Forecasting- where computational intelligence meets the stock market

doi:10.1007/s11704-009-0012-8

Front Comput Sci Chin

2009, Vol. 3

Issue (1) : 53-63 https://doi.org/10.1007/s11704-009-0012-8

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Forecasting- where computational intelligence meets the stock market

Edward TSANG(

)

School of Computer Science and Electronic Engineering, University of Essex, Colchester C04 3SQ, UK

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Abstract

Forecasting is an important activity in finance. Traditionally, forecasting has been done with in-depth knowledge in finance and the market. Advances in computational intelligence have created opportunities that were never there before. Computational finance techniques, machine learning in particular, can dramatically enhance our ability to forecast. They can help us to forecast ahead of our competitors and pick out scarce opportunities. This paper explains some of the opportunities offered by computational intelligence and some of the achievements so far. It also explains the underlying technologies and explores the research horizon.

Keywords forcasting computational finance evolutionary computation computational intelligence machine learning

Corresponding Author(s): TSANG Edward,Email:edward@essex.ac.uk

Issue Date: 05 March 2009

Cite this article:

Edward TSANG. Forecasting- where computational intelligence meets the stock market[J]. Front Comput Sci Chin, 2009, 3(1): 53-63.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-009-0012-8
https://academic.hep.com.cn/fcs/EN/Y2009/V3/I1/53

1	Fama E F. Efficient capital markets: A review of theory and empirical work. Journal of Finance , 1970, 25(2): 383-417 doi: 10.2307/2325486
2	Shleifer A. Inefficient Markets: An Introduction to Behavioral Finance. Oxford University Press , 2000
3	Holland J H. Adaptation in Natural and Artificial Systems. Ann Arbor: University of Michigan Press, 1975
4	Koza J R. Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge: MIT Press, 1992
5	Koza J R. Genetic Programming II: Automatic Discovery of Reusable Programs. Cambridge: MIT Press, 1994
6	Neely C, Weller P, Ditmar R. Is technical analysis in the foreign exchange market profitable? A genetic programming approach, Journal of Financial and Quantitative Analysis , 1997, 32: 405-26 doi: 10.2307/2331231
7	Tsang E P K, Butler J M, Li J. EDDIE beats the bookies. International Journal of Software, Practice and Experience. Wiley , 1998, 28(10): 1033-1043 doi: 10.1002/(SICI)1097-024X(199808)28:10<1033::AID-SPE198>3.0.CO;2-1
8	Li J, Tsang E P K. Reducing failure in investment recommendations using genetic programming. In: Proceedings of Computing in Economics and Finance Conference , 2000
9	Tsang E P K, Yung P, Li J. EDDIE-Automation, a decision support tool for financial forecasting, Journal of Decision Support Systems. Special Issue on Data Mining for Financial Decision Making , 2004, 37: 559-565
10	BrockW, Lakonishok J, LeBaron B. Simple technical trading rules and the stochastic properties of stock returns. Journal of Finance , 1992, 47: 1731-1764 doi: 10.2307/2328994
11	Sweeney R J. Some new filter rule tests: Methods and results, Journal of Financial and Quantitative Analysis , 1998, 23: 285-300 doi: 10.2307/2331068
12	Li J, Tsang E P K. Investment decision making using FGP: a case study. In: Proceedings of Congress on Evolutionary Computation (CEC’99). IEEE Press , 1999, 6-9
13	Tsang E P K, Li J. EDDIE for financial forecasting. In: Chen S-H, ed. Genetic Algorithms and Programming in Computational Finance, Kluwer Series in Computational Finance , 2002, 161-174
14	Bishop C M. Neural Networks for Pattern Recognition. Oxford University Press , 1995
15	Tsang E P K. Foundations of Constraint Satisfaction. London and San Diego: Academic Press, 1993
16	Jin N. Equilibrium selection by co-evolution for bargaining problems under incomplete information about time preferences. In: Proceedings of Congress on Evolutionary Computation , 2005, 2661-2668
17	Jin N, Tsang E P K. Co-adaptive Strategies for Sequential Bargaining Problems with Discount Factors and Outside Options. In: Proceedings of Congress on Evolutionary Computation (CEC) , 2006, 7913-7920
18	Muthoo A. Bargaining Theory with Applications. Cambridge: Cambridge University Press , 1999
19	Gosling T, Jin N, Tsang E P K. Games, supply chains and automatic strategy discovery using evolutionary computation. In: Rennard J-P, ed. Handbook of research on nature-inspired computing for economics and management , 2007, 2: 572-588
20	Tsang E P K, Markose S, Er H. Chance discovery in stock index option and future arbitrage. New Mathematics and Natural Computation. World Scientific , 2005, 1(3): 435-447 doi: 10.1142/S1793005705000251
21	Martinez-Jaramillo S. Artificial financial markets: an agent based approach to reproduce stylized facts and to study the Red Queen Effect. PhD Thesis. Centre for Computational Finance and Economic Agents (CCFEA), University of Essex , 2007
22	García-Almanza A. New classification methods for gathering patterns in the context of genetic programming. PhD Thesis. Department of Computing and Electronic Systems, University of Essex , 2008
23	García-Almanza A L, Tsang E P K. Detection of stock price movements using chance discovery and genetic programming, International Journal of Knowledge-based and Intelligent Engineering Systems , 2007, 11(5): 329-344
24	Quinlan J R. Improved use of continuous attributes in C4.5. Journal of Artificial Intelligence Research. AI Access Foundation and Morgan Kaufmann Publishers , 1996, 4: 77-90
25	Quinlan J R. Data mining tools See5 and C5.0, http://www. rulequest.com/see5-info.html (accessed 25 August 2008)
26	Simon H. Models of Man. New York: Wiley, 1957
27	Rubinstein A. Modeling Bounded Rationality. MIT Press , 1998
28	Tsang E P K. Computational intelligence determines effective rationality. International Journal on Automation and Control , 2008, 5(11): 63-66 doi: 10.1007/s11633-008-0063-6
29	Olsen R. Classical economics: an emperor with no clothes. Wilmott Magazine , 2005, 15: 84-85

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