Henschke Towards a more accurate equity valuation

An empirical analysis
2009
ISBN: 978-3-8349-8342-8
Verlag: Betriebswirtschaftlicher Verlag Gabler
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Deutsch, 165 Seiten, eBook

Reihe: Quantitatives Controlling

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.

Zielgruppe

Research

Autoren/Hrsg.

Henschke, Stefan

Inhaltsverzeichnis

1;Preface;7
2;Acknowledgements;9
3;Table of Contents;10
4;List of Abbreviations;14
5;List of Symbols;16
6;List of Figures;19
7;List of Tables;20
8;1 Introduction;22
8.1;1.1 Motivation;22
8.2;1.2 Research objectives and outline of the thesis;24
9;2 Valuing equity;28
9.1;2.1 Overview on valuation methods;28
9.2;2.2 The intrinsic valuation methods;30
9.2.1;2.2.1 The dividend discount model;30
9.2.2;2.2.2 The discounted cash flow model;31
9.2.3;2.2.3 The residual income model;34
9.3;2.3 The multiple valuation method;36
9.4;2.4 Linear information models;39
9.4.1;2.4.1 Introduction and motivation;39
9.4.2;2.4.2 The Ohlson (1995) model;39
9.4.3;2.4.3 T he l'e ltham/O hlson (1995) model;41
9.4.4;2.4.4 T he Choi/O"l fa nlon/Pope (2006) mcdel;45
9.5;2.5 Measuring valuation accuracy;48
10;3 The accuracy of equity valuation methods;54
10.1;3.1 Introduction and motivation;54
10.2;3.2 What affects valuation errors?;56
10.3;3.3 The valuation errors of intrinsic valuation methods;60
10.4;3.4 The valuation errors of the multiple valuation method;65
10.5;3.5 The valuation errors of linear information models;74
10.6;3.6 Comparing the valuation errors of different valuation methods;79
10.7;3.7 Conclusions;81
11;4 Multiples: Controlling for differences between firms;84
11.1;4.1 Introduction;84
11.2;4.2 Relation to prior research;86
11.3;4.3 Research design;88
11.3.1;4.3.1 Theoretical considerations;88
11.3.2;4.3.2 The impact of differences between firms;90
11.3.3;4.3.3 Detecting differences between firms;94
11.3.4;4.3.4 Controlling for ditferences between firms;96
11.4;4.4 Sampie and data;98
11.4.1;4.4.1 SampIe selection;98
11.4.2;4.4.2 Descriptive statist ics;100
11.5;4.5 Results;102
11.5.1;4.5.1 The impact of differences between firms;102
11.5.2;4.5.2 Detectin g differences between firm s;103
11.5.3;4.5.3 Controlling for differences between firms;105
11.5.4;4.5.4 Benchmarking to prior literature;107
11.5.5;4.5.5 The impact of differences in industry;111
11.6;4.6 Sensitivity analyses;112
11.7;4.7 Conclusions;120
12;5 Linear information models: The effects of conservative accounting;123
12.1;5.1 Motivation and relation to prior research;123
12.2;5.2 Resea rch desig n;126
12.2.1;5,2.1 Model estimation;126
12.2.2;5.2.2 Conservatism analyses;129
12.2.2.1;5.2.2.1 Partition approach;129
12.2.2.2;5.2.2.2 Delta regression;131
12.3;5.3 Sample selection and sample characteristics;135
12.4;5.4 Results;136
12.4.1;5.4.1 Model estimations and out-of-sample forecasts;136
12.4.2;5.4.2 Partition ana lyses;137
12.4.3;5.4.3 Delta regressions;139
12.4.4;5.4.4 Conservatism specific model estimation;141
12.5;5.5 Sensltivity anelyses;147
12.5.1;5.5.1 Alternative model spectüeauon using Fl'1tham/Oh lson (1995 );147
12.5.2;5.5.2 Alternative model specificat ion using Liu/Ohlson (2000);153
12.5.3;5.5.3 Adjusting for analyst forecast bias;159
12.5.4;5.5.4 Further sensitivity tests;160
12.6;5.6 Conclusions;162
13;6 Summary and conclusions;165
13.1;6.1 Summary of findings;165
13.2;6.2 Research outlook;167
14;Appendix 1: Compustat items;170
15;References;171

Valuing equity.- The accuracy of equity valuation methods.- Multiples: Controlling for differences between firms.- Linear information models: The effects of conservative accounting.- Summary and conclusions.

Leseproben

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 .

Über Autor(innen)

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.