Econophysics and companies: statistical life and death in complex business networks
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| Format: | Buch |
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| Sprache: | English |
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Cambridge [u.a.]
Cambridge Univ. Press
2010
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| Ausgabe: | 1. publ. |
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| Online-Zugang: | Cover Inhaltsverzeichnis |
| Beschreibung: | XXVI, 234 S. Ill., graph. Darst. |
| ISBN: | 9780521191494 |
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MARC
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| 245 | 1 | 0 | |a Econophysics and companies |b statistical life and death in complex business networks |c Hideaki Aoyama ... |
| 250 | |a 1. publ. | ||
| 264 | 1 | |a Cambridge [u.a.] |b Cambridge Univ. Press |c 2010 | |
| 300 | |a XXVI, 234 S. |b Ill., graph. Darst. | ||
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| 650 | 0 | |a Economics / Methodology | |
| 650 | 0 | |a Statistical physics | |
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| 700 | 1 | |a Aoyama, Hideaki |e Sonstige |4 oth | |
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Datensatz im Suchindex
| _version_ | 1804142929666637824 |
|---|---|
| adam_text | Titel: Econophysics and companies
Autor: Aoyama, Hideaki
Jahr: 2010
Contents
List of figures
List of tables
About the authors
Foreword
Preface
Prologue
page ix
xv
xvi
xix
xxi
xxiii
New insights
1.1 A scientific approach
1.1.1 Science of complex systems
1.1.2 The emergence of econophysics
1.2 Distributions and fluctuations
1.3 Are networks complex?
1.4 Change in the environment surrounding companies
1.4.1 Outline of the Japanese electrical and electronics and
automobile industries
1.4.2 The electrical and electronics industry
1.4.3 The automobile industry
1.4.4 Industrial structures and business networks
Size distribution
2.1 Preliminaries
2.1.1 Flow s and stocks
2.1.2 Size distribution and Pareto s law
2.1.3 Other distributions with a long tail
Distribution of personal income
2.2.1 Income distribution and Pareto s law
Distribution of companies
2.3.1 Size distribution of companies
2.3.2 Size of European companies
2.3.3 A caveat: sample and true distributions
~ i
:.3
3
3
4
6
7
8
9
10
14
14
14
15
21
21
22
26
26
28
30
Contents
2.4 Pareto s law 33
2.4.1 Gini and Robin Hood 35
2.4.2 Simulation: the inverse-function method 39
2.4.3 Devil s Staircase 40
2.4.4 Oligopoly and monopoly 42
2.4.5 Pareto s 80-20 rule 46
2.4.6 The fractal dimension 51
2.5 Side story: long-tail phenomena 54
2.6 p = 1 and phase transition 56
Company growth as fluctuations 59
3.1 Gibrat s law and detailed balance 60
3.1.1 Growth-rate and Gibrat s law 61
3.1.2 Data for Japanese companies 64
3.1.3 Data for European companies 65
3.1.4 Gibrat revisited 67
3.1.5 Detailed balance 69
3.1.6 Relation between Pareto s and Gibrat s laws and the
detailed balance 72
3.1.7 Copulas 74
3.2 Digression: personal income fluctuation 78
3.2.1 Gibrat s law and detailed balance 78
3.2.2 Breakdown of the laws 80
3.2.3 Side story: public notice of high-tax payers, and
lost data in Japan 81
3.3 Small and medium-sized companies 83
3.3.1 Large-scale data for small and medium-sized enterprises 83
3.3.2 Size dependence of growth 84
3.4 Companies bankruptcy 87
3.4.1 Companies activity and bankruptcy 87
3.4.2 Lifetime and debt at bankruptcy 88
3.5 The production function and ridge theory 91
3.5.1 The production function 91
3.5.2 Ridge theory for companies growth 92
Complex business networks 99
4.1 Introduction to network science 99
4.2 1. 2, 3,.... 6 degrees of separation 106
4.3 Networks in the economy 111
4.3.1 The shareholding network 113
4.3.2 The interlocking directors network 115
4.3.3 The transaction network 116
4.3.4 The innovation network 118
Contents vii
4.4 Network indices 121
4.4.1 Degree centrality 122
4.4.2 Shortest path length 123
4.4.3 Clustering coefficient 123
