Kuo Computational Intelligence in Economics and Finance

Volume II
1. Auflage 2007
ISBN: 978-3-540-72821-4
Verlag: Springer-Verlag
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 228 Seiten

Computational intelligence (CI), as an alternative to statistical and econometric approaches, has been applied to a wide range of economics and finance problems in recent years, for example to price forecasting and market efficiency.

This book contains research ranging from applications in financial markets and business administration to various economics problems. Not only are empirical studies utilizing various CI algorithms presented, but so also are theoretical models based on computational methods. In addition to direct applications of computational intelligence, readers can also observe how these methods are combined with conventional analytical methods such as statistical and econometric models to yield preferred results.

Chen, Wang, and Kuo have grouped the 12 contributions following their introductory chapter into applications of fuzzy logic, neural networks (including self-organizing maps and support vector machines), and evolutionary computation. All chapters were selected either by invitation or based on a careful selection and extension of best papers from the International Workshop on Computational Intelligence in Economics and Finance in 2005. Overall, the book offers researchers an excellent overview of current advances and applications of computational intelligence techniques to economics and finance problems.

Autoren/Hrsg.

Kuo, Tzu-Wen

Inhaltsverzeichnis

1;Preface;5
2;Contents;8
3;List of Contributors;10
4;Computational Intelligence in Economics and Finance: Shifting the Research Frontier;13
4.1;1 About the CIEF Series;13
4.2;2 About this Volume;14
4.3;3 Fuzzy Logic;16
4.4;4 Artificial Neural Networks;17
4.5;5 Evolutionary Computation;30
4.6;6 Agents;32
4.7;7 Concluding Remarks;33
4.8;Acknowledgements;33
4.9;References;33
5;An Overview of Insurance Uses of Fuzzy Logic;36
5.1;1 Introduction;36
5.2;2 Insurance Application Areas;38
5.3;3 Linguistic Variables and Fuzzy Set Theory;39
5.4;4 Fuzzy Numbers and Fuzzy Arithmetic;43
5.5;5 Fuzzy Inference Systems;50
5.6;6 Fuzzy Clustering;56
5.7;7 Fuzzy Programming;60
5.8;8 Fuzzy Regression;63
5.9;9 Soft Computing;65
5.10;10 Conclusions;68
5.11;Acknowledgements;68
5.12;References;68
6;Forecasting Agricultural Commodity Prices using Hybrid Neural Networks;73
6.1;1 Introduction;73
6.2;2 A Hybrid Forecast Model and Its Components;74
6.3;3 Empirical Analysis;77
6.4;4 Conclusions;81
6.5;References;83
7;Nonlinear Principal Component Analysis for Withdrawal from the Employment Time Guarantee Fund;85
7.1;1 Introduction;85
7.2;2 Withdrawal from the FGTS;87
7.3;3 Methods;92
7.4;4 Dimension Reduction;94
7.5;5 Conclusions;100
7.6;Acknowledgements;101
7.7;References;101
8;Estimating Female Labor Force Participation through Statistical and Machine Learning Methods: A Comparison;103
8.1;1 Introduction;103
8.2;2 Classifiers to be compared;105
8.3;3 Example;109
8.4;4 Conclusions;114
8.5;Acknowledgements;114
8.6;References;114
9;An Application of Kohonen’s SOFM to the Management of Benchmarking Policies;116
9.1;1 An Introduction to Efficiency;116
9.2;2 The Self Organizing Feature Map for a Representation of Efficiency;121
9.3;3 Empirical Application;122
9.4;4 Conclusions;128
9.5;References;128
10;Trading Strategies Based on K-means Clustering and Regression Models;131
10.1;1 Introduction;131
10.2;2 Time Series Data Preparation;132
10.3;3 Methodology for Trend Analysis;133
10.4;4 Trading Strategies;135
10.5;5 Experimental Design;136
10.6;6 Experimental Results;137
10.7;7 Conclusions and Future Work;140
10.8;Acknowledgments;141
10.9;References;142
11;Comparison of Instance-Based Techniques for Learning to Predict Changes in Stock Prices;143
11.1;1 Introduction;143
11.2;2 Identification of the Problem;144
11.3;3 Obtaining and Pre-processing Data;144
11.4;4 Model and Parameter Selection;145
11.5;5 Interpretation of Results;148
11.6;6 Areas for Future Study;150
11.7;References;150
12;Application of an Instance Based Learning Algorithm for Predicting the Stock Market Index;152
12.1;1 Introduction;152
12.2;2 Background and RelatedWork;153
12.3;3 Predicting Stock Price Index Variation;155
12.4;4 Experimental Framework;156
12.5;5 Results;158
12.6;6 Conclusions;160
12.7;Acknowledgement;160
12.8;References;161
13;Evaluating the Efficiency of Index Fund Selections Over the Fund’s Future Period;163
13.1;1 Introduction;163
13.2;2 Preliminaries;164
13.3;3 GAMethod;165
13.4;4 Numerical Experiments;167
13.5;5 Concluding Remarks;172
13.6;References;172
14;Failure of Genetic-Programming Induced Trading Strategies: Distinguishing between Efficient Markets and Inefficient Algorithms;175
14.1;1 Motivation and Introduction;175
14.2;2 Pretests: Description and Rationale;178
14.3;3 What do the pretests tell us ?;182
14.4;4 Experiments;184
14.5;5 Conclusions;185
14.6;Acknowledgements;186
14.7;A Genetic Programming Settings;186
14.8;References;186
15;Nonlinear Goal-Directed CPPI Strategy;189
15.1;1 Introduction;189
15.2;2 Evolutionary Algorithms;191
15.3;3 Trading Strategies;197
15.4;4 Experiments and Analyses;203
15.5;5 Conclusions;209
15.6;References;211
16;Hybrid-Agent Organization Modeling: A Logical- Heuristic Approach;215
16.1;1 Introduction;215
16.2;2 Organizational Environment;216
16.3;3 Heuristic Decision Support Systems;219
16.4;4 The Structure of the Decision-Makers Mind;222
16.5;5 Hybrid Agents;225
16.6;6 Connecting Functions;226
16.7;7 Hybrid Agents in a Financial Organization;227
16.8;8 Concluding Remarks;228
16.9;Acknowledgements;228
16.10;References;228
17;Index;230