Skip to main content
SpringerLink
Log in

Computational Intelligence in Economics and Finance

  • Chapter
Book cover Computational Intelligence in Economics and Finance

Part of the book series: Advanced Information Processing ((AIP))

Abstract

Computational intelligence is a consortium of data-driven methodologies which includes fuzzy logic, artificial neural networks, genetic algorithms, probabilistic belief networks and machine learning as its components. We have witnessed a phenomenal impact of this data-driven consortium of methodologies in many areas of studies, the economic and financial fields being no exception. In particular, this volume of collected works will give examples of its impact on various kinds of economic and financial modeling, prediction and forecasting, and the analysis of various phenomena which sheds new light on a fundamental understanding of the research issues. This volume is the result of the selection of high-quality papers presented at the Second International Workshop on Computational Intelligence in Economics and Finance (CIEF’2002), held at the Research Triangle Park, North Carolina,United State of America, March 8–14, 2002. To complete a better picture of the landscape of this subject, some invited contributions from leading scholars were also solicited.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aha, D. (1997): Lazy Learning. Kluwer

    Google Scholar 

  2. Aoki, M. (1987): State Space Modeling of Time Series. Springer-Verlag

    Google Scholar 

  3. Armano, G., Minnt A., Marchesi, M. (2002): NXCS-A Hybrid Approach to Stock Indexes Forecasting. In: Chen, S.-H. (Ed.), Genetic Algorithms and Genetic Programming in Computational Finance, Kluwer

    Google Scholar 

  4. Aussem, A., Campbell, J., Murtagh F. (1998): Wavelet-Based Feature Extraction and Decomposition Strategies for Financial Forecasting. Computational Intelligence in Economics and Finance, Vol. 6, No. 2, 5–12

    Google Scholar 

  5. Azoff, M. (1994): Neural Network Time Series: Forecasting of Financial Markets. Wiley

    Google Scholar 

  6. Baestaens, 1).-E., Van Den Bergh, W., Wood D. (1991): Neural Network Solutions for Trading in Financial Markets, Pitman

    Google Scholar 

  7. Baglioni, S., Sorbello, D., Pereira, C., Tettarnanzi, A. G. B. (2000): “Evolutionary Multiperiod Asset Allocation,” In: Whitley, D., Goldberg, D., Cantti-Paz, E., Spector, L., Parrnee, I., Beyer, H. -G. (Eds.), Baglioni, S., Sorbello, D., Pereira, C., Tettarnanzi, A. G. B, 597–604. Morgan Kaufmann

    Google Scholar 

  8. Balakrishnan, A. (1987): Kalman Filtering Theory. Optimization Software

    Google Scholar 

  9. Balakrishnan, A. (1988): State Space Theory of Systems. Optimization Software

    Google Scholar 

  10. Bauer, R., Jr. (1994): Genetic Algorithms and Investment Strategies. Wiley

    Google Scholar 

  11. Billot, A. (1995): Economic Theory of Fuzzy Equilibria: an Axiomatic Analysis. Springer-Verlag, 2nd edition

    Google Scholar 

  12. Bojadziev, G., Bojadziev, M., Zadeh, L. (1997): Fuzzy Logic for Business, Finance, and Management. World Scientific

    Google Scholar 

  13. Boguslayskij, 1. (1988): Filtering and Control. Optimization Software

    Google Scholar 

  14. Bonabeau, E., Meyer, C. (2002): Swarm Intelligence: a Whole New Way to Think About Business. Harvard Business School Press

    Google Scholar 

  15. Chang, K., Osier, C. (1994), “Evaluating Chart-Based Technical Analysis: the Head-and-Shoulders Pattern in Foreign Exchange Markets,” Working Paper, Federal Reserve Bank of New York

    Google Scholar 

  16. Chen, 5.-11. (2002a): Evolutionary Computation in Economics and Finance, Physica, Verlag

    Google Scholar 

  17. Chen, S.-H. (20021r): Genetic Algorithms and Genetic Programming in Computational Finance, Kluwer

    Google Scholar 

  18. Chen, T., Chen, H. (1995): Universal Approximation to Nonlinear Operators by Neural Networks with Arbitrary Activation Functions and Its Application to Dynamical Systems. IEEE Transactions on Neural Netwroks, Vol. 6, 911–917

