30
Finance, Accounting and Business Analysis
Volume 7 Issue 1, 2025
http://faba.bg/
ISSN 2603-5324
DOI: https://doi.org/10.37075/FABA.2025.1.03
Do key performance indicators derived from value-based management
better predict total stockholder return than traditional performance
indicators?
Matthias Olivier
1*
, Roland Wolf
2
UCAM Universidad Católica de Murcia, Murcia, Spain
1
FOM University of Applied Sciences for Economics and Management, Essen, Germany
2
* Corresponding author
Info Articles
Abstract
History Article:
Submitted 26 October 2024
Revised 4 January 2025
Accepted 20 February 2025
Purpose: This study investigates whether key performance indicators
derived from value-based management are able to better predict total
stockholder return than traditional performance indicators.
Design/Methodology/Approach: A sample (n = 1388) is drawn from
corporate indices in four European countries (France, Germany, Italy,
and Spain). The explanatory power of traditional performance indicators
and value-based performance indicators is compared with regard to total
stockholder return.
Findings: It is found that in the sample, value-based performance
indicators are not able to better explain total stockholder return than
traditional performance indicators.
Practical Implications: The results suggest that companies should
consider placing greater emphasis on performance indicators, as
leveraging both traditional and value-based performance metrics could
help improve understanding of stockholder returns and potentially drive
more informed strategic decision-making.
Originality/Value: The study provides insights into the relative
effectiveness of value-based performance indicators versus traditional
ones in explaining stockholder return across multiple European
countries.
Keywords: Value Based Management, Key Performance Indicators,
Value Oriented Performance Measurement, Value Accounting,
Paper Type: Research Paper.
Keywords:
Value Based Management,
key performance Indicators,
Value oriented performance
measurement, Value
Accounting.
JEL: G32; M41
*
Address Correspondence:
E-mail: molivier@alu.ucam.edu
1
roland.wolf@fom.de
2
Matthias Olivier, Roland Wolf / Finance, Accounting and Business Analysis, Volume 7, Issue 1, 2025
31
INTRODUCTION
Shareholder value is oftentimes considered the primary goal of any company in a free enterprise
system (Friedman 1970), the concept was formalized by Alfred Rappaport in his book "Creating Shareholder
Value: The New Standard for Business Performance" in 1986. Rappaport argues that ultimately the only
reliable way to evaluate management’s performance with regards to corporate strategy is the rate at which
shareholder value is created (Rappaport 1986). The key concept of Rappaport’s theoretical approach resulted
in the establishment of the value-based management as a management principle. The concept of value-based
management asserts that the primary guiding principle for management decisions is determined by
maximizing shareholder value. Therefore, all actives of the company should be aligned in a way to maximize
the value of the company (Weber et al. 2017). It is to note that the maximization of shareholder value does
not necessarily (or oftentimes not at all) mean the short-term maximization of company profits. The value-
based management principle much rather postulates that shareholder maximization is achieved when long-
term implications of company policy and management decisions are taken into consideration (Weber et al.
2017). While these observations brought about a fundamental change in the understanding of corporate
business strategies and today are considered a fundamental part of the body of knowledge in management
science, the operationalization of these concepts is an area that is continuously evolving (Wobst et al. 2025).
The operationalization of value-based management principles with the implementation of value-based
measures of performance measurement puts these value-based measures of performance measurement into
contrast to traditional performance indicators. This paper examines whether value-based performance
measures are used by the participants in the European capital market to make market decisions using a
sample of listed companies from France, Germany, Italy, and Spain.
KEY PERFORMANCE INDICATORS DERIVED FROM VALUE-BASED MANAGEMENT
Theoretical foundation of value relevance and empirical insights
The value relevance of performance indicators (both financial and non-financial) enables stake- and
shareholders to evaluate the performance of a company and is ultimately reflected in the performance at the
marketplace. This chapter summarizes the most discussed scientific research with regard to performance
indicators and details the developments of the theoretical background and empirical insights.
The theoretical background of the value relevance can be traced back to the efficient market
hypotheses based on the work of Fama (1970). The efficient market hypotheses states that the market price
of a stock represents fully the available information, including both financial and non-financial data. In an
efficient market all available information is already represented in the stock price and changes in the stock
price are caused by new facts that are able to change the current price. This theoretical concept can be seen
as empirically supported by the data analysis of Ball and Brown (1968) that showed the association of market
information and stock price reaction. This study is noteworthy because it highlighted the value relevance of
ad hoc capital market information. Based on these foundations numerous models were implemented. One
noteworthy model that was developed by Ohlson (1995) that shows the relationship between market value
and accounting information. Ohlson’s concept is based on the idea that the value of a company can be based
on a linear function of book values and earnings. These theoretical approaches have formed the basis that
most research is founded on to develop the approaches for value relevance further. Feltham and Ohlson
(1995) expanded these ideas by also including less secure factors into their equations. Most notably including
growth potential in their model and therefore focusing more on the future performance of a stock that is
represented by the current stock price. This extension has proven to be a cornerstone of the approach to
value performance as the stock price is considered to only represent future performance of a stock.
