. MRET is different for Panel A and B,because the market differs. This table provides an overview for Panel B. Free float,leverage, firm size, market to book value and all share variables are medians of i’s

industry/firm year observations.

22

Table 5Pearson correlation table

Panel B-Industry specificEARN MRET-12m RET-12m CC-CEO CC-AVG

EARN 1.0000MRET-12m 0.3107*** 1.0000RET12m 0.3107*** 1.0000*** 1.0000CC-CEO 0.4767*** 0.1082*** 0.1082*** 1.0000CC-AVG 0.3654*** 0.0018 0.0018 0.5184*** 1.0000Panel B-Firm specific

EARN MRET-12m RET-12m CC-CEO CC-AVGEARN 1.0000MRET-12m 0.2886*** 1.0000RET-12m 0.2886*** 1.0000*** 1.0000CC-CEO 0.1972*** -0.0151 -0.0151 1.0000CC-AVG 0.2295*** 0.1144*** 0.1144*** 0.6790*** 1.0000

The table shows correlations. Panel A describes the data that excludes firm years with aCEO change, while Panel B includes these years. This table uses Panel B. The

correlation for Panel B are in line with Panel A. This table distinguishes betweenindustry and firm specific median variables.

Significance Levels: *p< 0.05, ** p < 0.01, ***p < 0.001

Table 6Effect of earnings on stock market return

Dep. Var.: MRETMRET-12m MRET-15m MRET-12m3

EARN 550.5*** 625.9*** 339.7***(3.99) (3.91) (2.62)

Obs. 658 658 658R-sq. 0.388 0.395 0.453Adj. R-sq. 0.367 0.374 0.434

First regression:MRETi = α0 + V ECi · EARNi + α1D(t) + εi

Notes: The t-statistics are in parentheses. The sample period is 2006-2013. The Panelexcludes obs. with a CEO change. All regressions control for industry (15) and year

fixed effects, while the effects saved for the industry specific third regression only controlfor year fixed effects.

Significance Levels: *p< 0.1, ** p < 0.05, ***p < 0.01

23

Table 7Effect of earnings on compensation

Dep. Var.: CC CCCEO AVG

EARN 516.4* 414.4**(1.76) (2.36)

RET12m 0.501 0.131***(1.42) (2.66)

Obs. 615 658R-sq. 0.087 0.078Adj. R-sq. 0.052 0.045

Second regression:Ci = β0 + CECi · EARNi + β1RETi + β2D(t) + εi

Notes: The t-statistics are in parentheses. The sample period is 2006-2013. The Panelexcludes obs. with a CEO change. All regressions control for industry (15) and year

fixed effects, while the effects saved for the industry specific third regression only controlfor year fixed effects.

Significance Levels: *p< 0.1, ** p < 0.05, ***p < 0.01

Table 8Summary statistics of the VECs and CECs

Variable Obs Mean Std. Dev. Min MaxIndustry specificValuation coeff. (MRET-12m) 15 727.72 630.82 -334.12 1720.92Stewardship coeff.CASH-CEO 15 2718.32 5725.02 -771.26 22614.45CASH-AVG 15 730.19 1255.47 -1135.39 3140.23Firm specificValuation coeff. (MRET-12m) 93 2946.29 19932.22 -22118.7 153119.7Stewardship coeff.CASH-CEO 74 -1073.5 33947.7 -283921.8 28673.83CASH-AVG 80 2166.89 6859.11 -35256.71 23230.37

Notes: The table shows a summary statistics of the estimated values for stewardshipand valuation relevance of earnings (excluding obs. with a CEO change). At least sixfirm year observations estimate one firm specific coefficient. The abbreviation CASH

stands for cash compensation.

24

Table 9Pearson - Spearman rank - correlation

Pearson Spearman rank Pearson Spearman rankIndustry Industry Firm Firm

CEO 0.5810** 0.8036*** 0.4316*** 0.0975(0.0231) (0.0003) (0.0001) (0.4083)

AVG 0.1930 0.1571 0.3494*** 0.1313(0.4907) (0.5760) (0.0016) (0.2488)

Notes: The table shows correlations. The p-values are in parentheses. We use fixed timeeffects for industry specific regressions only, the 12-month market adjusted stock return

and cash compensation. This table applies the earnings coefficients of Table 8.Significance Levels: *p< 0.1, ** p < 0.05, ***p < 0.01

Table 10Industry and firm specific association between valuation and stewardship

relevance(with cash compensation and MRET-12m)

CEO AVG CEO AVGIndustry Industry Firm Firm

Valuation 5.272** 0.384 2.777*** 0.451***(2.57) (0.71) (4.06) (3.27)

Obs. 15 15 74 79R-sq. 0.338 0.037 0.186 0.122Adj. R-sq. 0.287 -0.037 0.175 0.111

All regressions estimate this equation: CECi = γ0 + V SC · V ECi + εiNotes: The t-statistics are in parentheses. The sample period is 2006-2013. These

regressions do not control for time fixed effects. The coefficients used in these thirdregressions are estimated in regression one and two industry and firm specific controllingfor time fixed effects in industry specific regressions only. Valuation earnings coefficient

are estimated with MRET-12m in regression one.Significance Levels:*p< 0.1,** p < 0.05,***p < 0.01

