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Pattern recognition and financial time‐series

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  • Dave Elliman

Abstract

This paper investigates financial time‐series from the perspective of a practitioner in artificial intelligence methods and pattern recognition. It presents results from statistical experiments which suggest that financial markets operate with a measure of inefficiency and predictability. However, identifying the nature of any regularities and patterns presents a difficult challenge to the artificial intelligence community, in that established techniques make assumptions about the underlying process that mostly prove to be invalid for this class of data. Copyright © 2007 John Wiley & Sons, Ltd.

Suggested Citation

  • Dave Elliman, 2006. "Pattern recognition and financial time‐series," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 14(3), pages 99-115, July.
    • Handle: RePEc:wly:isacfm:v:14:y:2006:i:3:p:99-115
    DOI: 10.1002/isaf.279
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    References listed on IDEAS

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    1. Didier Sornette & Wei-Xing Zhou, 2002. "The US 2000-2002 market descent: How much longer and deeper?," Quantitative Finance, Taylor & Francis Journals, vol. 2(6), pages 468-481.
    2. Robert J. Shiller, 1992. "Market Volatility," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262691515, December.
    3. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
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    1. repec:cup:judgdm:v:10:y:2015:i:5:p:416-428 is not listed on IDEAS
    2. Sigrid Møyner Hohle & Karl Halvor Teigen, 2015. "Forecasting forecasts: The trend effect," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 10(5), pages 416-428, September.

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