Empirical results regarding value relevance
The empirical research has shown consecutively that finical information like Net Income, EBIT,
EBITDA and Cashflow have a significant influence on market pricing, however the results regarding the
significance of individual factors has been the subject of a multifaceted debate and has led to a wide array of
insights.
Income is generally considered as being the most impactful performance indicator with regard to
value relevance, as Kothari and Zimmerman (1995) have shown in a conclusive literature review and
concluded that income is highly correlated with stock returns. This underscores the relevance that individual
investors give to actual and estimated income publications that can lead to abrupt changes in market prices,
especially if there is a difference between prior and current expectations. Collins et al. (1997) extended this
perspective by further increasing the time horizon of the investigation and observed that the value relevance
of income has been increasing at a slim rate over time, however that a corresponding slight increase in the
relevance of book value has off set this development when considering income and book value and income
Matthias Olivier, Roland Wolf / Finance, Accounting and Business Analysis, Volume 7, Issue 1, 2025
32
in combination. This observation is reinforced by the work of Penman and Sougiannis (1998) that showed
that the book value has a significant impact on stock prices. This might indicate that the book value is a
factor that is used to stabilize rapid changes in the estimated earnings and therefore makes models based on
earnings performance more robust when considered as an additional variable. This was further dissected by
Burgstahler and Dichev (1997) that noted that the situation of the company under consideration can
influence the performance measurement that proves too impactful, as for companies that show a history of
losses, book value becomes more relevant.
The significance of cashflows has increased over time, based on academic work of Dechow (1994)
and Barth et al. (1998) who argued that cashflows paint a clearer picture of operative performance than the
accrual accounting based performance indicators. This is considered to be especially true for industries and
sectors that have discretion in accrual-based accounting by using leeway granted by accounting standards
and auditors.
A different approach to the implication of historical performance was shown by focusing on the
dividends a company pays as a signaling instrument to show successful performance. As Lintner (1956)
coined the belief that stable dividends promise a stable performance. Based on these insights DeAngelo et
al. (2000) proved that dividend policy is an important tool for signaling. And also, may represent trust in
future performance.
The value relevance of non-financial performance indicators has increased in more recent years.
Gompers et al. (2003) developed a model to include corporate governance into the performance relevance
model and showed that a successful corporate governance structure is associated with a better performance.
Klein (2002) showed that independence in boards and audit commits can increase the financial performance.
Value based management as a management concept
The creation of shareholder value oftentimes lies at the heart of corporate strategy. The idea is fleshed
out by the concept of value-based management. Value based management is a way for the corporate strategy
department to put the maximization of shareholder value into the individual business units of the company.
The main idea is to align corporate strategy with the creation of shareholder value by viewing each decision
and action that is made within the company from the perspective of shareholder value. In other words, value-
based management means that the management of each individual business unit evaluates individual
decision from a perspective that puts creating shareholder value for the company as a whole as the top
priority (Weber et al. 2017).
At the beginning of any value-based management concept stands the idea of strategic planning. For
an implementation of a value-based management concept each department responsible for strategic planning
has to identify the value drivers from a strategic standpoint. The value drivers are individual factors that
influence the value of the company. Metaphorically speaking looking at the value drivers is like putting
shareholder value under the microscope to get a clearer picture of the individual elements of the value
creation process. Commonly considered value drivers are profitability, market share and revenue growth.
However, the identification of the value drivers in specific should go beyond these platitudes. Identification
of value drivers therefore has to be based on a rigorous data analysis of financial and operational data.
Based on these value drivers the company can derive long term goals for value creation by individually
setting goals for the value drivers. Subsequently, a corporate controlling that is focused on value creation
within the strategic units of the company is central to value based management. A corporate controlling that
adheres to the principles of value-based management promotes an approach that evaluates long term
cashflows from each strategic unit and discounts them using the appropriate cost of capital. Additional
shareholder value is created whenever the return of the investments exceeds the cost of capital. The common
denominator of all actions based on value-based management is that the company’s value is driven by
discounted future cash flows. The key differentiator of a value-based management approach is to align
decision making regarding strategic and operational decisions with the corresponding impacts on future
discounted cash flows. From a more practical perspective this means that capital is allocated to those units
of the company that promise the highest return on capital employed. Conversely, underperforming strategic
units are changed or discontinued. The bridge between these theoretical considerations of the value-based
management framework is built by the implementation of key performance indicators to evaluate the
strategic units and projects of the company.
