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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.edu1 

  roland.wolf@fom.de2 

 

 
  

http://faba.bg/
https://doi.org/10.37075/FABA.2025.1.03
https://orcid.org/0009-0000-2288-4001
https://orcid.org/0009-0009-5282-8815


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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 



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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, 



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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 



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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 
  



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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 



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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.  

  



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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 



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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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