Computational intelligence in economics and finance: Shifting the research frontier

Computational Intelligence in Economics and Finance: Volume II

Volumn , Issue , 2007, Pages 1-23

  Chen, Shu Heng ^a   Wang, Paul P ^b   Kuo, Tzu Wen ^c  

^a NATIONAL CHENGCHI UNIVERSITY   (Taiwan)

^b Duke University   (United States)

^c ALETHEIA UNIVERSITY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84895343979     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/978-3-540-72821-4_1     Document Type: Chapter

References (47)

1. Adcock A, Thangavel A, Whitfield-Gabrieli S, Knutson B, Gabrieli J (2006) Reward-motivated learning: mesolimbic activation precedes memory formation. Neuron 50(3): 507-517
2. Aha D (1997) Lazy learning. Kluwer
3. Aha D, Kibler D, Marc K (1991) Instance-based learning algorithms. Machine Learning 6(1): 37-66 (Pubitemid 21727227)
4. Alvarez-Diaz M, Alvarez A (2005) Genetic multi-model composite forecast for nonlinear prediction of exchange rates. Empirical Economics 30: 643-663 (Pubitemid 41386788)
5. Brenner T (2006) Agent learning representation: Advice on modelling economic learning. In: Tesfatsion L, Judd K (eds) Handbook of computational economics, volume 2: agent-based computational economics. North-Holland, Amsterdam: 895-947
6. Browne S (1997) Survival and growth with a liability: optimal portfolio strategies in continuous time. Mathematics of Operations Research 22(2): 468-493
7. Campbell J, Lo A, MacKinlay C (1997) The econometrics of financial markets. Princeton University Press, NJ
8. Cao L, Tay F (2003) Saliency analysis of support vector machines for feature selection in financial time series forecasting. In: Chen SH, Wang P (eds) Computational intelligence in economics and finance. Springer-Verlag: 182-199
9. Chen J, Xu D (1998) An economic forecasting system based on recurrent neural networks. In: Proceedings of IEEE international conference on systems, man, and cybernetics: 1762-1767
10. Chen SH (ed) (2002) Genetic algorithms and genetic programming in computational finance. Kluwer
11. Chen SH (2005) Computational intelligence in economics and finance: carrying on the legacy of Herbert Simon. Information Sciences 170: 121-131
12. Chen SH (2007a) Computationally intelligent agents in economics and finance. Information Sciences 177(5): 1153-1168
13. Chen SH (2007b) Graphs, networks and ACE. New Mathematics and Natural Computation 2(3): 299-314
14. Chen SH, He H (2003) Searching financial patterns with self-organizing maps. In Chen SH, Wang P (eds) Computational intelligence in economics and finance. Springer-Verlag: 203-216
15. Chen SH, Kuo TW (2002) Evolutionary computation in economics and finance: A bibliography. In: Chen SH (eds) Computational intelligence in economics and finance. Springer-Verlag: 419-455
16. Chen SH, Kuo TW (2003) Discovering hidden patterns with genetic programming. In Chen SH, Wang P (eds) Computational intelligence in economics and finance. Springer-Verlag: 329-347
17. Chen SH, Tai CC (2007) On the selection of adaptive algorithms in ABM: a computational-equivalence approach. Computational Economics 28: 313-331
18. Chen SH, Wang P (eds) (2003) Computational intelligence in economics and finance. Springer-Verlag
19. Chen SH, Kuo TW, Hoi KM (2007) Genetic programming and financial trading: how much about " What we Know" ? In: Zopounidis C, Doumpos M, Pardalos PM (eds) Handbook of financial engineering. Springer. Forthcoming
20. Cooper W, Seiford L, Tone K (2005) Introduction to data envelopment analysis and its uses: with DEA-Solver software and references. Springer
21. Das N (2003) Hedge fund classification using K-means method. EconPapers, No. 204
22. De Cock M (1999) Representing the adverb very in fuzzy set theory. ESSLLI Student Papers 1999: 223-232