Value based management and key performance indicators
The implementation of a value-based management system requires the selection of key performance
indicators that operationalize the value-based management approach (Martin et al. 2009). However, the
implementation of key performance indicators is oftentimes considered as the gateway to principal agent
conflicts. Agency theory is relevant in situations whenever a “principal” hires an “agent” to act on the
principal’s behalf (Gailmard 2014). The situation results in the principal-agent conflict. The conflict arises,
Matthias Olivier, Roland Wolf / Finance, Accounting and Business Analysis, Volume 7, Issue 1, 2025
33
because the agent takes actions in his own interest and not in the interest of the principal that he represents
(Jensen and Meckling 1976). The most important factor contributing to this conflict is the information
asymmetry between the principal and the agent. Most commonly considered are hidden actions and hidden
information. The hidden actions are due to the principal’s inability to monitor all of the agents’ actions in
detail and the agent is able to take actions that benefit the agent and may hurt the principal. The possibility
of hidden actions can lead to a moral hazard for the agent because the agent can be in a situation where an
action is beneficial to him but at the principal’s expense (Pauly 1968). Hidden Information is relevant due
to the fact that the agent has more and better access to information, as the agent is closer to the business
itself and might even be privy to some of the information, resulting in the principal being at an information
disadvantage. The possibility of hidden information can lead to an adverse selection for the principal,
because the information asymmetry might lead to an imbalance between the agent and the principal (Akerlof
1970). One of the most important tools to mitigate these problems in terms of the principal agent conflict is
the design and the contents of the contractual relationship between the principal and the agent (Jensen and
Meckling 1976). In an ideal situation the contract can be designed in a way that aligns the interests of the
principal and the agent. While there are ample scientific models to evaluate these considerations from a
theoretical perspective, the practical perspective is often concerned with the problem, how the success of the
agent is measured (Ali and Hwang 2000). As the measures of success are therefore a key element to the
mitigation of the principal agent conflicts, the analysis will look to the measures of success used for
performance measurement in order to highlight the challenges resulting from the principal agent conflict.
Traditional performance measures like Earnings and Revenue are criticized for lacking the alignment
between shareholder value and management performance. Value based management emphasizes the use of
key performance indicators that underscore the created value.
CONCEPT FOM
To enable a comparison between the predictive power of traditional performance indicators and
value-based performance indicators a standardized value concept is helpful. The standardization of a value-
based concept enables our research to incrementally develop the understanding of value-based performance
indicators. In this paper we therefore want to draw on the standardized approach that was developed by cfrv
(Center for Financial Reporting and Valuation) and FOM (Hochschule für Oekonomie & Management) to
determine value-oriented key performance indicators (Wolf 2017). In detail we identified four different
value-oriented performance indicators for use in our model.
Value Added (cfrv/FOM): determines the value added by subtracting total cost of capital from EBIT,
while total cost of capital is calculated using a WACC-approach. For comparability purposes, the value
added per share (cfrv/FOM) ratio is used.
Value rate (cfrv/FOM) per share determines the value-added rate by dividing the Value Added by the
capital used.
Price value ratio (cfrv/FOM): determines the ratio of the stock price to the added value.
Value performance ratio cfrv/FOM: determines the ratio of the Value rate (cfrv/FOM) to the Price
value ratio (cfrv/FOM)
For additional corroboration we used the value-based performance indicators ROCE, EVA and
Price/Value Ratio based on EVA that have been calculated in accordance with the industry standards.
The following research analyses whether novel ideas for value-based performance indicators are better
able to capture the actual value creation of the companies.
RESEARCH DESIGN
Performance indicators for analysis
For the analysis we have considered different performance indicators that might be suitable to explain
the change in the shareholder value. From an analytical perspective we grouped the performance indicators
into two subgroups that form the basis of our analysis. On the one hand we considered traditional
performance indicators, on the other hand we considered value-oriented performance indicators.
Among the group of traditional performance indicators, we made the following consideration with
regard to selection of performance indicators. We considered Revenue per share and return on sales to
include a top line perspective in the analysis. We included EBIT per share, EBIT margin, Earnings Before
Tax per share and Earnings per share to include the most commonly used indicators for economic success
in the accrual sense and expanded the selection with the CF margin for a more Cash oriented perspective.
To incorporate the perspective of traditional stock analysis we incorporated the P/E Ratio, return on equity
before tax, return on equity after tax, return on assets before tax and Tobin's Q. For the calculation of these
performance indicators, we were able to rely on traditional patterns for calculation.