4.4.4 The betweenness centrality of nodes 125
4.4.5 Cliques 125
4.5 Statistical properties of network indices 126
4.5.1 Comparison of industries by using network
indices 126
4.5.2 Degree distribution 128
4.5.3 Correlations related to degree 131
4.5.4 The shareholding network and company size 133
4.6 Dynamics of the company network 136
4.6.1 Change in the shareholding network 136
4.6.2 Change of degree distribution 139
4.6.3 Correlation between companies in networks 143
An agent-based model for companies 152
5.1 Gibrat s process 152
5.2 Model of the shareholding network 154
5.2.1 Reproduction of size distribution 155
5.2.2 Reproduction of degree distribution 156
5.2.3 Effects of nodal characteristics 157
5.3 Balance sheet dynamics 158
5.3.1 The basic agent model 159
5.3.2 Representative agents 163
5.3.3 Reduction to a multiplicative process 164
5.3.4 Distribution of company sizes 165
5.3.5 Synchronised bankruptcy 168
5.4 Network effects on wealth distribution 169
5.4.1 Model construction 170
5.4.2 Network effects 170
5.4.3 Clustering of wealth 172
5.5 Modelling the transaction network 175
5.5.1 Autonomous companies 175
5.5.2 Model of bounded rationality 180
Perspectives for practical applications 184
6.1 Development of business strategies 184
6.1.1 Valuation of companies 184
6.1.2 Optimum capital structure 188
6.1.3 Decision-making for business entry and exit 190
6.1.4 Decision-making under a given economic trend 193
Contents
6.2 Chain bankruptcy and credit risk 196
6.2.1 Transaction network 196
6.2.2 The relationship of debtors and creditors 198
6.2.3 The causes of bankruptcy and the link effect 199
6.2.4 Magnitude of link effect 200
6.2.5 The ripple effect 201
6.2.6 Propagation of credit risk on the transaction network 206
6.3 Business model and business information 209
6.3.1 The industrial group as a business model 209
6.3.2 Robustness of industrial groups 214
6.3.3 Synergy in industrial groups 215
6.3.4 Business information systems 216
Epilogue 221
References 224
Index 230
Figures
2.1 Probability distribution of human height (high-school senior male
students in Japan) page 16
2.2 Probability distribution of companies declared income 17
2.3 Double-logarithmic plot of PDF of companies declared income 18
2.4 Double-logarithmic plot of CDF of companies declared income 20
2.5 Distribution of personal income tax and income 22
2.6 Evolution of Pareto index of personal income 23
2.7 CDF of personal income in 2000 24
2.8 Correlation between the Pareto index, land prices and stock prices
(TOPIX) 25
2.9 CDF of company size, 2002: (a) sales (b) profit 29
2.10 CDF of sales in various business sectors, 2002 30
2.11 CDF of company size, 2001: (a) total capital in France (b) sales in France
(c) number of employees in the UK 31
2.12 Evolution of the Pareto index, 1993-2001: (a) France (b) Italy (c) Spain
(d) UK, for total capital, sales and number of employees 32
2.13 CDF for declared company income for all the data (solid circles) and for
all listed companies (open circles) 32
2.14 PDF for declared company income for all the data (solid circles) and for
all listed companies (open circles) 32
PDFs of various Pareto distributions 34
CDFs of various Pareto distributions 34
An example of a Lorenz curve 36
The p.-dependence of the Gini coefficient 37
Lorenz curves for various values of p. 37
Definition of the Robin Hood index 38
p.-dependence of the Robin Hood index 38
The inverse-function method to generate random numbers that obey an
arbitrary distribution 39
How to make a staircase plot. Each dot corresponds to a company 40
Examples of the staircase plot 40
2.15
2.16
2.17
2.18
2.19
2.20
2.21
2.22
2.23
2.24
List of figures
2.25 Devil s Staircases for p = 0.8, 1.0, 1.2 41
2.26 Share of the largest company 44
2.27 The average share of the second-largest company 45
2.28 Total shares of the top companies 45
2.29 Distribution of the share of the top company 47
2.30 The value of p below which the top n companies achieve a total share
greater than 80% 48
2.31 The minimum number, n, of the top companies, whose total share is
