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.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Fama E F. Efficient capital markets: A review of theory and empirical work. Journal of Finance, 1970, 25(2): 383–417
Shleifer A. Inefficient Markets: An Introduction to Behavioral Finance. Oxford University Press, 2000
Holland J H. Adaptation in Natural and Artificial Systems. Ann Arbor: University of Michigan Press, 1975
Koza J R. Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge: MIT Press, 1992
Koza J R. Genetic Programming II: Automatic Discovery of Reusable Programs. Cambridge: MIT Press, 1994
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
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
Li J, Tsang E P K. Reducing failure in investment recommendations using genetic programming. In: Proceedings of Computing in Economics and Finance Conference, 2000
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
Brock W, Lakonishok J, LeBaron B. Simple technical trading rules and the stochastic properties of stock returns. Journal of Finance, 1992, 47:1731–1764
Sweeney R J. Some new filter rule tests: Methods and results, Journal of Financial and Quantitative Analysis, 1998, 23: 285–300
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
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
Bishop C M. Neural Networks for Pattern Recognition. Oxford University Press, 1995
Tsang E P K. Foundations of Constraint Satisfaction. London and San Diego: Academic Press, 1993
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
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
Muthoo A. Bargaining Theory with Applications. Cambridge: Cambridge University Press, 1999
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
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
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
Garcá-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
Garcá-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
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
Quinlan J R. Data mining tools See5 and C5.0, http://www.rulequest.com/see5-info.html (accessed 25 August 2008)
Simon H. Models of Man. New York: Wiley, 1957
Rubinstein A. Modeling Bounded Rationality. MIT Press, 1998
Tsang E P K. Computational intelligence determines effective rationality. International Journal on Automation and Control, 2008, 5(11):63–66
Olsen R. Classical economics: an emperor with no clothes. Wilmott Magazine, 2005, 15: 84–85
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Tsang, E. Forecasting — where computational intelligence meets the stock market. Front. Comput. Sci. China 3, 53–63 (2009). https://doi.org/10.1007/s11704-009-0012-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11704-009-0012-8