With regard to the value-based performance indicators we use the cfvr/FOM approach outlined in
Matthias Olivier, Roland Wolf / Finance, Accounting and Business Analysis, Volume 7, Issue 1, 2025
34
the previous chapter and also included the EVA methodology to broaden the value-based approach of the
research.
Sample selection and data
As a basis for our sample of companies, we chose Stock Indices from four Continental European
countries (France, Germany, Italy, and Spain). We chose these four European countries, because they
represent a significant portion of the EU’s GDP (in total these four countries are responsible of about 60 %
of the EU’s GDP). The benchmark stock indices were chosen, because the biggest public companies are
oftentimes considered to be a benchmark to smaller companies as big public companies are at the pulse of
current developments in corporate strategy. As a result, we chose the French CAC 40, the German DAX
40, the Italian MIB and the Spanish IBEX 35 to give us the basic population for our analysis. In total we
had a number of 155 companies in our initial sample. We collected the financial information data for an
analysis period of ten years (2014 through 2023) to have a longer-term perspective on the development of
shareholder value to include a medium to long term perspective on the creation of shareholder value.
We employed Bloomberg Financial to retract the financial data of our sample. We corroborated the
data by verifying accuracy through comparisons with Refinitiv and if necessary, replacing missing data in
our sample. For the ten-year observation period we extracted a population of n = 1388 individual
observations. For the calculation of the performance indicators, we used standard calculation principles.
Table 1. Sample Composition
Index
No.
Possible
Observations
Exclusion due to missing
data
Individual
Observations
CAC 40
40
400
42
358
DAX 40
40
400
45
355
IBEX 35
35
350
34
316
MIB
40
400
41
359
Total
155
1550
136
1388
Source: Authors’ compilation
Table 2. Sample Structure
Index
Industry
Banking
Insurance
Other Sectors
Total
CAC 40
21
2
3
14
40
DAX 40
20
3
4
13
40
IBEX 35
16
6
3
10
35
MIB
22
4
3
11
40
Total
79
15
13
48
155
Source: Authors’ compilation
Models’ specification
To determine the predictive power of the different performance indicators, we use a fixed effects
model. The dependent variable is the total stockholder return (TSR). We have identified n = 1388
individually calculated performance indicators. We have grouped the performance indicators into two
groups. The first group of the performance indicators are traditional performance indicators that are based
on a traditional accrual-based approach towards performance measurement. The other group of
performance indicators are based on value-oriented management performance indicators.
Table 3. Dependent Variable
Dependent Variable
Variable Abbreviation
Total Stockholder Return
TSR
Source: Authors’ compilation
Matthias Olivier, Roland Wolf / Finance, Accounting and Business Analysis, Volume 7, Issue 1, 2025
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Table 4. Independent Variables: traditional Performance Indicators
Performance Indicators
Variable Abbreviation
Revenue per share
RpS
Return on sales
RoS
Ebit per share
EBITpS
Ebit margin
EBITM
CF margin
CFMAR
Earnings per share
EpS
P/E Ratio
PER
Ebt per share
EBTpS
Return on equity before tax
RoEbT
Return on equity after tax
RoEaT
Return on assets before tax
RoAbT
Tobin's Q
TQ
Source: Authors’ compilation
Table 5. Independent Variables: Value Oriented Performance Indicators
Performance Indicators
Variable Abbreviation
Return on capital employed
ROCE
Value added cfrv/FOM per share
VApS
Value rate cfrv/FOM per share
VRpS
Price value ratio cfrv/FOM
PVR
Value performance ratio cfrv/FOM
VPR
Economic value added per share
EVApS
Price Value ratio EVA
PEVAR
Source: Authors’ compilation
Table 6. Full definitions of the variables
Variable
Abbreviation
Variable Definition
TSR
((Ending Stock Price - Beginning Stock Price + Dividends Paid) / Beginning Stock
Price) × 100
RpS
Total revenue / Number of outstanding shares
RoS
(Operating income (Ebit) / Total revenue) × 100
EBITpS
EBIT / Number of Outstanding Shares
EBITM
(EBIT / Total Revenue) × 100
CFMAR
(Operating Cash Flow / Total Revenue) × 100
EpS
Net Income / Number of Outstanding Shares
PER
Share Price / Earnings per Share (EPS)
EBTpS
EBT / Number of Outstanding Shares
RoEbT
(EBT / Shareholders' Equity) × 100
RoEaT
(Net Income / Shareholders' Equity) × 100
RoAbT
(EBT / Total Assets) × 100
TQ
Market Value of Firm's Assets / Replacement Cost of Firm's Assets
ROCE
(EBIT / Capital Employed) × 100
VApS
(Cash Flow Return on Value / Fixed Operating Margin) / Number of Outstanding
Shares
VRpS
Cash Flow Return on Value / Fixed Operating Margin per Share
Matthias Olivier, Roland Wolf / Finance, Accounting and Business Analysis, Volume 7, Issue 1, 2025
36
Variable
Abbreviation
Variable Definition
PVR
hare Price / (Cash Flow Return on Value / Fixed Operating Margin)
VPR
(Cash Flow Return on Value / Fixed Operating Margin) × 100
EVApS
Economic Value Added / Number of Outstanding Shares
PEVAR
Share Price / Economic Value Added per Share
Source: Authors’ compilation
To determine the predictive power of the regression model we run the regressions individually for
each independent variable. The independent variables show the rate of change of an individual performance
indicator.