25

Table 11Firm specific association between valuation and stewardship relevance for

MRET12m and cash compensation including controls

(1) (2) (3) (4) (5) (6)CEC for CEO AVG CEO AVG CEO AVGVEC 2.866*** 0.409*** 2.966*** 0.418*** 2.314*** 0.383***

(3.88) (2.93) (4.20) (3.03) (3.48) (2.68)Free float 22650.4 6725.1*

(1.17) (1.87)share-in –18080.4 -5658.3

(-0.78) (-1.31)share-out -65068.3*** -10711.2**

(-2.82) (-2.49)share-man -604.7 4294.9

(-0.02) (0.60)share-fam -23689.9 -9083.3*

(-1.00) (-1.83)share-strat -159136.2*** -15565.4**

(-5.08) (-2.54)share-bank -9413.8 -4945.6

(-0.26) (-0.63)share-inst -15192.0 -7377.3

(-0.34) (-0.77)share-other -21884.0 -6172.4

(-0.81) (-1.10)Firm size 390.3 -286.6 33.45 -277.3 -644.8 -149.4

(0.17) (-0.68) (0.02) (-0.66) (-0.30) (-0.33)Leverage 848.9 -88.52 1472.3 -12.01 595.0 -272.4

(0.35) (-0.19) (0.62) (-0.03) (0.24) (-0.51)Market to book value 1340.1 728.6 11.41 616.3 -899.4 214.7

(0.47) (1.36) (0.00) (1.12) (-0.31) (0.36)Obs. 68 74 68 74 68 74R-sq. 0.219 0.180 0.297 0.211 0.474 0.264Adj. R-sq. 0.156 0.120 0.228 0.141 0.382 0.147

All regressions estimate this equation:CECi = γ0 + V SC · V ECi + γ1Firmi + γ2CorpGovi + εi

Notes: The t-statistics are in parentheses. The sample period is 2006-2013. The Panelexcludes obs. with a CEO change. We include several firm and ownership variables,

where each of them indicates the median for firm i (calculated from i’s firm yearobservations). Firm variables included in these regressions are firm size, leverage andmarket to book value. Corporate governance controls are free float, percentage firmshare of in- and outside investors and percentage firm share of different shareholdertypes. These shareholder types are manager (man), family (fam), strategic investors

(strat), bank (bank), institutional investors (inst) and other investors (other).Furthermore, we do not control for year fixed effects.Significance Levels:*p< 0.1,** p < 0.05,***p < 0.01

26

Table 12Industry specific association between valuation and stewardship relevance for

MRET12m and cash compensation including controls

(1) (2) (3) (4) (5) (6)CEC for CEO AVG CEO AVG CEO AVGVEC 5.642* 0.394 5.761* -0.0399 7.021* 0.434

(2.09) (0.82) (2.13) (-0.07) (2.13) (0.81)Free float -6500.5 6497.3**

(-0.42) (2.35)share-in -17558.9 -3583.5

(-0.49) (-0.45)share-out 9090.1 1987.6

(0.38) (0.37)share-man -52051.4 -81984.0

(-0.09) (-0.89)share-fam 0 0

(.) (.)share-strat 0 0

(.) (.)share-bank -49613.0 -76538.4*

(-0.23) (-2.17)share-inst -53247.9 26115.8

(-0.49) (1.49)share-other 7472.7 6731.6

(0.30) (1.65)Firm size -260.2 237.0 -574.6 528.5* -869.1 426.5

(-0.20) (1.01) (-0.49) (2.01) (-0.63) (1.89)Leverage 279.1 -55.07 281.8 -370.8 1180.4 -390.7

(0.17) (-0.19) (0.18) (-1.02) (0.58) (-1.19)Market to book value 2399.9 243.4 3841.9 739.3 759.1 1619.0

(0.58) (0.33) (0.77) (0.66) (0.11) (1.45)Obs. 15 15 15 15 15 15R-sq. 0.404 0.604 0.424 0.391 0.470 0.710Adj. R-sq. 0.073 0.385 -0.008 -0.065 -0.238 0.322

All regressions estimate this equation:CECi = γ0 + V SC · V ECi + γ1Firmi + γ2CorpGovi + εi

Notes: The t-statistics are in parentheses. The sample period is 2006-2013. The Panelexcludes obs. with a CEO change. We include several firm and ownership variables,

where each of them indicates the median for industries i (calculated from i’s firm yearobservations). Firm variables included in these regressions are firm size, leverage andmarket to book value. Corporate governance controls are free float, percentage firmshare of in- and outside investors and percentage firm share of different shareholdertypes. These shareholder types are manager (man), family (fam), strategic investors

(strat), bank (bank), institutional investors (inst) and other investors (other).Furthermore, we do not control for year fixed effects.Significance Levels:*p< 0.1,** p < 0.05,***p < 0.01

27

Table 13Industry and firm specific association between valuation and stewardship

relevance (with MRET-12m and total compensation)