greater than 80% 48
2.32 Various Pareto distributions and the 20% line 49
2.33 Dependence of the share of the top 20% of companies on p 49
2.34 The range of p where the 80-20 rule holds 50
2.35 How to obtain the fractal dimension of the size distribution of companies 52
2.36 Fractal dimension of the Devil s Staircase 53
2.37 Two forces that besiege p = 1 58
3.1 Time-series of annual company size for the eight largest electrical and
electronics companies (1988 to 2004) 61
3.2 Time-series of growth-rates for the eight largest electrical and electronics
companies (corresponding to Figure 3.1; 1989 to 2004) 62
3.3 Probability distribution for logarithmic growth-rates of company income
(2001 to 2002; roughly 57,000 companies) 63
3.4 Probability distribution for logarithmic growth-rates conditioned by
company income size (corresponding to Figure 3.3) 64
3.5 Probability distributions for growth-rates: (a) sales (b) profits (years
2002/2001) 65
3.6 Probability distributions for growth-rates: (a) total assets (France)
(b) sales (France) (c) number of employees per company (UK) (years
2001/2000) 66
3.7 Scatterplot for company sizes at successive points in time: (a) sales
(b) profits (years 2001/2000) 70
3.8 Scatterplot for company sizes at successive points in time: (a) total assets
(France) (b) sales (France) (c) number of employees (UK) (years
2001/2000) 71
3.9 Three typical examples of copulas 76
3.10 Copula for company incomes in the years 2001 and 2002 77
3.11 Copula for personal incomes in the years 1997 and 1998 78
3.12 Copula for company incomes in the year 2001 and its growth-rates 78
3.13 Scatterplot for personal incomes (measured by the amount of taxes paid)
for two consecutive years (1997 and 1998) 79
3.14 Probability distribution for the growth-rate of personal income (1997 to
1998) 80
3.15 Probability distribution for the growth-rate of personal income, 1991 to
1992, corresponding to the Bubble collapse in Japan 81
List of figures xi
3.16 Cumulative distribution for company size measured by number of
employees (whole range; year 2001) 84
3.17 Probability distribution for growth-rates of small and medium companies:
(a) total assets (b) debt (c) sales (years 2001/2000) 85
3.18 Relation between company size and variance of growth-rate for small and
medium companies: (a) total assets (b) debt (c) sales (years 2001/2000) 86
3.19 Annual number of bankruptcies in Japan and total sum of resulting debts
(1985 to 2005; calendar years) 89
3.20 Annual sum of debts when bankrupted, and ratio to nominal GDP (1996
to 2004; fiscal years) 89
3.21 Cumulative distribution for debt when bankrupted (approximately 16,000
companies bankrupted with debts larger than ¥10 million in 1997) 90
3.22 Cumulative distribution for lifetime before bankruptcy
(approximately 16,000 companies bankrupted with debts larger than
¥10 million in 1997) 90
3.23 Distribution of company s x and two values of x at which profit is
maximised under different constraints 94
3.24 Mountain view with a ridge 95
3.25 A mountain-climber and his or her directions 96
3.26 Landscape for the profit function n 96
3.27 Contour lines, steepest-ascent lines and a ridge for the profit landscape of
Figure 3.26 97
3.28 Distribution of company s x (Figure 3.23) and the solution of x
corresponding to the ridge 97
4.1 Watts-Strogatz /3 model 100
4.2 A complete graph in which every node is connected to every other within
two degrees of separation 107
4.3 Correlation r between degrees of nodes at distance 1 109
4.4 Correlation r$ between degrees of nodes at distance 4 110
4.5 Incoming and outgoing links of a listed company in a shareholding or a
transaction network 113
4.6 Shareholding network in the automobile industry 114
4.7 The corporate board and directors network and its reduced graphs 115
4.8 The corporate board network in the automobile industry 116
4.9 Transaction network of the automobile industry 118
4.10 Patent network and its reduced graphs 119
