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Towards a more accurate equity valuation

An empirical analysis

AutorStefan Henschke
VerlagGabler Verlag
Erscheinungsjahr2010
Seitenanzahl165 Seiten
ISBN9783834983428
FormatPDF
KopierschutzDRM
GerätePC/MAC/eReader/Tablet
Preis46,99 EUR
The accurate valuation of companies is essential for investors and managers. What appears to be straightforward from an academic perspective - discount expected future payoffs using adequate cost of capital - can be extremely difficult to implement. Using an empirical approach, Stefan Henschke investigates and improves the performance of different equity valuation methods. His research provides guidance for identifying inaccurate valuations and for improving the accuracy of valuations based on multiples.

Dr. Stefan Henschke received his doctor's degree at the University of Cologne, his supervisor was Prof. Dr. Carsten Homburg from the Department of Management Accounting.

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Leseprobe
6 Summary and conclusions (S. 145-146)

6.1 Summary of findings


This thes is investigates the valuation accuracy of equit y valuation methods. The main aims of this thesis are I) to empirically analyze the absolute and relati ve valuation errors of different equity valuation rnethod s, 2) to empiricall y analyze the determ inants of valu ation errors and 3) to improve the valuation accuracy of equity valuation methods. In Chapter 3, I address these research questions by reviewing the empirical literature on equity valuation methods. Overall , I find that all investigated equity valuation methods are inaccurate. Compared to observed stock prices even the most accurate equit y valuation methods yield mean absolute percentage errors of 20% and more .

Within this thesis I argue that the observed va luation errors are not fixed but rather can be attr ibuted to a number 01 influencing factors . The results of prior research indicate that intrinsic valuation methods appear to be inaccurate because of simpl ifications arising from the terminal value. Furtherrnore, prior research finds that different intrinsic valuat ion methods may yield different value estimates when the payoffs used by these methods are inconsistent to each oth er. However, when the forecasted futur e payoffs are consistent to each other, the methods yield the same value estimates and , therefore, perform identically.

On averag e, valu e estimates based on multipl es appear to yield valu e estimates which - compared to intrinsic valuation methods - are clos er to the observed mark et values.l" However, the results of prior literature indicate that the accuracy of multipl es appears to depend on the adequacy of the comparable firms . Finally, linear information models such as the Ohlson (1995) model appear to perform less accurate than mult iples or intrinsic valuation methods. lt appears that a one size fits it all approach to forecast futur e payoffs is not appropriate. lt is also unclear wheth er current implementations of linear information models are able to capture the effe cts of accounting conservatism.

In Chapt er 4, I investigate how differences betw een firms affect the valuation errors of the multiple valuation method. Based on the common approach to use industry membership to form peer groups, I find signific ant systematic errors in the value estimates 01 different value drivers. These systematic valuation error s are con sisten t to my hypotheses, statistically signifi cant, economically substantial, consistent between different value drivers and robust across time .
Inhaltsverzeichnis
Preface7
Acknowledgements9
Table of Contents10
List of Abbreviations14
List of Symbols16
List of Figures19
List of Tables20
1 Introduction22
1.1 Motivation22
1.2 Research objectives and outline of the thesis24
2 Valuing equity28
2.1 Overview on valuation methods28
2.2 The intrinsic valuation methods30
2.2.1 The dividend discount model30
2.2.2 The discounted cash flow model31
2.2.3 The residual income model34
2.3 The multiple valuation method36
2.4 Linear information models39
2.4.1 Introduction and motivation39
2.4.2 The Ohlson (1995) model39
2.4.3 T he l'e ltham/O hlson (1995) model41
2.4.4 T he Choi/O"l fa nlon/Pope (2006) mcdel45
2.5 Measuring valuation accuracy48
3 The accuracy of equity valuation methods54
3.1 Introduction and motivation54
3.2 What affects valuation errors?56
3.3 The valuation errors of intrinsic valuation methods60
3.4 The valuation errors of the multiple valuation method65
3.5 The valuation errors of linear information models74
3.6 Comparing the valuation errors of different valuation methods79
3.7 Conclusions81
4 Multiples: Controlling for differences between firms84
4.1 Introduction84
4.2 Relation to prior research86
4.3 Research design88
4.3.1 Theoretical considerations88
4.3.2 The impact of differences between firms90
4.3.3 Detecting differences between firms94
4.3.4 Controlling for ditferences between firms96
4.4 Sampie and data98
4.4.1 SampIe selection98
4.4.2 Descriptive statist ics100
4.5 Results102
4.5.1 The impact of differences between firms102
4.5.2 Detectin g differences between firm s103
4.5.3 Controlling for differences between firms105
4.5.4 Benchmarking to prior literature107
4.5.5 The impact of differences in industry111
4.6 Sensitivity analyses112
4.7 Conclusions120
5 Linear information models: The effects of conservative accounting123
5.1 Motivation and relation to prior research123
5.2 Resea rch desig n126
5,2.1 Model estimation126
5.2.2 Conservatism analyses129
5.2.2.1 Partition approach129
5.2.2.2 Delta regression131
5.3 Sample selection and sample characteristics135
5.4 Results136
5.4.1 Model estimations and out-of-sample forecasts136
5.4.2 Partition ana lyses137
5.4.3 Delta regressions139
5.4.4 Conservatism specific model estimation141
5.5 Sensltivity anelyses147
5.5.1 Alternative model spectüeauon using Fl'1tham/Oh lson (1995 )147
5.5.2 Alternative model specificat ion using Liu/Ohlson (2000)153
5.5.3 Adjusting for analyst forecast bias159
5.5.4 Further sensitivity tests160
5.6 Conclusions162
6 Summary and conclusions165
6.1 Summary of findings165
6.2 Research outlook167
Appendix 1: Compustat items170
References171

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