Foreign exchange market prediction with multiple classifiers

Journal of Forecasting

Volumn 29, Issue 3, 2010, Pages 271-284

  Qian, Bo ^a   Rasheed, Khaled ^a  

^a UNIVERSITY OF GEORGIA   (United States)

Author keywords

Efficient market hypothesis; Foreign exchange market prediction; Hurst exponent; Machine learning; Model ensemble

Indexed keywords

COMMERCE; DECISION TREES; ELECTRONIC TRADING; FORECASTING; LEARNING SYSTEMS; NEAREST NEIGHBOR SEARCH; NEURAL NETWORKS; RANDOM PROCESSES;

EFFICIENT MARKET HYPOTHESIS; FOREIGN EXCHANGE; FOREIGN EXCHANGE MARKET PREDICTION; HURST EXPONENTS; MACHINE-LEARNING; MARKET PREDICTION; MARKET PRICE; MODEL ENSEMBLES; MULTIPLE-CLASSIFIERS; RANDOM WALK;

FINANCIAL MARKETS;

EID: 77949615819     PISSN: 02776693     EISSN: 1099131X     Source Type: Journal    
DOI: 10.1002/for.1124     Document Type: Article

References (50)

1. Aha DW, Kibler D, Albert MK. 1991. Instance-based learning algorithms. Machine Learning 6: 37-66.
2. Alexander SS. 1961. Price movements in speculative markets: trends or random walks. Industrial Management Review May: 7-26.
3. Baestaens DE, van den Bergh WM, Vaudrey H. 1996. Market inefficiencies, technical trading and neural networks. In Forecasting Financial Markets, Financial Economics and Quantitative Analysis, Christian D (ed.). Wiley: Chichester; 254-260.
4. Breiman L. 1996. Bagging predictors. Machine Learning 24(2): 123-140.
5. Breiman L, Friedman JH, Stone CJ, Olshen RA. 1984. Classification and Regression Trees. Chapman & Hall (Wadsworth, Inc.): New York.
6. Butler KC, Malaikah JS. 1992. Efficiency and inefficiency in thinly traded stock markets: Kuwait and Saudi Arabia. Journal of Banking and Finance 16(1): 197-210.
7. Cao L. 1997. Practical method for determining the minimum embedding dimension of a scalar time series. Physica D 110: 43-50.
8. Cootner PH. 1964. The Random Character of Stock Market Prices. MIT Press: Cambridge, MA.
9. Corazza M, Malliaris AG. 2002. Multi-fractality in foreign currency markets. Multinational Finance Journal 6(2): 65-98.
10. Dietterich TG. 2000. Ensemble methods in machine learning. In First International Workshop on Multiple Classifier Systems, New York.
11. Dietterich TG, Winter 1997. Machine-learning research: four current direction. AI Magazine 18(4): 97-136.
12. Fama EF, Fisher L, Jensen MC, Roll WR. 1969. The adjustment of stock price to new information. International Economic Review 10(1): 1-21.
13. Fama EF. 1991. Efficient capital markets: II. Journal of Finance 46(5): 1575-1617.
14. Fama EF. 1965. The behaviour of stock market prices. Journal of Business 38: 34-105.
15. Frank RJ, Davey N, Hunt SP. 2000. Input window size and neural network predictors. In IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00), Vol.2; 2237-2242.
16. Gallagher LA, Taylor MP. 2002. Permanent and temporary components of stock prices: evidence from ssessing macroeconomic stocks. Southern Economic Journal 69: 245-262.
17. Gately E. 1996. Neural Networks for Financial Forecasting. Wiley: New York.
18. Giles CL, Lawrence S, Tsoi AC. July 2001. Noisy time series prediction using a recurrent neural network and grammatical inference. Machine learning 44(1-2): 161-183.
19. Grech D, Mazur Z. 2004. Can one make any crash prediction in finance using the local Hurst exponent idea? Physica A: Statistical Mechanics and its Applications 336: 133-145.
20. Hagan MT, Menhaj M. 1994. Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks 5(6): 989-993.
21. Hagan MT, Demuth HB, Beale MH. 1996. Neural Network Design. PWS Publishing: Boston, MA.
22. Hansen LK, Salamon P. 1990. Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence 12: 993-1001.