4.11 Network of joint applications for patents in the automobile industry 120
4.12 Network of joint applications for patents between the automobile and
electrical and electronic industries 121
4.13 Weighted network of the automobile industry 122
4.14 Outgoing degree distribution of the shareholding network 130
4.15 Degree distribution of the network of joint patent applications 131
4.16 Degree correlation of the shareholding network 132
List of figures
4.17 Correlation between the degree and the clustering coefficient in the
shareholding network 133
4.18 Distribution of total assets, and correlation between degree and total
assets 134
4.19 Distribution of company age, and correlation between outgoing degree
and age 134
4.20 Shareholding network with shareholders belonging to the transportation
equipment industries (1985) 137
4.21 Shareholding network with shareholders belonging to the transportation
equipment industries (1995) 137
4.22 Shareholding network with shareholders belonging to the transportation
equipment industries (2000) 138
4.23 Shareholding network with shareholders belonging to the transportation
equipment industries (2003) 138
4.24 Shareholding network with shareholders belonging to the electrical and
electronics industry (1985) 139
4.25 Shareholding network with shareholders belonging to the electrical and
electronics industry (1995) 140
4.26 Shareholding network with shareholders belonging to the electrical and
electronics industry (2000) 140
4.27 Shareholding network with shareholders belonging to the electrical and
electronics industry (2003) 141
4.28 Change of degree distribution 142
4.29 Change of the long-term shareholding rate and the cross-shareholding
rate, and correlation with the power-law exponent 143
4.30 Change of degree distribution in a shareholding network 144
4.31 Distribution of growth-rates of sales, X, and costs, Y 145
4.32 Correlation coefficient between growth-rates for sales and costs 146
4.33 Cumulative probability distributions of incoming degree and outgoing
degree 147
4.34 Standard deviation of residual error and confidence level 148
4.35 Distribution of correlation coefficient for sales 149
4.36 Network effect on correlation coefficient 149
4.37 Distribution of correlation coefficient for sales in the overlapping
network 150
5.1 Behaviour of companies in Gibrat s process 154
5.2 Distribution of company sizes in Gibrat s process 154
5.3 Simulated results for sizes and ages of companies 156
5.4 Conversion of the simulated results to the degree distribution 157
5.5 Conceptual figure of agent-based model: companies interacting
through a single bank 159
5.6 Balance sheets for companies and a bank 160
List of figures xiii
5.7 Value added versus fixed assets for listed Japanese companies in 2003 160
5.8 Emergence of finite probability of bankruptcy with increase of expected
profit 162
5.9 Determination of the interest rate for a company 162
5.10 Representative company (left panel) and bank (right panel) 164
5.11 Temporal evolution of the agent-based simulation 165
5.12 Competition among companies 166
5.13 Temporal evolution of the bank agent 167
5.14 Size distribution for companies existing eternally 167
5.15 Size distribution for companies susceptible to bankruptcy 167
5.16 Macroscopic shocks originating from synchronised bankruptcy 168
5.17 Wealth distribution in a regular network 171
5.18 Wealth distribution in a small-world network 171
5.19 Wealth distribution in a random network 172
5.20 Time evolution of distribution of wealth across agents in the regular
network, corresponding to Figure 5.17 173
5.21 Time evolution of distribution of wealth across agents in the
small-world network, corresponding to Figure 5.18 173
5.22 Time evolution of distribution of wealth across agents in the random
network, corresponding to Figure 5.19 174
5.23 Interacting company agents 176
5.24 Multiple Nash equilibrium solutions 179
5.25 Extensive form game 180
5.26 Gene 181
5.27 Crossover process 181
5.28 Mutation process 182
5.29 Payoff and fitness 182
