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International Conference on Intelligent Data Engineering and Automated Learning

IDEAL 2000: Intelligent Data Engineering and Automated Learning — IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents pp 532–537Cite as

Combining Ordinal Financial Predictions with Genetic Programming

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1983))

Abstract

Ordinal data play an important part in financial forecasting. For example, advice from expert sources may take the form of “bullish”, “bearish” or “sluggish”, or “buy” or “do not buy”. This paper describes an application of using Genetic Programming (GP) to combine investment opinions. The aim is to combine ordinal forecast from different opinion sources in order to make better predictions. We tested our implementation, FGP (Financial Genetic Programming), on two data sets. In both cases, FGP generated more accurate rules than the individual input rules.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Essex, Wivenhoe Park, CO4 3SQ, Colchester, Essex, UK

    Edward P.K. Tsang & Jin Li

Authors
  1. Edward P.K. Tsang
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  2. Jin Li
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong

    Kwong Sak Leung  & Lai-Wan Chan  & 

  2. Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong Kong

    Helen Meng

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© 2000 Springer-Verlag Berlin Heidelberg

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Tsang, E.P., Li, J. (2000). Combining Ordinal Financial Predictions with Genetic Programming. In: Leung, K.S., Chan, LW., Meng, H. (eds) Intelligent Data Engineering and Automated Learning — IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents. IDEAL 2000. Lecture Notes in Computer Science, vol 1983. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44491-2_77

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  • DOI: https://doi.org/10.1007/3-540-44491-2_77

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41450-6

  • Online ISBN: 978-3-540-44491-6

  • eBook Packages: Springer Book Archive

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