Table 7. Regression Model: traditional performance indicators
Variable Abbreviation
Regression Model
RpS
TSR = α + β*RpS+ ϵ
RoS
TSR = α + β*RoS+ ϵ
EBITpS
TSR = α + β*EBITpS+ ϵ
EBITM
TSR = α + β*EBITM+ ϵ
CFMAR
TSR = α + β*CFMAR+ ϵ
EpS
TSR = α + β*EpS+ ϵ
PER
TSR = α + β*PER+ ϵ
EBTpS
TSR = α + β*EBTpS+ ϵ
RoEbT
TSR = α + β*RoEbT+ ϵ
RoEaT
TSR = α + β*RoEaT+ ϵ
RoAbT
TSR = α + β*RoAbT+ ϵ
TQ
TSR = α + β*TQ+ ϵ
Source: Authors’ compilation
Table 8. Regression Model: value-oriented performance indicators
Variable Abbreviation
Regression Model
ROCE
TSR = α + β*ROCE+ ϵ
VApS
TSR = α + β*VApS + ϵ
VRpS
TSR = α + β*VRpS + ϵ
PVR
TSR = α + β*PVR + ϵ
VPR
TSR = α + β*VPR + ϵ
EVApS
TSR = α + β*EVApS+ ϵ
PEVAR
TSR = α + β*PEVAR+ ϵ
Source: Authors’ compilation
Building on the results of the simple regression models, a multiple regression model is developed that
incorporates the highest-ranked performance indicators to provide deeper insights. However, this approach
introduces the potential challenge of multicollinearity, which may affect the stability and interpretability of
the model's estimates. As a result, the variables chosen for the multiple regression will need to be reviewed
for the level of correlation before the regression is performed to mitigate this issue.
Matthias Olivier, Roland Wolf / Finance, Accounting and Business Analysis, Volume 7, Issue 1, 2025
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EMPIRICAL RESULTS
Results simple panel regression analysis
Descriptive statistics related to the variables of the research are presented in Table 8 and 9.
Table 9. Descriptive statistics independent variables
Abbreviation
N
Median
Mean
SD
RpS
1355
0.0701
0.0997
0.2717
RoS
1355
2.1130
4.4340
7.0671
EBITpS
1355
0.0983
0.1372
0.2520
EBITM
1355
0.1291
0.4722
12.7261
CFMAR
1355
1.1769
2.6272
5.7987
EpS
1355
13.6821
18.4380
116.8462
PER
1355
1.5835
3.5706
7.0446
EBTpS
1355
0.1374
0.1404
0.2134
RoEbT
1355
0.1048
0.1028
0.1754
RoEaT
1355
0.0535
0.0597
0.0629
RoAbT
1355
1.1360
1.4560
1.1384
TQ
1355
0.0780
0.0909
0.1171
ROCE
1355
0.1955
0.8783
4.3732
VApS
1355
0.0105
0.0227
0.1200
VRpS
1355
14.7566
32.3438
358.4022
PVR
1355
0.0127
-0.0626
5.4477
VPR
1355
-0.0503
-2.4621
22.7578
EVApS
1355
-1.6805
-117.1010
4,215.7867
PEVAR
1355
16.5466
40.7874
65.1478
Source: Authors’ compilation
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Table 10. Correlation Matrix
TSR
RpS
RoS
EBITpS
EBITM
CFMAR
EpS
PER
EBTpS
RoEbT
RoEaT
RoAbT
TQ
ROCE
VApS
VRpS
PVR
VPR
EVApS
PEVAR
TSR
1.0000
0.3935
0.1165
0.7088
0.1208
0.0807
0.6707
0.0316
0.7055
0.2039
0.2208
0.3579
0.2473
0.1803
0.5643
0.0264
0.0245
-0.0722
-0.0005
0.5584
RpS
0.3935
1.0000
-0.0836
0.7131
-0.1110
-0.0947
0.6897
0.0161
0.7025
0.0816
0.0884
-0.0195
0.0634
0.0268
0.2466
-0.0237
0.0115
-0.3158
0.0065
-0.0181
RoS
0.1165
-0.0836
1.0000
0.2340
0.9596
0.2659
0.2801
0.0115
0.2422
0.4185
0.3659
0.5058
0.3479
0.2953
0.2666
0.0298
0.4687
0.0169
-0.0438
0.1819
EBITpS
0.7088
0.7131
0.2340
1.0000
0.2207
0.0312
0.9862
0.0116
0.9974
0.3151
0.3150
0.3564
0.2936
0.2347
0.7359
-0.0008
0.1567
-0.1970
-0.0212
0.2282
EBITM
0.1208
-0.1110
0.9596
0.2207
1.0000
0.3170
0.2483
0.0105
0.2245
0.3803
0.3522
0.5025
0.3556
0.3012
0.2724
0.0319
0.4083
0.0310
-0.0551
0.2048
CFMAR
0.0807
-0.0947
0.2659
0.0312
0.3170
1.0000
0.0298
0.0012
0.0333
0.1030
0.0999
0.1919
0.1337
0.0824
0.0482
0.0345
-0.0047
0.0077
-0.0344
0.2131
EpS
0.6707
0.6897
0.2801
0.9862
0.2483
0.0298
1.0000
0.0038
0.9899
0.3453
0.3293
0.3661
0.3056
0.2448
0.7401
0.0008
0.1907
