Accouting for trends : Relevance, Explanatory and Predictive Power of the Framework of Triple-Entry Bookkeeping and Momentum Accounting of Yuji Ijiri

336 Pages·2008·9.257 MB·English

by  Eric Melse

Drs Eric Melse MBA, assistant professor Business Intelligence bij het Nyenrode Strategy Center,
promoveert op vrijdag 3 oktober, 12.00uur, in de AULA van de Universiteit Maastricht,
Minderbroedersberg 4-6, 6211 LK, Maastricht, op zijn proefschrift getiteld:
Accounting for Trends.
Relevance, explanatory and predictive power of the framework of triple-entry bookkeeping and
momentum accounting of Yuji Ijiri.
Managers en investeerders wensen toekomstgerichte informatie in de (externe) verslaggeving
van ondernemingen. De inzet van momentum accounting maakt waardecreatie zichtbaar met een
nieuwe financiële ratio die werkt als een snelheidsmeter op het corporate dashboard. Het blijkt
mogelijk om met momentum accounting modellen vrij nauwkeurige uitspraken te doen over de
ontwikkeling van het eigen vermogen in de toekomst van ondernemingen genoteerd aan de Dow
en de AEX. De Dow Jones index wordt in deze studie correct voorspeld met spectramap
analyses. De ontwikkeling van het eigen vermogen van ondernemingen kan worden gevolgd en
voorspeld aan de hand van de volatiliteit van het momentum van winst en balansvermogen.
Trefwoorden:
momentum accounting, spectramap, toekomstgerichte informatie, AEX, Dow Jones index
Telefoonnummer: 06-10934901
Telefaxnummer: 070-4140756
Emailadres: [email protected]; [email protected]; [email protected]
Accounting for Trends

Accounting for Trends
Accounting for Trends
Relevance, Explanatory and Predictive Power of the Framework of Triple-Entry
Bookkeeping and Momentum Accounting of Yuji Ijiri
Copyright © E. Melse, Rijswijk 2008
Proefschrift Universiteit Maastricht
isbn 978-90-9022105-2
Cover Melse meets Lewi, 2008
Intentio Ellen Hoogeveen, pictor Eric Melse, tractus Spectramap™
Statio Manifesta, Rotterdam, photography Tom Croes, Schelluinen
Printed by Datawyse, Maastricht
Accounting for Trends

Accounting for Trends
Relevance, Explanatory and Predictive Power of
the Framework of Triple-Entry Bookkeeping &
Momentum Accounting of Yuji Ijiri
PROEFSCHRIFT
Ter verkrijging van de graad van doctor aan de Universiteit Maastricht,
op gezag van de Rector Magnificus, prof.mr. G.P.M.F. Mols,
volgens het besluit van het College van Decanen in het openbaar te verdedigen
op vrijdag 3 oktober 2008 om 12.00 uur
door
Eric Melse
Promotores
Prof.dr. A.M.M. Blommaert
Prof.dr. E.H.J. Vaassen
Beoordelingscommissie
Prof.dr. J. Hunton (voorzitter, Bentley College)
Prof.dr. J.M.J. Blommaert (Universiteit van Tilburg, Universiteit Leiden)
Prof.dr. E.A. de Groot (Nyenrode Business Universiteit)
Prof.dr. H.F.D. Hassink RA

Financial support was received from the Nyenrode Research Institute, Breukelen and
from Conquaestor BV, Amsterdam.
Accounting for Trends

Table of contents—concise

1 Introduction 1
2 Research methodology 47
Part I Data Analysis 77
3 Accounting in three dimensions. A case for momentum. 79
4 Decomposition analysis within the tema framework 93
5 Spectramap decomposition of wealth, momentum & force 117
6 Color coding of accounting information 135
Part II Time Series Analyses 151
7 Explanatory & predictive power of momentum accounting – the aex 153
8 Explanatory & predictive power of momentum accounting – the Dow 173
9 Proportional Change of the Balance Sheet of 3M 193
10 Momentum accounts as leading indicators of the Dow 211
11 Summary & conclusion 243
12 Summary in Dutch — Nederlandse samenvatting 259
Appendix 279
Accounting for Trends

Table of contents
1 Introduction 1
1.1 Introduction 3
1.2 Accounting for disclosure, analysis or decision? 4
1.2.1 Points past 5
1.2.2 Management by momentum 5
1.3 Three dimensional accounting 7
1.3.1 Quantitas materiae 7
1.3.2 Dimensions of accounting measurement 8
1.3.3 Force accounting & the controller 9
1.3.4 Impulse & action accounting 10
1.3.5 Illustration 10
1.3.6 Other accounting analogies with physics 11
1.4 Advancement of tema 13
1.5 Value creation, measurement & management 14
1.5.1 Relevance lost 14
1.5.2 Relevance recovered 15
1.5.3 Value drivers 15
1.5.4 A dynamic view 17
1.5.5 The measurement of economic value 18
1.5.6 Eva® 19
1.5.7 Tema, vbm & eva® 20
1.5.8 Eva® calculation 21
1.5.9 Eva® & valuation 22
1.5.10 The explanatory power of eva® 23
1.5.11 Eva® & the tema framework 24
1.6 Criticisms 25
1.6.1 Dimensional analysis & Ijiri’s framework 25
1.6.2 Administration or confusion? 26
1.6.3 A general relationship? 27
1.6.4 Information by accident? 28
1.6.5 Corroboration or contradiction? 28
1.7 Research objective 28
1.8 Research hypotheses 29
1.8.1 A general relationship between wealth, momentum & force 29
1.8.2 Explanatory power 32
1.8.3 Predictive power 33
1.8.4 Forecasting with factors 34
1.9 Research motivation 34
1.9.1 The ‘new economy’ – is it real-time? 34
1.9.2 Accounting Information Systems 35
1.9.3 The rea framework 37
1.9.4 Virtual close, xbrl & the continuous audit 38
1.9.5 Corporate governance: disclosure & transparency 40
1.9.6 Strategic accounting & auditing 43
1.10 Research contribution 45
1.11 Structure of the dissertation 45
2 Research methodology 47
2.1 Introduction 49
2.2 Research material 50
2.2.1 The research matrix 50
2.2.2 Connectedness of the hypotheses in the research studies 51
2.2.3 Data samples & time granularity 51
2.3 Time series analysis 52
2.3.1 Accounting measurement of change 52
2.3.2 Trend behavior 55
Accounting for Trends

