ISBN: 3790814768
TITLE: Evolutionary Computation in Economics and Finance
AUTHOR: Chen
TOC:

Foreword V
Part I. An Overview
1 Evolutionary Computation in Economics and Finance: An Overview 3
Shu-Heng Chen
1.1 The Birth of this Volume 3
1.2 Playing Games: 1987 4
1.3 Exploring Agent-Based Artificial Stock Markets: 1988 6
1.4 Probing Econometrics and Financial Engineering: 1992 18
1.5 Conclusions 25
References 25
Part II. Games
2 Playing Games with Genetic Algorithms 31
Robert E. Marks
2.1 Introduction 31
2.2 Deductive Versus Evolutionary Approaches to Game Theory 32
2.3 The Repeated Prisoner's Dilemma 32
2.4 Boundedly Rational Players 32
2.5 Game-Playing Automata 33
2.6 Co-Evolution of Automata 34
2.7 Learning 35
2.8 Replicator Dynamics 36
2.9 Other Refinements 38
2.10 Empirical Games 38
2.11 Conclusion 39
References 40
3 Genetic Algorithm Learning and Economic Evolution 45
Thomas Riechmann
3.1 Introduction 45
3.2 The Standard Genetic Algorithm 46
3.3 Genetic Algorithms as Evolutionary Processes 47
3.4 Populations as Near Nash Equilibria 50
3.5 Evolutionary Stability of Genetic Populations 51
3.6 Evolutionary Dynamics 54
3.7 Modified Genetic Operators and Their Impact on Stability 56
3.8 Summary 58
References 58
4 Using Symbolic Regression to Infer Strategies from Experimental Data 61
John Duffy, Jim Engle-Warnick
4.1 Introduction 61
4.2 Symbolic Regression Using Genetic Programming 62
4.3 An Illustration 64
4.4 The Regression Model 65
4.5 The Algorithm 71
4.6 Parameters and Fitness Specification 71
4.7 Regression Results for the Ultimatum Game 72
4.8 Summary and Conclusions 81
References 81
Part III. Agent-Based Computation Economics
5 The Efficiency of an Artificial Double Auction Stock Market with Neural Learning Agents 85
Jing Yang
5.1 Motivation and Introduction 85
5.2 Market Structure 87
5.3 Experiment Design 94
5.4 Computational Results 95
5.5 Conclusions and Directions for Future Research 101
A Appendix 102
References 103
6 On AIE-ASM: Software to Simulate Artificial Stock Markets with Genetic Programming 107
Shu-Heng Chen, Chia-Husan Yeh, Chung-Chih Liao
6.1 Introduction 107
6.2 AIE-ASM, Version 2: A User's Guide 108
6.3 Search Process without Business School 115
6.4 An Example 117
6.5 A Summary of AIE-ASM Publications 121
References 121
7 Exchange Rate Volatility 123
Jasmina Arifovic
7.1 Introduction 123
7.2 Description of the Model 124
7.3 Description of the Artificial Foreign Exchange Market 126
7.4 Further Research 131
References 134
8 Using an Artificial Market Approach to Analyze Exchange Rate Scenarios 135
Kiyoshi Izumi, Kazuhiro Ueda
8.1 Introduction 135
8.2 Problems with Conventional Approaches 136
8.3 Framework of the Artificial Market Approach 136
8.4 Observation in the Field 137
8.5 Construction of a Multi-agent Model 142
8.6 Scenario Analysis 151
8.7 Conclusion 156
References 156
9 Emulating Trade in Emissions Permits: An Application of Genetic Algorithms 159
Rosalyn Bell, Stephen Beare
9.1 Background 159
9.2 Model Construction and Use of GAS 161
9.3 Simulation Results 167
9.4 Concluding Remarks 170
9.5 Symbol Listing 172
References 173
10 Cooperative Computation with Market Mechanism 175
Masayuki Ishinishi, Hiroshi Sato, Akira Namatame
10.1 Introduction 175
10.2 A Model of Economic Agents and Definition of Equilibrium Solutions 177
10.3 The Competitive Adaptation Using Market Prices 180
10.4 Social Rules that Induce Implicit Cooperation 182
10.5 Simulation Results 184
10.6 Conclusion 187
References 187
11 Hysteresis in an Evolutionary Labor Market with Adaptive Search 189