-0.2078
-0.0200
0.2156
PER
0.0316
0.0161
0.0115
0.0116
0.0105
0.0012
0.0038
1.0000
0.0102
0.0250
0.0260
0.0393
0.0283
0.0127
-0.0020
0.0137
0.0032
-0.0008
0.0017
0.0608
EBTpS
0.7055
0.7025
0.2422
0.9974
0.2245
0.0333
0.9899
0.0102
1.0000
0.3227
0.3218
0.3626
0.3010
0.2409
0.7438
0.0008
0.1591
-0.2024
-0.0227
0.2344
RoEbT
0.2039
0.0816
0.4185
0.3151
0.3803
0.1030
0.3453
0.0250
0.3227
1.0000
0.9781
0.6555
0.4393
0.3439
0.3968
0.0396
0.4377
0.0308
-0.0207
0.3687
RoEaT
0.2208
0.0884
0.3659
0.3150
0.3522
0.0999
0.3293
0.0260
0.3218
0.9781
1.0000
0.6464
0.4187
0.3142
0.3944
0.0410
0.3591
0.0353
-0.0238
0.3922
RoAbT
0.3579
-0.0195
0.5058
0.3564
0.5025
0.1919
0.3661
0.0393
0.3626
0.6555
0.6464
1.0000
0.6706
0.5222
0.5304
0.0492
0.4074
0.1088
-0.0266
0.6914
TQ
0.2473
0.0634
0.3479
0.2936
0.3556
0.1337
0.3056
0.0283
0.3010
0.4393
0.4187
0.6706
1.0000
0.8708
0.4340
0.0293
0.4005
-0.0018
-0.0134
0.4769
ROCE
0.1803
0.0268
0.2953
0.2347
0.3012
0.0824
0.2448
0.0127
0.2409
0.3439
0.3142
0.5222
0.8708
1.0000
0.4244
0.0172
0.4824
0.0231
-0.0119
0.2962
VApS
0.5643
0.2466
0.2666
0.7359
0.2724
0.0482
0.7401
-0.0020
0.7438
0.3968
0.3944
0.5304
0.4340
0.4244
1.0000
0.0229
0.2559
-0.0367
-0.0293
0.3333
VRpS
0.0264
-0.0237
0.0298
-0.0008
0.0319
0.0345
0.0008
0.0137
0.0008
0.0396
0.0410
0.0492
0.0293
0.0172
0.0229
1.0000
0.0039
0.0136
0.0014
0.0934
PVR
0.0245
0.0115
0.4687
0.1567
0.4083
-0.0047
0.1907
0.0032
0.1591
0.4377
0.3591
0.4074
0.4005
0.4824
0.2559
0.0039
1.0000
0.0066
-0.0031
0.0128
VPR
-0.0722
-0.3158
0.0169
-0.1970
0.0310
0.0077
-0.2078
-0.0008
-0.2024
0.0308
0.0353
0.1088
-0.0018
0.0231
-0.0367
0.0136
0.0066
1.0000
-0.0027
0.0715
EVApS
-0.0005
0.0065
-0.0438
-0.0212
-0.0551
-0.0344
-0.0200
0.0017
-0.0227
-0.0207
-0.0238
-0.0266
-0.0134
-0.0119
-0.0293
0.0014
-0.0031
-0.0027
1.0000
0.0017
PEVAR
0.5584
-0.0181
0.1819
0.2282
0.2048
0.2131
0.2156
0.0608
0.2344
0.3687
0.3922
0.6914
0.4769
0.2962
0.3333
0.0934
0.0128
0.0715
0.0017
1.0000
Matthias Olivier, Roland Wolf / Finance, Accounting and Business Analysis, Volume 7, Issue 1, 2025
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We ran 19 simple panel regression analysis for all the companies in the CAC, DAX, IBEX and MIB over
a ten-year time span. The simple regression analysis for the whole data set showed statistically significant results
in 2 out of 19 regression models at the 5% significance level between the performance indicator and Total
Shareholder Return (TSR) (as shown in Table 11 and 12). Divided by subgroups we found that out of the 12
traditional performance indicators 2 show a significant result at the 5% significance level between the
performance indicator and Total Shareholder Return (TSR). Out of the 7 value-oriented performance indicators
none show a significant result at the 5% significance level between the performance indicator and Total
Shareholder Return (TSR). We have ranked the 19-panel regression model by the predictive power (indicated
by r-squared). It is to note that Tobin’s Q shows the highest predictive power among the traditional performance
indicators. The highest ranked performance indicator from the group of the value-oriented performance
indicators is Value rate per share with rank 3. However, this result is not significant. To further deepen the
understanding of the predictive power we incrementally performed a two-factor regression analysis to better
understand if the combination of traditional performance indicators offers a higher predictive power.