    Google Scholar 

  19. Cordon, O., Herrera, F., Hoffmann, F., Magdalena. L. (2001): Genetic Fuzzy Systems. World Scientific

    Google Scholar 

  20. Crisan, I). (2001): Particle Filters-a Theoretical Perspective. In: Doucel A., Freitag, N., Cordon, N. ( 2001 ) ( Eds.), Sequential Monte Carlo Methods in Practice, Springer-Verlag, 17–41

    Google Scholar 

  21. Cristiarrini, N., Shawe-Taylor, J. (2000): An Introduction I Srtpport Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press

    Google Scholar 

  22. Deboeck, G., Kohonen, T. (1998), Visual Explorations in Finance with Self-organizing Maps, Springer-Verlag

    Google Scholar 

  23. Deng, J. (1982): Control Problems of Grey System. System and (.’ou trol Letters, No. 5, 288 299

    Google Scholar 

  24. Deng, J. (1989): Introduction to Grey Syeierri Theory, Journal of Carey System; Vol. 1, No. 11. 121

    Google Scholar 

  25. Dorigo, M. (1992): Optimization, Learning and Natural Algorithms, 1’h. I ). Thesis, Politecnice di Milano, Italy, in Italian

    Google Scholar 

  26. Dorigo, V1., Marriezzo, V., Colorni, A. (1996): The Ant System: Optimization by a Colony of Cooperating Agents, IEEE, Transactions on Systems, Klan. and Cybernetics, Part B, Vol. 26, No. I, 29–41

    Google Scholar 

  27. Duffy, J., McNelis, P. D. (2001): Approximating and Simulating the Stoehast,k: Growth Model: Parameterized Expectations, Neural Networks, and the (ïcnet is Algorithm. Journal of Economic Dynamics and Control 25(9), 1273 130: 1

    Google Scholar 

  28. Eberhart, R., Simpson, R, Dobbins, R. (1996): Computational Intelligence PC Tools. AP Professional

    Google Scholar 

  29. Epstein, J., Axtell, 11. (1996): Growing Artificial Societies: Social Science from the Bottom Up. MIT Press

    Google Scholar 

  30. Farr, J., Gijbels, I. (1996): Local Polynomial Modeling and Its Applications. Chapman & Hall.

    Google Scholar 

  31. Fischer, R. (2001): “l’lie New Fibonacci ‘Trader: Tools and Strategies for ‘Trading Success. Wiley

    Google Scholar 

  32. Fogel, D. (1995): livolut.ionary Computation toward a New Philosophy of Machine Intelligence. IEEE Press

    Google Scholar 

  33. Fogel, D., Chellapilla, K., Angeline, P. (2002): Evolutionary Computation and Economic Models: Sensitivity and Unintended Consequences. In: Chen, S. 11. (Ed.), Evolutionary Computation in Economics and Finance, Physics Verlag, 215 269

    Google Scholar 

  34. Vogel, L. (1964): On the Organization of Intellect, l’h.l). “Thesis, UCLA

    Google Scholar 

  35. Vogel, L. (1997): A Retrospective View and Outlook on Evolutionary Algorithms. In: Reusch, B. (Ed.), Computational Intelligence: ‘Theory and Applications, 5th Fuzzy Days, Springer-Verlag, Berlin, 337 312

    Google Scholar 

  36. Fogel, L. J., Owens, A..1., Walsh, NI..J. (1966): Artificial Intelligence through Simulated Evolution, Wiley

    Google Scholar 

  37. Carey, NI., Johnson, D. (1979): Computers and Intractability, a Guide to the Theory of NP-(ompleteness. Freeman

    Google Scholar 

  38. I.38 Gately. E. (1996): Neural Networks for f’inancial Forecasting. Wiley

    Google Scholar 

  39. Gencay, R.., Selcuk, I“., Whitcher, B. (2001): an Introduction to Wavelet, and Other Filtering Methods in Finance and Economies, Academic Press