5.30 Selection by roulette method 183
6.1 Distribution of U ? D and risk capital Ep 188
6.2 Portion of the transaction network in Japan 194
6.3 GDP scenario 194
6.4 Cumulative probability distributions of sales revenues 195
6.5 GDP scenarios and cumulative probability distributions of sales
revenues 196
6.6 Rank-size plots of indebtedness at bankruptcy, comparing origins of
bankruptcy 200
6.7 Rank-size plots of indebtedness at bankruptcy plotted for comparison
between two major causes of bankruptcy 201
6.8 (a) Fraction of bankruptcies due to link effects (b) dependence of
bankruptcies due to link effects on the amount of indebtedness 202
6.9 Cumulative distributions of (a) in-degree (vendors) (b) out-degree
(customers) 204
List of figures
6.10 A part of the transaction network consisting of a bankrupt company,
creditors and creditors of creditors 206
6.11 Model of chain bankruptcy in a transaction network 207
6.12 Results of the chain bankruptcy simulation 208
6.13 Coarse-grained transaction network 211
6.14 Parameters of the production function for companies belonging to a
Japanese industrial group 216
Tables
2.1 Average shares (%) of the top 10, 50 and 100 companies for values
of p. close to 1 page 46
2.2 The value of p at which the top 20% of companies have combined shares
of 75%, 80% and 85% 50
3.1 Parameters of r and p for copulas 77
4.1 Network indices for thirteen automobile companies 123
4.2 Cliques among thirteen companies belonging to the automobile industry 126
4.3 Network indices for the whole network and the electrical and electronics
and automobile industries 127
4.4 Network indices for the pharmaceuticals and steel industries 129
4.5 Change of shareholding network 141
5.1 Strategic form game 1 177
5.2 Strategic form game 2 178
5.3 Strategic form game 3 178
6.1 Correspondence between symbols used in corporate finance theory and
variables in this book 186
6.2 Number of creditors and amount of indebtedness for a bankrupt company 203
6.3 Cases of mergers and acquisitions in the Japanese electronic components
industry 210
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| illustrated | Illustrated |
| indexdate | 2024-07-09T22:39:35Z |
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| language | English |
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| spelling | Econophysics and companies statistical life and death in complex business networks Hideaki Aoyama ... 1. publ. Cambridge [u.a.] Cambridge Univ. Press 2010 XXVI, 234 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Economics / Methodology Statistical physics Wirtschaft Aoyama, Hideaki Sonstige oth http://assets.cambridge.org/97805211/91494/cover/9780521191494.jpg Cover HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020317549&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Econophysics and companies statistical life and death in complex business networks Economics / Methodology Statistical physics Wirtschaft |
| title | Econophysics and companies statistical life and death in complex business networks |
| title_auth | Econophysics and companies statistical life and death in complex business networks |
| title_exact_search | Econophysics and companies statistical life and death in complex business networks |
| title_full | Econophysics and companies statistical life and death in complex business networks Hideaki Aoyama ... |
| title_fullStr | Econophysics and companies statistical life and death in complex business networks Hideaki Aoyama ... |
| title_full_unstemmed | Econophysics and companies statistical life and death in complex business networks Hideaki Aoyama ... |
| title_short | Econophysics and companies |
| title_sort | econophysics and companies statistical life and death in complex business networks |
| title_sub | statistical life and death in complex business networks |
| topic | Economics / Methodology Statistical physics Wirtschaft |
| topic_facet | Economics / Methodology Statistical physics Wirtschaft |
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