23. Hellstrom T, Holmstrom K. 1998. Predicting the Stock Market. Technical Report Series IMa-TOM-1997-2007 Center of Mathematical Modeling, Mälardalen University, Västerås, Sweden.
24. Hornik K, Stinchcombe MB, White H. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2(5): 259-366.
25. Hsieh DA. 1991. Chaos and nonlinear dynamics: application to financial markets. Journal of Finance 46: 1839-1877.
26. Hurst EH. 1951. Long-term storage of reservoirs: an experimental study. Transactions of the American Society of Civil Engineers 116: 770-799.
27. Jensen MC. 1978 Some anomalous evidence regarding market efficiency. Journal of Financial Economics 6: 95-102.
28. Kavussanos MG, Dockery E. October 2001. A multivariate test for stock market efficiency: the case of ASE. Applied Financial Economics 11(5): 573-579.
29. Lo AW, MacKinlay CA. 1997. Stock market prices do not follow random walks. Market Efficiency: Stock Market Behaviour in Theory and Practice 1: 363-389.
30. Mandelbrot BB. 1982. The Fractal Geometry of Nature. W. H. Freeman: New York.
31. Mandelbrot BB, Ness WVJ. 1968. Fractional Brownian motions, fractional noises and applications. SIAM Review 10: 422-437.
32. May CT. 1999. Nonlinear Pricing: Theory and Applications. Wiley: New York.
33. Merkwirth C, Parlitz U, Wedekind I, Lauterborn W. 2002. TSTOOL user manual. http://www.physik3.gwdg.de/ tstool/manual.pdf [5 February 2006].
34. Mitchell T. 1997. Decision tree learning. In Machine Learning, Mitchell T (ed.). McGraw-Hill: New York; 52-78.
35. Nir F, Dan G, Moises G. 1997. Bayesian network classifier. Machine Learning 29: 131-163.
36. Peters EE. 1991. Chaos and Order in the Capital Markets: A New View of Cycles, Prices, and Market Volatility. Wiley: New York.
37. Peters EE. 1994. Fractal Market Analysis: Applying Chaos Theory to Investment and Economics. Wiley: New York.
38. Qian B, Rasheed K. 2004. Hurst exponent and financial market predictability. In Proceedings of the 2nd IASTED International Conference on Financial Engineering and Applications, Cambridge, MA; 203-209.
39. Refenes AP. 1995. Neural Networks in the Capital Markets. Wiley: New York.
40. Rish I. 2001. An empirical study of the naive Bayes classifier. In IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, Seattle, WA.
41. Schapire RE, Freund Y, Bartlett P, Lee WS. 1997. Boosting the margin: a new explanation for the effectiveness of voting methods. In Proceedings of the Fourteenth International Conference on Machine Learning. Morgan Kaufmann: San Francisco, CA; 322-330.
42. Soofi AS, Cao L. 2002. Modelling and Forecasting Financial Data: Techniques of Nonlinear Dynamics. Kluwer: Norwell, MA.
43. Stone M. 1974. Cross-validatory choice and assessment of statistical prediction. Journal of the Royal Statistical Society B 36: 111-120.
44. Takens F. 1981. Dynamical System and Turbulence. Lecture Notes in Mathematics 898. Springer: Berlin.
45. Ting K, Witten IH. 1999. Issues in stacked generalization. Journal of Artificial Intelligence Research 10: 271-289.
46. Tsibouris G, Zeidenberg M. 1995. Testing the efficient markets hypothesis with gradient descent algorithms. In Neural Networks in the Capital Markets, Refenes AP (ed.). Wiley: Chichester; 127-136.
47. Walczak S. 2001. An empirical analysis of data requirements for financial forecasting with neural networks. Journal of Management Information Systems 17(4): 203-222.
48. White H. 2000. A reality check for data snooping. Econometrica 68(5): 1097-1126.
49. Wolpert DH. 1992. Stacked generalization. Neural Networks 5: 241-259.
50. Zirilli JS. 1997. Financial Prediction using Neural Networks. International Thomson Computer Press: London.