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Combining heterogeneous classifiers for stock selection

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Albanis, George T. and Batchelor, R. A. (1999) Combining heterogeneous classifiers for stock selection. Working Paper. Coventry: Warwick Business School, Financial Econometrics Research Centre. Working papers (Warwick Business School. Financial Econometrics Research Centre) (No.02-).

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Official URL: http://www2.warwick.ac.uk/fac/soc/wbs/research/wfr...

Abstract

Combining unbiased forecasts of continuous variables necessarily reduces the error variance below that of the median individual forecast. However, this does not necessarily hold for forecasts of discrete variables, or where the costs of errors are not directly related to the error variance. This paper investigates empirically the benefits of combining forecasts of outperforming shares, based on five linear and nonlinear statistical classification techniques, including neural network and recursive partitioning methods. We find that simple “Majority Voting” improves accuracy and profitability only marginally. Much greater gains come from applying the “Unanimity Principle”, whereby a share is not held in the high-performing portfolio unless all classifiers agree.

Item Type:

Working or Discussion Paper (Working Paper)

Subjects:

H Social Sciences > HG Finance
H Social Sciences > HB Economic Theory

Divisions:

Faculty of Social Sciences > Warwick Business School > Financial Econometrics Research Centre
Faculty of Social Sciences > Warwick Business School

Library of Congress Subject Headings (LCSH):

Economic forecasting, Stock exchange, Discriminant analysis, Recursive partitioning

Series Name:

Working papers (Warwick Business School. Financial Econometrics Research Centre)

Publisher:

Warwick Business School, Financial Econometrics Research Centre

Place of Publication:

Coventry

Official Date:

September 1999

Dates:

Date	Event
September 1999	Completion

Number:

No.02-

Number of Pages:

Status:

Not Peer Reviewed

Access rights to Published version:

Open Access

References:

