Abstract
Stock market prediction is attractive and challenging. According to the efficient market hypothesis, stock prices should follow a random walk pattern and thus should not be predictable with more than about 50 percent accuracy. In this paper, we investigated the predictability of the Dow Jones Industrial Average index to show that not all periods are equally random. We used the Hurst exponent to select a period with great predictability. Parameters for generating training patterns were determined heuristically by auto-mutual information and false nearest neighbor methods. Some inductive machine-learning classifiers—artificial neural network, decision tree, and k-nearest neighbor were then trained with these generated patterns. Through appropriate collaboration of these models, we achieved prediction accuracy up to 65 percent.
Similar content being viewed by others
References
Fama EF, Fisher L, Jensen M, Roll R (1969) The adjustment of stock price to new information. Int Eco Rev 10(1):1–21
Fama EF (1991) Efficient capital markets: II J Fin 46(5):1575–1617
Cootner PH (1964) The random character of stock market prices. MIT Press, MA
Fama EF (1965) The behaviour of stock market prices. J Bus 38:34–105
Alexander SS (1961) Price movements in speculative markets: Trends or random walks. Ind Manage Rev pp 7–26
Jensen MC (1978) Some anomalous evidence regarding market efficiency. J Fin Eco 6:95–102
Gallagher L, Taylor M (2002) Permanent and temporary components of stock prices: Evidence from assessing macroeconomic stocks. Southern Eco J 69:245–262
Lo AW, MacKinlay AC (1997) Stock market prices do not follow random walks. Market Efficiency: Stock Market Behaviour in Theory and Practice 1:363–389
Kavussanos MG, Dockery E (2001) A multivariate test for stock market efficiency: The case of ASE Applied Financial Economics 11(5):573–579(7)
Kirt CB, Malaikah SJ (1992) Efficiency and inefficiency in thinly traded stock markets: Kuwait and Saudi Arabia. J Bank & Fin 16(1):197–210
Walczak S (2001) An empirical analysis of data requirements for financial forecasting with neural networks. J Manag Infor Syst 17(4):203–222
Baestaens DJE, van den Bergh WM, Vaudrey H (1996) Market inefficiencies, technical trading and neural networks. In: Dunis C (ed) forecasting financial markets, financial economics and quantitative analysis. John Wiley & Sons, Chichester, England, pp 254– 260
Tsibouris G, Zeidenberg M (1995) Testing the efficient markets hypothesis with gradient descent algorithms. In: Refenes AP (ed) Neural networks in the capital markets. John Wiley & Sons, Chichester, England, Chap 8, pp 127–136
Hellstrom T, Holmstrom K (1998) Predicting the stock market, technical report series IMa-TOM-1997-07, Center of Mathematical Modeling, Malardalen University
Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Net 2(5):259–366
Refenes A (1995) Neural networks in the capital markets. Wiley, New York
Gately E (1996) Neural networks for financial forecasting. Wiley, New York
Zirilli JS (1997) Financial prediction using neural networks. International Thomson Computer Press, UK
White H (2000) A reality check for data snooping. Econometrica 68(5):1097–1126
Hurst HE (1951) Long-term storage of reservoirs: an experimental study. Trans Amer Soc Civil Engi 116:770–799
Mandelbrot BB, Ness JV (1968) Fractional brownian motions, fractional noises and applications. SIAM Rev 10:422–437
Mandelbrot B (1982) The fractal geometry of nature. WH Freeman, New York
May CT (1999) Nonlinear pricing: theory & applications. Wiley, New York
Corazza M, Malliaris AG (2002) Multi-fractality in foreign currency markets. Multinat Fin J 6(2):65–98
Grech D, Mazur Z (2004) Can one make any crash prediction in finance using the local Hurst exponent idea?. Physica A: Statistical Mech Appl 336:133–145
Peters EE (1991) Chaos and order in the capital markets: a new view of cycles, prices, and market volatility. Wiley, New York
Peters EE (1994) Fractal market analysis: applying chaos theory to investment and economics. Wiley, New York
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, USA, pp 203–209
Walczak S (2001) An empirical analysis of data requirements for financial forecasting with neural networks. J Manag Infor Syst 17(4):203–222
Hsieh DA (1991) Chaos and nonlinear dynamics: application to financial markets. J Fin 46:1839–1877
Takens F (1981) Dynamical system and turbulence. In: Rand A, Young Ls (eds) Lecture notes in mathematics, 898(Warwick 1980). Springer, Berlin
Cao L (1997) Practical method for determining the minimum embedding dimension of a scalar time series. Physica D 110:43–50
Soofi AS, Cao L (2002) Modelling and forecasting financial data: techniques of nonlinear dynamics. Kluwer Academic Publishers: Norwell, Massachusetts
Frank RJ, Davey N, Hunt SP (2000) Input window size and neural network predictors. IEEE-INNS-ENNS Int Joint Conf Neural Netw (IJCNN’00)-Vol 2, pp. 2237–2242
Merkwirth C, Parlitz U, Wedekind I, Lauterborn W (2002) TSTOOL user manual, http://www.physik3.gwdg.de/tstool/manual.pdf
Hagan MT, Demuth HB, Beale MH (1996) Neural network design. PWS Publishing, Boston, MA
Hagan MT, Menhaj M (1994) Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Netw 5(6):989–993
Aha D, Kibler DW, Albert MK (1991) Instance-based learning algorithms. Mach Learn 6:37–66
Mitchell T (1997) Decision tree learning, machine learning. The McGraw-Hill Companies, Inc., pp 52–78
Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification and regression trees. Chapman & Hall (Wadsworth, Inc.), New York
Stone M (1974) Cross-validatory choice and assessment of statistical prediction. J Roy Statistic Soc B 36:111–120
Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140
Dietterich TG (1997) Machine-learning research: Four current direction. AI Magazine 18(4):97–136
Hansen L, Salamon P (1990) Neural network ensembles. IEEE Trans Patt Analy Mach Intell (12):993–1001
Dietterich TG (2000) Ensemble methods in machine learning. First International Workshop on Multiple Classifier Systems, New York
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, pp 322–330
Wolpert DH (1992) ‘Stacked generalization,’ Neural networks. Pergamon Press, vol 5, pp. 241–259
Ting KM, Witten IH (1999) Issues in stacked generalization. J Artif Intell Res 10:271–289
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Qian, B., Rasheed, K. Stock market prediction with multiple classifiers. Appl Intell 26, 25–33 (2007). https://doi.org/10.1007/s10489-006-0001-7
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-006-0001-7