    Google Scholar 

  40. Granger, C., Flatanal:a. M. (1964): Spectral Analysis of Economic Thne Series. Princeton

    Google Scholar 

  41. Goffe, W. (1996): SIMANN: A Global Optimization Alogorithm Using Simulated Annealing. Studies in Nonlinear Dynamics and Econometrics. Vol. I. No. 3

    Google Scholar 

  42. Goffe, W., Ferrier, (1, Rogers. J. (1992): Simulated Annealing: an Initial Application in Econometrics. Computer Science in Economics and Managenrent. Vol. 5

    Google Scholar 

  43. Goffe, W., Ferrier, G., Rogers. J. (1991): Global Optimization of Stat ist ienI Functions with Simulated Annealing. Journal of fir onometrics. Vol. 60. No. 1/2. January/February. 65 99

    Google Scholar 

  44. Cross. S., Aron. S., Deneubourg, J. L., Pasteels, J. M. (1989): Self-organized Shortcuts in the Argentine Ant. Naturwissenschaften 76, 579–581

    Article  Google Scholar 

  45. Hampton, J. (1997): Rough Set Theory The Basics (Part 1). Journal of Computational Intelligence in Finance, Vol. 5, No. 6, 25–29

    Google Scholar 

  46. Ilampton, J. (1998): Rough Set Theory—The Basics (Part 2). Journal of Computational Intelligence in Finance, Vol. 6, No. 1, 40–42

    Google Scholar 

  47. Hampton, J. (1998): Rough Set Theory The Basics (Part 3). Journal of Computational Intelligence in Finance, Vol. 6, No. 2, 35–37

    Google Scholar 

  48. Harvey, A. (1989): Forecasting Structural Time Series Models and the Kalman Filter. Cambridge

    Google Scholar 

  49. Hiemetra, Y. (1996): Applying Neural Networks and Genetic Algorithms to Tactical Asset Allocation, NeuroveSt Journal, 4 (3), 8–15

    Google Scholar 

  50. Keber, C. (2002): Collective Intelligence in Option Pricing-Determining Black-Scholes Implied Volatilities with the Ant Programming Approach, working paper, University of Vienna

    Google Scholar 

  51. Kirkpatrick, S., Gelatt, C., Vecchi, M. (1983), Optimization by Simulated Annealing, Science, Vol. 220, 671–680

    Google Scholar 

  52. Kitagawa, G., Sata, S. (2001): Monte Carlo Smoothing and Self-organizing State Space Model. In: Doucet, A., Freitas, N., Gordon, N. (2001) (Eds.), Sequential Monte Carlo Methods in Practice, Springer-Verlag, 177–196

    Google Scholar 

  53. Kohonen, T. (1982): Self-organized Foundation of Topologically Correct Feature Nlaps. Biological Cybernetics 43, 59–69

    Article  MathSciNet  MATH  Google Scholar 

  54. Lin, C. -T., Yang, S. -Y. (1999): Selection of Home Mortgage Loans Using Grey Relational Analysis. Journal of Grey System, Vol. 11, No. 4, 359–368

    Google Scholar 

  55. Lin, C. -T., Chen, L. -H. (1999): A Grey Analysis of Bank Re-decreasing the Required Reserve Ratio. Journal of Grey System, Vol. 11, No. 2, 119–132

    Google Scholar 

  56. Lin, C. -T., Chang, P. -C. (2001): Forecast the Output Value of Taiwan’s Machinery Industry Using the Grey Forecasting. Journal of Grey System, Vol. 1.3, No. 3, 259 268

    Google Scholar 

  57. Lo, A., Mama,ysky, HI., Wang, J. (2000): Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation, Journal of Finance. Vol. LV, No. 4, 1705–1765

    Google Scholar 

  58. Mansur, Y. (1995): Fuzzy Sets and Economics: Applications of Fuzzy Mathematics to Non-cooperative Oligopoly. Edward Elgar