Table 11. Independent Variables: traditional Performance Indicators
Performance Indicators
Variable
Abbreviation
Coefficient
Std.
Error
t-
Statistic
p-Value
R2
Rank
Revenue per share
RpS
0.5478
0.0306
17.9273
0.0000
0.2552
2
Return on sales
RoS
0.0002
0.0005
0.3500
0.7264
0.0001
9
EBIT per share
EBITpS
-0.0002
0.0013
-0.1707
0.8645
0.0000
16
EBIT margin
EBITM
-0.0004
0.0014
-0.3119
0.7552
0.0001
13
CF margin
CFMAR
-0.0001
0.0017
-0.0670
0.9466
0.0000
17
Earnings per share
EpS
0.0002
0.0007
0.3244
0.7457
0.0001
12
P/E RATIO
PER
-0.0009
0.0010
-0.8589
0.3906
0.0008
5
EBT per share
EBTpS
-0.0005
0.0019
-0.2455
0.8062
0.0001
15
Return on equity before tax
RoEbT
0.0002
0.0006
0.2960
0.7673
0.0001
14
Return on equity after tax
RoEaT
-0.0011
0.0016
-0.6864
0.4927
0.0005
6
Retrun on assets before tax
RoAbT
-0.0004
0.0013
-0.3304
0.7411
0.0001
11
Tobin's Q
TQ
1.1819
0.0530
22.2986
0.0000
0.3464
1
Source: Authors’ compilation
Table 12. Independent Variables: Value Oriented Performance Indicators
Performance Indicators
Variable
Abbreviation
Coefficient
Std.
Error
t-
Statistic
p-
Value
R2
Rank
Return on capital
employed
ROCE
-0.0004
0.0013
-0.3333
0.7390
0.0001
10
Value added per share
VApS
0.0008
0.0008
1.0202
0.3079
0.0011
4
Value rate per share
VRpS
0.0009
0.0007
1.2418
0.2146
0.0016
3
Price value ratio
PVR
0.0002
0.0003
0.6313
0.5280
0.0004
7
Value performance ratio
VPR
-0.0000
0.0000
-0.0320
0.9745
0.0000
19
EVA per share
EVApS
0.0000
0.0003
0.0565
0.9550
0.0000
18
Price Value ratio EVA
PEVAR
0.0001
0.0001
0.4487
0.6537
0.0002
8
Source: Authors’ compilation
Results multiple panel regression analysis
To better understand the interactions and the incremental knowledge from combining the individual
performance indicators we ran a combination of two-factor panel regression analysis. We compared the
predictive power of a two-factor regression model using the two highest ranked traditional performance indicator
with a two-factor regression model using the highest ranked performance indicator from the subgroup of
traditional performance indicator with the highest ranked performance indicator from the subgroup of value-
oriented performance indicators. As a multiple regression approach introduces the potential challenge of
multicollinearity the variables chosen for the multiple regression were reviewed for critical levels of correlation
before the regression is performed. However, the correlation between the two pairs of variables TQ/RpS
(correlation: 0.0634) and TQ/VRpS (correlation: 0.0293) did not reach a critical level.