CEO AVG CEO AVGIndustry Industry Firm Firm

Valuation 4.874** 0.700 2.956*** 0.369***(2.45) (1.50) (4.10) (2.77)

Obs. 15 15 74 79R-sq. 0.316 0.147 0.189 0.091Adj. R-sq. 0.263 0.081 0.178 0.079

All regressions estimate this equation: CECi = γ0 + V SC · V ECi + εiNotes: The t-statistics are in parentheses. The sample period is 2006-2013. Theseregressions do not control for time fixed effects. Valuation earnings coefficient are

estimated with MRET-12m in regression one and regression two uses totalcompensation. Significance Levels:*p< 0.1,** p < 0.05,***p < 0.01

28

Table 14Industry and firm specific association between valuation and stewardship

relevance (with Panel B, MRET12m, cash and total compensation)

IndustryC C T TCEO AVG CEO AVG

Valuation 0.771 0.951* 0.261 0.372(1.10) (1.91) (0.50) (0.66)

Obs. 15 15 15 15R-sq. 0.085 0.220 0.019 0.032Adj. R-sq. 0.014 0.160 -0.057 -0.042

FirmC C T TCEO AVG CEO AVG

Valuation 0.628*** 0.00850 0.754*** -0.239**(3.79) (0.12) (4.07) (-3.04)

Obs. 74 95 74 95R-sq. 0.166 0.000 0.187 0.090Adj. R-sq. 0.154 -0.011 0.176 0.080

All regressions estimate this equation: CECi = γ0 + V SC · V ECi + εiNotes: The t-statistics are in parentheses. The sample period is 2006-2013. Theseregressions do not control for time fixed effects. Valuation earnings coefficient are

estimated with MRET-12 in regression. C and T stand for cash and total compensation.These regression use Panel B that includes firm year observations with a CEO change

and years where we have no information about a CEO change.Significance Levels:*p< 0.1,** p < 0.05,***p < 0.01

29

Table 15Industry and firm specific association between valuation and stewardship

relevance for MRET15m and MRET12m3

IndustryPanel A

with MRET-15m with MRET-12m3C C T T C C T TCEO AVG CEO AVG CEO AVG CEO AVG

Valuation 3.558** -0.0989 3.433** 0.438 2.341* -0.0896 2.267* 0.448(2.61) (-0.27) (2.64) (1.39) (1.80) (-0.28) (1.83) (1.70)

Obs. 15 15 15 15 15 15 15 15R-sq. 0.343 0.006 0.350 0.129 0.200 0.006 0.206 0.181Adj. R-sq. 0.292 -0.071 0.300 0.062 0.139 -0.070 0.145 0.119

Panel Bwith MRET-15m with MRET-12m3

C C T T C C T TCEO AVG CEO AVG CEO AVG CEO AVG

Valuation -0.0392 0.447 -0.277 0.195 -0.559 0.181 -0.350 0.504(-0.06) (0.91) (-0.59) (0.38) (-1.08) (0.44) (-0.92) (1.26)

Obs. 15 15 15 15 15 15 15 15R-sq. 0.000 0.060 0.026 0.011 0.082 0.015 0.061 0.108Adj. R-sq. -0.077 -0.012 -0.049 -0.065 0.011 -0.061 -0.011 0.040

FirmPanel A

with MRET-15m with MRET-12m3C C T T C C T TCEO AVG CEO AVG CEO AVG CEO AVG

Valuation 1.639*** 0.263** 1.735*** 0.220** 1.255** 0.277** 1.349** 0.307**(2.93) (2.44) (2.94) (2.02) (2.04) (2.36) (2.08) (2.63)

Obs. 73 80 73 80 73 80 73 80R-sq. 0.108 0.071 0.108 0.050 0.055 0.067 0.057 0.081Adj. R-sq. 0.095 0.059 0.096 0.037 0.042 0.055 0.044 0.069

Panel Bwith MRET-15m with MRET-12m3

C C T T C C T TCEO AVG CEO AVG CEO AVG CEO AVG

Valuation 0.386*** -0.00545 0.497*** -0.200*** 0.353** 0.000918 0.418*** -0.207***(2.89) (-0.10) (3.34) (-3.18) (2.61) (0.02) (2.74) (-3.48)

Obs. 74 95 74 95 74 95 74 95R-sq. 0.104 0.000 0.134 0.098 0.087 0.000 0.095 0.115Adj. R-sq. 0.091 -0.011 0.122 0.088 0.074 -0.011 0.082 0.106

All regressions estimate this equation: CECi = γ0 + V SC · V ECi + εi

Notes: The t-statistics are in parentheses. The sample period is 2006-2013. Theabbreviations C and T stand for cash (C) and total (T) compensation. The left tablepart uses MRET-15m in regression one, while the right uses MRET-12m in regression

one. Panel A excludes obs. with a CEO change, while Panel B includes them and yearswhere we have no information about a CEO change.Significance Levels:*p< 0.1,** p < 0.05,***p < 0.01

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B Figures

Figure 1: Industry specific valuation-stewardship associations (with MRET12m)

Figure 2: Firm specific valuation-stewardship associations (with MRET12m)

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