article

The role of predictability of financial series in emerging market applications

Authors:

Gabriela Prelipcean,

Mircea Boscoianu,

Nicolae PopoviciuAuthors Info & Claims

WSEAS Transactions on Mathematics, Volume 7, Issue 7

Pages 472 - 481

Published: 01 July 2008 Publication History

Abstract

A new metric that quantifies the predictability of financial time series is proposed. Time series predictability provides a measure of how well a time series can be modeled by a particular method, or how well a prediction can be made. This new time series predictability metric is developed based on the Kaboudan η -metric. The new metrics, based on Genetic Programming (GP) and Artificial Neural Networks (ANN) overcomes the stationarity problem presented in the pure η -metric and provides a new feature, which shows how the predictability changes over different subsequences in a time series. Timing detection and portfolio balancing should be based on trading strategies that evolved to optimize buy/sell decisions. The interest is to explore new trading rules based on an automated security trading decision support system triggered by both quantitative and qualitative factors. The focus is to develop quantitative metrics that characterize time series according to their ability to be modeled by a particular method, such as the predictability of a time series using the GP approach or an ANN.

References

[1]

G.E. Box, Jenkins G., Time series analysis: Forecasting and Control. Cambridge University Press, 1976.

[2]

W. Brock, Lakonishok J., LeBaron B. (1992) Simple Technical Trading Rules and the Stochastic Properties of Stock Returns, Journal of Finance XLVII, no 5, pp. 1731-1764.

[3]

D. Fogel, "Preliminary experiments on discriminating between chaotic signals and noise using evolutionary programming." proceedings of Genetic Programming 1996, pp. 512-520.

[4]

M. Kaboudan, "Genetic Programming Prediction of Stock Prices," Computational Economics, 2007.

[5]

R. J. Povinelli, Time Series Data Mining: Identifying Temporal Patterns for Characterization and Prediction of Time Series Events, Ph.D. Dissertation.

[6]

W.G. David, H. Wolpert, "No Free Lunch Theorems for Optimization," IEEE Trans. on Evolutionary Computation, vol. 1, pp. 67-82, 1997.

[7]

B. Freisleben, "Stock Market Prediction with Back propagation Networks," Lecture on computer science, vol. 604, 1992.

[8]

J. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA: The MIT Press, 1992.

Digital Library

[9]

Akaike, "A new look at the statistical model identification," IEEE Trans. Auto Control, vol. 19, 1994.

[10]

N. Popoviciu, Neural Networks. Mathematical Foundation, Algorithms and Applications, Hyperion University Press, Bucharest (in press, 2008).

[11]

G. Prelipcean, M. Boscoianu, "Some aspects regarding the dynamic correlation between different types of strategic investments in Romania after integration", Proceedings of the 9th WSEAS International Conference on Mathematics and Computers in Business and Economics (MCBE '08), Bucharest, Romania, Published by WSEAS Press, p. 162-166.

[12]

G. Prelipcean, M. Boscoianu, "Computational Framework for Assessing Decisions in Energy Investments Based on a Mix between Real Option Analysis and Artificial Neural Networks", Proceedings of the 9th WSEAS International Conference on Mathematics and Computers in Business and Economics (MCBE '08), Bucharest, Romania, Published by WSEAS Press, p. 179-184.

[13]

S.S. Rao, "Evolving reduced parameter bilinear models for time series prediction using fast evolutionary programming." Proceedings of the First Annual Conference, Cambridge, MA, 1996, pp 528-535.

[14]

A.N. Refenes, Burgess A.N., Bentz Y. (1997) Neural Networks in Financial Engineering: A Study in Methodology. IEEE Transactions on Neural Networks 8 (6), pp. 1222-1267.

Digital Library

[15]

M. Stoica, "Fuzzy sets and their apllications", Proceedings of the 9th WSEAS International Conference on Mathematics and Computers in Business and Economics (MCBE '08), Bucharest, Romania, Published by WSEAS Press, p. 162-166.

[16]

X. Yao, "Fast evolutionary programming," Proceedings of 5th Annual Conference on Evolutionary Programming, Cambridge, MA, 1996, pp. 451-456.

[17]

Y. Zhang, "A Reduced Parameter Bilinear Time Series Model." IEEE Trans Signal Processing, vol. 42, 1994.

Cited By

Prelipcean GBoscoianu MMoisescu F(2010)New ideas on the artificial intelligence support in military applicationsProceedings of the 9th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases10.5555/1808036.1808044(34-39)Online publication date: 20-Feb-2010
https://dl.acm.org/doi/10.5555/1808036.1808044
Prelipcean GMoisescu FBoscoianu MCiufudean CCioban G(2009)A hybrid trading system for defence procurement applicationsProceedings of the 9th WSEAS international conference on Simulation, modelling and optimization10.5555/1627368.1627406(173-178)Online publication date: 3-Sep-2009
https://dl.acm.org/doi/10.5555/1627368.1627406

Index Terms

The role of predictability of financial series in emerging market applications

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Published In

cover image WSEAS Transactions on Mathematics

WSEAS Transactions on Mathematics Volume 7, Issue 7

July 2008

84 pages

ISSN:1109-2769

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Publisher

World Scientific and Engineering Academy and Society (WSEAS)

Stevens Point, Wisconsin, United States

Publication History

Published: 01 July 2008

Revised: 09 June 2008

Received: 14 February 2008

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Cited By

Prelipcean GBoscoianu MMoisescu F(2010)New ideas on the artificial intelligence support in military applicationsProceedings of the 9th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases10.5555/1808036.1808044(34-39)Online publication date: 20-Feb-2010
https://dl.acm.org/doi/10.5555/1808036.1808044
Prelipcean GMoisescu FBoscoianu MCiufudean CCioban G(2009)A hybrid trading system for defence procurement applicationsProceedings of the 9th WSEAS international conference on Simulation, modelling and optimization10.5555/1627368.1627406(173-178)Online publication date: 3-Sep-2009
https://dl.acm.org/doi/10.5555/1627368.1627406

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