Albanis G.T., and R. A. Batchelor, 1999a, Using probabilistic neural networks and rule induction
techniques to predict long-term bond ratings, Proceedings of SCI /ISAS Conference Orlando, 3, 175-80.
Albanis G.T., and R. A. Batchelor, 1999b, Bond ratings prediction: the value of nonlinear dimensionality reduction, Journal of Computational Intelligence in Finance, forthcoming.
Albanis, G. T. and Roy A. Batchelor, 1999c, Economic versus accounting factors in predicting excess stock returns, Discussion Paper, City University Business School, London.
Altman, E. I., Financial ratios, discriminant analysis and the prediction of corporate bankruptcy, Journal of Finance, 23, 589 - 609, 1968.
Ali K.M. and Pazzani M.J., 1995, Error reduction through learning multiple descriptions, Dept. of Information and Computer Science Technical Report 95-39, University of California, Irvine.
Alpaydin E., 1998, Techniques for Combining Multiple Learners, Proceedings of Engineering of Intelligent Systems Conference, Vol. 2, pp. 6-12, ICSC Press.
Arnott, R., and W. Copeland, 1985, The business cycle and security selection, Financial Analysts Journal, March-April, 26-33.
Asness, C., 1997, The interaction of value and momentum strategies, Financial Analysts Journal, March-April, 29-36.
Balakrishnan, P. V., Cooper, M. C., Jacob, V. S., and Lewis, P. A., 1994, A study of the classification capabilities of neural networks using unsupervised learning: a comparison with k-means clustering, Psychometrika, 59, 509-525
Batchelor R. A., and P. Dua , 1995, Forecaster diversity and the benefits of combining forecasts, Management Science, 41, 68-75.
Bates J. M. Granger C. W. J., 1969, The combination of forecasts, Operations Research Quarterly, 20, 451-468.
Battiti R. and Colla A.M., 1994, Democracy in neural nets: voting schemes for classification, Neural Networks 7 : 691-707.
Breiman L., 1994, Bagging Predictors, Technical Report 421, Department of Statistics, University of California, Berkeley.
Breiman L., Friedman J.H., Olsen E.A. and Stone C.J., 1984, Classification and Regression Trees, Belmont, CA: Wadsworth International Group.
Brock W. A. LeBaron B. Lakonishok J., 1992, Simple technical trading rules and the stochastic properties of stock returns, Journal of Finance, 47, 1731-1764.
Clemen R. T., 1989, Combining forecasts: a review and annotated bibliography, International Journal of Forecasting, 5, 559-581.
Cohen W. W., 1995, Fast effective rule induction, Machine Learning: Proceedings of the Twelfth International Conference, Morgan Kaufman.
Deakin, E. B., 1976, Distributions of financial accounting ratios: some empirical evidence, The Accounting Review, January, 90 – 96.
Diebold F. X. and J. A. Lopez , 1995, Forecast evaluation and combination, in Maddala G. S. and C. R. Rao (Eds), Handbook of Statistics Volume 14: Statistical Methods in Finance, Amsterdam: North Holland.
Dubes, R. and A. K. Jain, 1976, Cluster analysis: the user’s dilemma, Pattern Recognition, 8, 247-260.
Fama, E. F., 1991, Efficient capital markets II, Journal of Finance, 46, 1575-1617.
Fama, E. F., and K. R. French, 1995, Size and book-to-market factors in earnings and returns, Journal of Finance, 50, 131-154.
Fama, E. F., and K. R. French, 1996, Multifactor explanations of asset pricing anomalies, Journal of Finance, 51, 55-84.
Fisher R.A., 1936, The use of measurements in taxonomical problems, Annals of Eugenics 8, pp. 179-184.
Frecka, T. J. and Hopwood, W. S., 1983, The effects of outliers on the cross-sectional distributional properties of financial ratios”, The Accounting Review, 58, 1.
Frydman, H., E. I. Altman and D-L. Kao, 1985, Introducing recursive classification for financial classification: the case of financial distress, Journal of Finance, 40, 269-291.
Hand, D. J., 1997, Construction and Assessment of Classification Rules, Chichester; Wiley.
Heath D. , Kasif, S., and Salzberg S., 1996, Committees of decision trees, In Gorayska B. and Mey J. (eds), Cognitive Technology: In Search of a Human Interface, Elsevier Science, Amsterdam, The Netherlands. 305-317, 1996.
Hansen L.K. and Salamon P., 1990, Neural Network Ensembles”, IEEE Transactions on Pattern Analysis and Machine Intelligence 12:993-1001.
Ippolito, R. A., 1989, Efficiency with costly information: a study of mutual fund performance 1965-1984, Quarterly Journal of Economics..
Jensen, M. C., 1968, Problems in the selection of security portfolios: the performance of mutual funds in the period 1945-64, Journal of Finance, 23,
Kaplan, S. and G. Urwitz, 1979, Statistical models of bond ratings: a methodological inquiry”, Journal of Business, 52, 2, 231-261
Kohonen, T., 1988, Learning vector quantization, Neural Networks, 1, (Supplement)
Krogh A. and Vedelsby, 1995, Neural network ensembles, cross validation, and active learning, in G. Tesauro, D.S. Touretzky, and T.K. Leen (Eds), Advances in Neural Information Processing Systems 7, MIT Press, pp. 231-238.
Leitch G., and J. E. Tanner , 1991, Economic forecast evaluation: profit versus conventional error measures, American Economic Review, 81, 580-590.
Lo A. , and A. C. McKinlay , 1988, Stock prices do not follow random walks: evidence from a simple specification test, Review of Financial Studies, 1, 41-66.
Michie, D., D. J. Spiegelhafter and C. C. Taylor (Eds), 1994, Machine Learning and Statistical Classification, Ellis Horwood, McNees S. K., 1992, The uses and abuses of ’consensus’ forecasts, Journal of Forecasting, 11, 703-710.
Makridakis S. Andersen A. Carbone, R., Fildes, R., Hibon, M., Lewandwski, R., Newton, J., Parzen, E. Winkler, R., 1982, The accuracy of time series (extrapolative) methods: results of a forecasting competition, Journal of Forecasting, 1, 111-153.
Mani, G.,1991, Lowering variance of decisions by artificial neural network ensembles, Neural Computation, Vol. 3, pp. 484-486.
Murthy, S. K., Kasif, S. and S. Salzberg, 1994, A system for induction of oblique decision trees, Journal of Artificial Intelligence Research, 2, 1-32.
Pesaran M. H., and Timmerman A. , 1994, Forecasting stock returns - an examination of stock market trading in the presence of transactions costs, Journal of Forecasting, 13, 335-367.
Sharpe, W. F., 1992, Asset allocation: management style and performance measurement, Journal of Portfolio Management, Winter, 7-19.
Specht D., 1990, Probabilistic neural networks, Neural Networks 3, pp. 109-118.
Tyree, E. and Long, J.A., 1996, Assessing financial distress with Probabilistic Neural Networks”, in A.N. Refenes (ed.) - Proceedings of the Third International Conference on Neural Networks in the Capital Markets, London Business School.
Xu L., Krzyzak, Suen C.Y. “Methods of Combining Multiple Classifiers and their Applications to Handwriting Recognition”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 22, pp. 418-435, 1992.