Leigh Tesfatsion
11.1 Introduction 189
11.2 Labor Market Framework 194
11.3 Descriptive Statistics 199
11.4 Experimental Design 202
11.5 Experimental Findings 204
11.6 Concluding Remarks 209
References 210
12 Computable Learning, Neural Networks and Institutions 211
Francesco Luna
12.1 Introduction 211
12.2 The Theoretical Reference Point 213
12.3 Neural Nets and Institutions 214
12.4 Memory, Confidence and Psychological Addiction 219
12.5 Physical Effectiveness and Structural Sclerosis 223
12.6 Psychological Addiction and Innovation 225
12.7 Structural Sclerosis and Innovation 227
12.8 Social Learning 228
12.9 Conclusions 231
References 231
13 On Two Types of GA-Learning 233
Nicolaas J. Vriend
13.1 Introduction 233
13.2 An Example 234
13.3 Analysis 238
13.4 Discussion 240
A Appendix 242
References 243
14 Evolutionary Computation and Economic Models: Sensitivity and Unintended Consequences 245
David B. Fogel, Kumar Chellapilla, Peter J. Angeline
14.1 Introduction 245
14.2 The El Farol Problem 247
14.3 The Iterated Prisoner's Dilemma 253
14.4 Discussion 264
References 267
Part IV. Financial Engineering
15 Tinkering with Genetic Algorithms: Forecasting and Data Mining in Finance and Economics 273
George G. Szpiro
15.1 Introduction 273
15.2 A Primer on Genetic Algorithms 274
15.3 Performance Boosters 277
15.4 Other Problems and Suggestions for Future Research 281
15.5 Concluding Remarks 284
References 284
16 Forecasting Ability But No Profitability: An Empirical Evaluation of Genetic Algorithm-Optimised Technical Trading Rules 287
Robert Pereira
16.1 Introduction 287
16.2 Technical Trading Rules 289
16.3 Genetic Algorithm Methodology 293
16.4 Performance Evaluation 299
16.5 An Empirical Application 302
16.6 Conclusion 308
References 308
17 Evolutionary Induction of Trading Models 311
Siddhartha Bhattacharyya, Kumar Mehta
17.1 Introduction 311
17.2 Representation of Trading Models 313
17.3 Fitness Function 317
17.4 Experimental Study 321
17.5 Discussion 326
References 329
18 Optimizing Technical Trading Strategies with Split Search Genetic Algorithms 333
Raymond Tsang, Paul Lajbcygier
18.1 Introduction 333
18.2 GAs and Mutation 334
18.3 The SSGA Explained 336
18.4 Preliminary Testing 338
18.5 Preliminary Results 340
18.6 Financial Application: Technical Trading Strategies 343
18.7 Conclusion 355
References 357
19 GP Forecasts of Stock Prices for Profitable Trading 359
M.A. Kaboudan
19.1 Introduction 359
19.2 SDTS 361
19.3 The Data 364
19.4 GEMs and Their Price Forecasts 365
19.5 Trading Profits 372
19.6 Remarks 376
References 377
20 Option Pricing via Genetic Programming 383
N. K. Chidambaran, Joaquin Triqueros, Chi-Wen Jevons Lee
20.1 Introduction 383
20.2 Genetic Programming - A Brief Overview 385
20.3 Performance Analysis in a Jump-Diffusion World 386
20.4 Application in the Real World 392
20.5 Conclusion 395
References 396
21 Evolutionary Computation in Option Pricing: Determining Implied Volatilities Based on American Put Options 399
Christian Keber
21.1 Introduction 399
21.2 The Implied Volatility Model 401
21.3 Genetic Programming 403
21.4 Genetic Determination of Implied Volatilities 404
21.5 Experimental Results 406
21.6 Concluding Remarks 413
References 414
Part V. Bibliography
22 Evolutionary Computation in Economics and Finance: A Bibliography 419
Shu-Heng Chen, Tzu Wen Kuo
22.1 Introduction 419
22.2 Publications by Application Domains 419
22.3 Publications by Journals 426
22.4 Publications by Conference Proceedings 427
22.5 Useful Websites 428
22.6 Software 429
References 429
Index 456
END