We ran two multiple panel regression analysis for all the companies in the CAC, DAX, IBEX and MIB
Matthias Olivier, Roland Wolf / Finance, Accounting and Business Analysis, Volume 7, Issue 1, 2025
40
over a ten-year time span (as shown in Table 13 and 14). The multiple regression analysis for the whole data set
showed statistically significant results in both of the models at the 5% significance level (as shown in Table 13
and 14). Divided by subgroups we found that using the two highest ranked traditional performance indicators
shows a higher predictive power (indicated by r-squared) as using a combination of traditional and value-based
performance indicators. The results show that the predictive power using the two highest ranked traditional
performance indicators is higher than the predictive power of a model using the highest ranked traditional and
value-oriented performance indicators.
Table 13. Independent variables: traditional performance indicators
Independent variables
Variable
abbreviation
Regression model
Revenue per share, Tobin's Q
RpS, TQ
TSR = α + β₁*RpS + β₂* TQ+ ε
Tobin's Q, Value rate per share
TQ, VRpS
TSR = α + β₁*TQ + β₂* VRpS + ε
Source: Authors’ compilation
Table 14. Two factor regression model using the two highest ranked traditional performance indicators
Performance
indicators
Variable
Abbreviation
Coefficient
Std. Error
t-Statistic
p-Value
R2
Rank
Revenue per share
RpS
0.5584
0.0412
13.5623
0.00
0.36907
1
Tobin's Q
TQ
23.2050
1.4604
15.8890
0.00
Source: Authors’ compilation
Table 15. Two factor regression model using the highest ranked traditional and value-oriented performance
indicators
Performance
Indicators
Variable
Abbreviation
Coefficient
Std. Error
t-Statistic
p-Value
R2
Rank
Tobin's Q
TQ
28.7588
1.5180
18.9455
0.0000
0.26019
2
Value rate per
share
VRpS
18.9156
8.7410
2.1640
0.0307
Source: Authors’ compilation
DISCUSSION
This study examines the role that key performance indicators play in changes in total stockholder return.
Specifically, the paper examines the differences in traditional performance indicators and value-oriented
performance indicators. Based on an extensive dataset of European companies listed in standard indices – the
CAC 40, DAX 40, MIB and IBEX 35 – and the analysis for data over a time period of 10 years we examined
the predictive power of traditional and value-based performance measures for total stockholder return. Of the
19 analyzed performance indicators 2 traditional KPIs showed a significant prediction ability for the TSR while
none of the 7 value-based performance indicators showed a similar significance. Among the traditional
performance indicators Tobin’s Q was the strongest predictor for TSR. In contrast the value-oriented indicators
were not significant, so we cannot assume any predictive power.
These results imply that traditional performance indicators still play an important role in explaining total
shareholder return despite the theoretical advantages that value based performance indicators could have. This
might be caused by the established processes to evaluate these indicators and the availability of the data for these
indicators for investors and analysts. An additional role might play, that the traditional indicators offer a more
straight forward approach in interpreting the performance of a company.
Our results are in line with a current study that examined the efficacy of value-based indicators in relation
to TSR prediction. A study of Makhija and Trivedi (2021), that examined a sample of Indian-listed companies
analyzed that performance indicators like Economic Value Added (EVA) and Cash Value Added (CVA) offer
noteworthy insights into a company but do not offer the same precise prediction ability as traditional
performance indicators. The authors Hauser et al. (2022) conclude similar results, that traditional performance
indicators like return on capital invested and earnings per share correlate stronger with market reactions in short
to medium time horizons, especially if market conditions are volatile. While these analyses found varying
degrees of predictive power of the value-oriented performance indicators the strong focus lay on the EVA model.
In contrast to this our research focuses on novel performance indicators that incrementally build on
previous studies that have shown results using a more differentiated approach regarding value-oriented
Matthias Olivier, Roland Wolf / Finance, Accounting and Business Analysis, Volume 7, Issue 1, 2025
41
performance indicators. Previous studies into the concept of novel performance indicators have shown, that the
predictive power of novel performance indicators offers predictive powers that lie between 7,7 and 19,4 % (see
table below).
Table 16. Independent variables: traditional performance indicators
Previous research
Value oriented performance indicator
R-Squared of Model
Kümpel et al. (2021a)
Value added and Value Rate (cfrv/FOM)
0.1937
Kümpel et al. (2021b)
Price Value Ratio (cfrv/FOM)
0.077
Source: Authors’ compilation
The findings in this paper show that value-oriented performance indicators have less predictive power
than traditional performance indicators. The cause for the relatively high predictive power of traditional
performance indicators can be caused by a number of factors. However, one explanation might be that value-
based management principles have not penetrated the approaches to strategic controlling as much as one would
expect in light of the popularity of the shareholder-based management approach. This could lead to the
conclusion that an increase in shareholder value could be possible if value-based management approaches were
applied more widely in practice. Further research is necessary to evaluate which reasons are responsible reserved
attitudes toward the application of these models.
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