Computational Intelligence in Economics and Finance (eBook)

Prof. Dr Shu-Heng Chen is a professor in the Department of Economics of the National Chengchi University. He serves as the Director of the AI-ECON Research Center, National Chengchi University. Dr. Chen holds a M.A. degree in mathematics and a Ph. D. in Economics from the University of California at Los Angeles. He has more than 150 publications in international journals, edited volumes and conference proceedings. Prof. Dr. Paul P. Wang, has published extensively in the fields of mathematical systems modeling, fuzzy logic, pattern recognition,intelligent ystems,managements of economical systems, and the computational biology and bioinformatics. He has been a co-founder of several corporations including Intelligent Machines Inc. He has served as an EiC of the Information Sciences Journal for two decades and he is the managing editor of the New Mathematics & Natural Computing at present. In addition,he is the founder of JCIS, Inc. and Society for Mathematics of Uncertainty in 2006. Prof. Dr. Tzu-Wen Kuo is an assistant professor in Department of Finance and Banking of Aletheia University in Taiwan. She is also a fellow of AI-ECON research center. Her research interest is Genetic Programming in Economics and Finance.

Preface 5
Contents 8
List of Contributors 10
Computational Intelligence in Economics and Finance: Shifting the Research Frontier 13
1 About the CIEF Series 13
2 About this Volume 14
3 Fuzzy Logic 16
4 Artificial Neural Networks 17
5 Evolutionary Computation 30
6 Agents 32
7 Concluding Remarks 33
Acknowledgements 33
References 33
An Overview of Insurance Uses of Fuzzy Logic 36
1 Introduction 36
2 Insurance Application Areas 38
3 Linguistic Variables and Fuzzy Set Theory 39
4 Fuzzy Numbers and Fuzzy Arithmetic 43
5 Fuzzy Inference Systems 50
6 Fuzzy Clustering 56
7 Fuzzy Programming 60
8 Fuzzy Regression 63
9 Soft Computing 65
10 Conclusions 68
Acknowledgements 68
References 68
Forecasting Agricultural Commodity Prices using Hybrid Neural Networks 73
1 Introduction 73
2 A Hybrid Forecast Model and Its Components 74
3 Empirical Analysis 77
4 Conclusions 81
References 83
Nonlinear Principal Component Analysis for Withdrawal from the Employment Time Guarantee Fund 85
1 Introduction 85
2 Withdrawal from the FGTS 87
3 Methods 92
4 Dimension Reduction 94
5 Conclusions 100
Acknowledgements 101
References 101
Estimating Female Labor Force Participation through Statistical and Machine Learning Methods: A Comparison 103
1 Introduction 103
2 Classifiers to be compared 105
3 Example 109
4 Conclusions 114
Acknowledgements 114
References 114
An Application of Kohonen’s SOFM to the Management of Benchmarking Policies 116
1 An Introduction to Efficiency 116
2 The Self Organizing Feature Map for a Representation of Efficiency 121
3 Empirical Application 122
4 Conclusions 128
References 128
Trading Strategies Based on K-means Clustering and Regression Models 131
1 Introduction 131
2 Time Series Data Preparation 132
3 Methodology for Trend Analysis 133
4 Trading Strategies 135
5 Experimental Design 136
6 Experimental Results 137
7 Conclusions and Future Work 140
Acknowledgments 141
References 142
Comparison of Instance-Based Techniques for Learning to Predict Changes in Stock Prices 143
1 Introduction 143
2 Identification of the Problem 144
3 Obtaining and Pre-processing Data 144
4 Model and Parameter Selection 145
5 Interpretation of Results 148
6 Areas for Future Study 150
References 150
Application of an Instance Based Learning Algorithm for Predicting the Stock Market Index 152
1 Introduction 152
2 Background and RelatedWork 153
3 Predicting Stock Price Index Variation 155
4 Experimental Framework 156
5 Results 158
6 Conclusions 160
Acknowledgement 160
References 161
Evaluating the Efficiency of Index Fund Selections Over the Fund’s Future Period 163
1 Introduction 163
2 Preliminaries 164
3 GAMethod 165
4 Numerical Experiments 167
5 Concluding Remarks 172
References 172
Failure of Genetic-Programming Induced Trading Strategies: Distinguishing between Efficient Markets and Inefficient Algorithms 175
1 Motivation and Introduction 175
2 Pretests: Description and Rationale 178
3 What do the pretests tell us ? 182
4 Experiments 184
5 Conclusions 185
Acknowledgements 186
A Genetic Programming Settings 186
References 186
Nonlinear Goal-Directed CPPI Strategy 189
1 Introduction 189
2 Evolutionary Algorithms 191
3 Trading Strategies 197
4 Experiments and Analyses 203
5 Conclusions 209
References 211
Hybrid-Agent Organization Modeling: A Logical- Heuristic Approach 215
1 Introduction 215
2 Organizational Environment 216
3 Heuristic Decision Support Systems 219
4 The Structure of the Decision-Makers Mind 222
5 Hybrid Agents 225
6 Connecting Functions 226
7 Hybrid Agents in a Financial Organization 227
8 Concluding Remarks 228
Acknowledgements 228
References 228
Index 230