23. Elamn J (1990) Finding structure in time. Cognitive Science 14: 179-211
24. Episcopos A, Davis J (1996) Predicting returns on Canadian exchange rates with artificial neural networks and EGARCHM-M models. Neural Computing and Application 4: 168-174
25. Ferńandez-Rodŕ?guez F, Sosvilla-Rivero S., Andrada-F́elix J (2003) Nearest-neighbour predictions in foreign exchange markets. In Chen SH, Wang P (eds) Computational intelligence in economics and finance. Springer-Verlag: 297-325
26. Florez-Lopez R (2003) Effective position of European firms in the face of monetary integration using Kohonen's SOFM. In Chen SH, Wang P (eds) Computational intelligence in economics and finance. Springer-Verlag: 217-233
27. Florez-Lopez R (2007) Strategic supplier selection in the added-value perspective: a CI approach. Information Science 177(5): 1169-1179 (Pubitemid 44908288)
28. Hann T, Steurer E (1996) Much ado about nothing? Exchange rate forecasting: neural networks vs. linear models using monthly and weekly data. Neurocomputing 10: 323-339 (Pubitemid 26170371)
29. Hersh H, Caramazza A (1976) A fuzzy set approach to modifiers and vagueness in natural language. Journal of Experimental Psychology-General 105 (3): 254-276
30. Hollans H, Munneke HJ (2003) Housing markets and house price appreciation: an Intercity Analysis. Working paper. Univeristy of Georgia
31. Jordan M (1986) Serial order: a parallel distributed processing approach. Technical Report, No. 8604. Institute for Cognitive Science, UC San Diego
32. Kohonen T (1995) Self-organizing Maps. Springer
33. Kovalerchuk B, Vityaev E (2000) Data mining in finance: advances in relational and hybrid methods. Kluwer
34. Kramer M (1990) Nonlinear principal analysis using autoassociative neural networks. AIChE Journal 37(2): 233-243
35. Kuan CM, Liu T (1995) Forecasting exchange rates using feedforward and recurrent neural networks. Journal of Applied Econometrics 10: 347-364
36. Kumbhakar S, Knox Lovell C (2003) Stochastic frontier analysis. Cambridge
37. MacQueen JB (1967) Some methods for classification and analysis of multivariate observations. Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability. University of California Press 1: 281-297
38. Mandic D, Chambers J (2001) Recurrent neural networks for prediction: learning Algorithms, architectures, and stability. John Wiley and Sons, New York
39. McNelis P (2005) Neural networks in finance: gaining predictive edge in the market. Elsvier
40. Overhaus M, Bermudez A, Buehler H, Ferraris A, Jordinson C, Lamnouar A (2007) Equity hybrid derivatives. Wiley Finance
41. Qian Y (2006) K-means algorithm and its application for clustering companies listed in Zhejiang province. WIT Transaction on Information and Communication Technologies 37: 35-44 (Pubitemid 350108031)
42. Rocco C, Moreno J (2003) A support vector machine model for currency crises discrimination. In: Chen SH, Wang P (eds) Computational intelligence in economics and finance. Springer-Verlag: 171-181
43. Shi S, Xu L, Liu B (1999) Improving the acuracy of nonlinear combined forecasting using neural networks. Expert Systems with Applications 16: 49-54 (Pubitemid 129667268)
44. Wei WX, Jiang ZH (1995) Artificial neural network forecasting model for exchange rate and empirical analysis. Forecasting 2: 67-V69
45. Weigend A, Huberman B, Rumelhart D (1992) Predicting sunspots and exchange rates with connectionist networks. In: Casdagli M, Eubank S (eds) Nonlinear modeling and forecasting. Addison-Wesley: 395V-432
46. Witten I, Frank E (2005) Data mining: practical machine learning tools and techniques (2nd Edition). Morgan Kaufmann
47. Wu B (1995) Model-free forecasting for nonlinear time series (with application to exchange rates). Computational Statistics and Data Analysis 19: 433-459