    Google Scholar 

  59. Marks, R. (2002): Playing Games with Genetic Algorithms. In: Chen, S. H. (Ed.), l’volutionary Computation in Economics and Finance, Physica-Verlag,:i 1 44

    Google Scholar 

  60. Messier, W. 1U’., Hansen, J. V. (1988): Inducing Rule for Expert System Development: art Example Using Default and Bankruptcy Data. Management Science 34, 1403 H15

    Google Scholar 

  61. Modzek, A., Skabek, K. (1998): Rough Sets in Economic Applications. In: Polkowski, L., Skowron, A. (Eds.), Rough Sets in Knowledge Discovery 2: Applications, Case Studies and Software Systems, Physica-Verlag. Chap. 13

    Google Scholar 

  62. Mortensen, R. (1987): Random Signals and Systems. Wiley

    Google Scholar 

  63. Osier, C’., Chang, K. (1995): Head and Shoulder: Not Just a Flaky Pattern. Staff Report: No. 1, federal Researve Bank of New York

    Google Scholar 

  64. Pan, Z., Wang, X. (1998): Wavelet-Based Density Estimator Model for Forecasting. (’output at.ional Intelligence in Economics and Finance, Vol. 6, No. 1, 13

    MathSciNet  Google Scholar 

  65. l’ackard, N. (1990): A Genetic Learning Algorithm for the Analysis of Complex Data, Complex Systems 4, No. 5, 543–572

    MathSciNet  Google Scholar 

  66. Pedrycz, W. (1997): Computational Intelligence: An Introduction. CRC I’n ss 1.67 Peray, K. ( 1999 ): Investing in Mutual Funds Using]Fuzzy Logic. CRC Press

    Google Scholar 

  67. Quinlan, R. (1986): Induction of Decision Trees. iVlachine Learning 1(1), 81. 106

    Google Scholar 

  68. Quinlan, R. (1987): Simplifying I)ecisiou ‘Frees. International Journal of \Ian-Machine Studies 27 (3), 221–231

    Article  Google Scholar 

  69. Quinlan, R. (1993): C4. 5: Programs for Machine Learning. Morgan Kaufmann

    Google Scholar 

  70. Rechenberg, 1. (1965): Cybernetic Solution Path of an Experimental Problem. Royal Aircraft Establishment, Library ‘Translation No. 1122, August. Farnborough, UK

    Google Scholar 

  71. Refenes, A. -P. (1995): Neural Networks in the Capital Markets. Wiley

    Google Scholar 

  72. Refenes, A.-P., Zapranis, A. (1999): Principles of Neural Model Identification, Selection and Adequacy: with Applications in Financial Econometrics. Springer

    Google Scholar 

  73. Shadbolt, J., Taylor, J. (2002): Neural Networks and the Financial Markets-Predicting, Combining, and Portfolio Optimisation. Springer

    Google Scholar 

  74. Schmertmann, C. P. (1996): Functional Search in Economics Irving Genetic Programming. Computational Economics 9(4), 275 298

    Google Scholar 

  75. Schwefel, H. -P. (1965): Kybernetische Evolution als Strategies der Experimentellen Forschung in der Strömungst.echnik. I)iplonma, Thesis, ‘kiln ncal versity of Berlin

    Google Scholar 

  76. Schwwfel, II. -P. (1995): Evolution and Optimum Seeking, ‘Wiley

    Google Scholar 

  77. Skalkoz, C. (1996): Rough Sets help Time the Bi X. M uralvelt. Journal. Nov./Dec., 20–27

    Google Scholar 

  78. Slowinski, R., Zopounidis, C. (1995): Applications of the (tough Set. Approach to Evaluation of Bankruptcy Risk. International Journal of Intelligent Systems in Accounting, Finance and Management 4. 27–41

    Google Scholar 

  79. Smithson, M. J. (1987): Fuzzy Set Analysis for Behavioral anda Social Sciences. Springer-Verlag, New York

    Book  Google Scholar 

  80. Sugeno, M., Yasukawa, T. (1993): A Fuzzy-Logic-Based Approach to Qualitative Modeling. IEEE Transactions on Fuzzy Systems, Vol. I, 7: 31