2.3.3 Arima modeling 56
2.3.4 Differencing 57
2.3.5 Balancing 57
2.3.6 Cointegration 58
2.3.7 Independent variables 58
2.3.8 Force equations 59
2.3.9 Regression analysis, the explanatory power of momentum accounting 61
2.3.10 3M, force equations 62
2.3.11 Diagnostic testing of equation residuals 64
2.3.12 Dynamic tema model — ex post simulation of 3M 67
2.3.13 Hold-out sample, static & dynamic forecasting 67
2.3.14 Dynamic tema model — ex ante simulation of 3M 69
2.3.15 Discussion 70
2.4 Visual analytics 71
2.4.1 Data reduction 71
2.4.2 Spectral map analysis 72
2.5 Color coding 73
2.5.1 Number perception 73
2.5.2 Color perception 74
2.5.3 Cielab color space 74
2.5.4 Color coding of data 75
2.5.5 Impairment 75
2.6 Conclusion 76
Part I Data Analysis 77
3 Accounting in three dimensions. A case for momentum. 79
3.1 Introduction 81
3.2 Financial ratio analysis 81
3.2.1 Rota, roe & sales margin 83
3.2.2 Rota disaggregated 83
3.3 Force & momentum ratios 84
3.3.1 Point or period measurement 84
3.3.2 Unitless & timeless ratios 85
3.3.3 Common size momentum ratio 85
3.4 Profitability 87
3.4.1 The common size format net wealth momentum ratio 87
3.4.2 Balance sheet dynamics by foliomap 91
3.4.3 Performance dynamics by scatter plot 91
3.5 Discussion 92
4 Decomposition analysis within the tema framework 93
4.1 Introduction 95
4.2 Measures of decomposition 96
4.2.1 Rationale 96
4.2.2 Entropy 97
4.2.3 Information 97
4.2.4 Decomposition measures 98
4.2.5 Critique of entropy based informational measures 99
4.2.6 Applications of entropy based informational measures 99
4.3 A measure of disaggregation 101
4.3.1 Dealing with the direction of change 101
4.3.2 The disaggregation measure 101
4.3.3 Disaggregation measures of income & wealth 102
4.3.4 Disaggregation measures of momentum & force 106
4.4 Decomposition measures of 3M 107
4.4.1 Measurement of wealth decomposition 107
4.4.2 Color coding of informational measures 111
4.4.3 Color coding of momentum & force measures 112
4.5 Discussion 113
Accounting for Trends

5 Spectral map decomposition of wealth, momentum & force 117
5.1 Introduction 119
5.2 Spectral map analysis 120
5.2.1 Wealth decomposition 120
5.2.2 Association 123
5.2.3 Calibration 123
5.2.4 Decomposition of momentum & force measures 123
5.2.5 Bivariate & trivariate mapping of wealth momentum 127
5.2.6 Total wealth momentum 131
5.3 Discussion 132
6 Color coding of accounting information 135
6.1 Introduction 137
6.2 Wealth and the balance sheet 138
6.2.1 The generality assumption 138
6.2.2 Relevance of balance sheet information 139
6.2.3 Data sample 139
6.2.4 Data analysis 141
6.3 Spectral map analysis 141
6.4 Color coding of balance sheet items 146
6.4.1 Higher factor’s contribution 146
6.5 Five dimensional analysis 148
6.6 Conclusion 149
Part II Time Series Analyses 151
7 Explanatory & predictive power of momentum accounting – the aex 153
7.1 Introduction 155
7.2 Royal Philips Electronics N.V. 157
7.2.1 Econometric tests 157
7.2.2 Regression models 159
7.2.3 Test statistics 159
7.2.4 Arima models 165
7.2.5 Simulation ex post 166
7.2.6 Simulation ex ante 166
7.3 The aex 167
7.3.1 Directional change 167
7.3.2 Simulation ex post & ex ante 172
7.4 Conclusion 172
8 Explanatory & predictive power of momentum accounting – the Dow 173
8.1 Introduction 175
8.2 Research question & hypotheses 176
8.2.1 Association 176
8.2.2 Research hypotheses 176
8.2.3 Quarterly financial statements’ data 177
8.2.4 Research design 177
8.3 Directional change analysis 177
8.3.1 Momentum & force 178
8.3.2 Visual analysis 181
8.3.3 Association statistics 183
8.3.4 Discussion 183
8.4 Time series analysis of Dow component companies 184
8.4.1 Regression models 184
8.4.2 Test statistics 186
8.4.3 Ex ante forecasts 189
8.5 Discussion 192