    Google Scholar 

  81. Suykens,I., Vandewalle. J. (1998): The K.U. Leuven Time Series Prediction Competition. In: Suykens, J., Vandewalle, J. (Eds.), Nonlinear Alodeling: Advanced Black-Box Techniques, Kluwer, 241–253

    Chapter  Google Scholar 

  82. Takagi, ‘T., Sugeno, M. (1985): Fuzzy Identification of Systems and Its Applications to.Modeling and Control. IEEE Transactions on Systems,: Alan, and Cybernetics, Vol. 15, 116–132

    Google Scholar 

  83. Tac, N., Linn, S. (2001): Fuzzy Inductive Reasoning, Expectation Formation and the Behavior of Security Prices. Journal of Economic Dynamics and Control. Vol. 25, 321 361

    Google Scholar 

  84. I.85 Taylor P., Abdul-Kader, Vl., Dngdale. D. (1998): Investment Decisions in Advanced Manufacturing “i(ehnology\Fuzzy Set ”T henry Approach. Ashgate

    Google Scholar 

  85. Thomason, M. R. (1997): Financial Forecasting with Wavelet Filters and Neural Networks. C’’DorpoLational Intelligence in Economics and Finance, Vol. No. 2, 27 32

    Google Scholar 

  86. Trippi, R. It., “Turban, E. (1993): Vernal Networks in Finance and Investing. Irwin

    Google Scholar 

  87. Ici, Y.-C., Lin. C.-`I’., “Tsai, II.-.1. (2001): `Ilre Performance Evaluation Model of Stock-Listed Banks in Taiwan liv (irev Relational Analysis and Factor Analysis. ‘Che Journal of (trey Systeu n. Vol. 13, No. 2, 153–164

    Google Scholar 

  88. Vapnik, V. (1998a): Statistical Learning “Theory. Wiley

    Google Scholar 

  89. Vapnik, V. (1998b): The Support Vector Method of Function Estimation. In: Suykens, J.. Vandewalle,.1. (Eds.). Nonlinear Modeling: advanced Black-Box Techniques. Kluwer, Boston, 55–85

    Google Scholar 

  90. Von Altrock, C. (1996): Fuzzy Logic and Neurofnzzy Applieatious iu Busirncse and finance. Prentice Hall

    Google Scholar 

  91. White, IL (1988): Economic Prediction Using Neural Networks. the Case of IBM Daily Stock Returns. Proceedings of IEEE International Conference on Neural Networks, Vol. 2, IEEE, New York, 451–458

    Chapter  Google Scholar 

  92. White, II. (1992): Artificial Neural Networks-Approximation ‘Theory and Learning Theory. Blackwell

    Google Scholar 

  93. Wolberg, J. (2000): Expert Trading Systems: Modeling Financial Markets with Kernel Regression. Wiley

    Google Scholar 

  94. Zirilli, J. (1996): Financial Prediction Using Neural Networks. International Thomson Publishing

    Google Scholar 

  95. Zopounidis, C., Pardalos, P., Baourakis, C. (2002): Fuzzy Sets in Management, Economy & Marketing. World Scientific

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. AI-ECON Research Center, Department of Economics, National Chengchi University, Taipei, Taiwan, 11623

    Shu-Heng Chen

  2. Department of Electrical & Computer Engineering, Duke University, P.O. 90291, Durham, NC, 27708-0291, USA

    Paul P. Wang

Authors
  1. Shu-Heng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paul P. Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. AI-ECON Research Center Department of Economics, National Chengchi University, 11623, Taipei, Taiwan

    Shu-Heng Chen

  2. Department of Electrical and Computer Science, Duke University, P.O. 90291, 27708-0291, Durham, NC, USA

    Paul P. Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Chen, SH., Wang, P.P. (2004). Computational Intelligence in Economics and Finance. In: Chen, SH., Wang, P.P. (eds) Computational Intelligence in Economics and Finance. Advanced Information Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-06373-6_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-06373-6_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-07902-3

  • Online ISBN: 978-3-662-06373-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation