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Proceedings of SAI Intelligent Systems Conference

IntelliSys 2016: Intelligent Systems and Applications pp 1–14Cite as

Pattern Sets for Financial Prediction: A Follow-Up

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 751))

Abstract

As a follow-up to an earlier investigation, a true forward test has been carried out by applying a previously developed financial predictor (in the form of a so called pattern set, optimized using an evolutionary algorithm) to a new data set, involving data for 200 stocks and covering a time period from February 2016 to the end of that year. Despite being applied to previously unseen data, the pattern set generated a set of trades with an average one-day return of 0.394%. Moreover, the pattern set’s total trading return (excluding transaction costs) over the entire period covered by the new data, when applied as a trading strategy with a simple m–day holding period for each trade, was 15.9% for \(m=1\), 24.9% for \(m=3\), and 61.6% for \(m=6\), compared to 16.2% for the benchmark index (S&P 500) over the same period.

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Notes

  1. 1.

    Note that entry points are ignored for the last (222nd) day of the data set, since the outcome of a trade entered on the very last day would not be computable. Thus, the effective number of days is 221.

References

  1. Ang, A., Goetzmann, W.N., Schaefer, S.M.: Review of the efficient market theory and evidence—implications for active investment management. Found. Trends Financ. 5, 157–242 (2011)

    Article  Google Scholar 

  2. Caginalp, G., Laurent, H.: The predictive power of price patterns. Appl. Math. Financ. 5, 181–205 (1998)

    Article  MATH  Google Scholar 

  3. Fama, E.F.: The efficient market hypothesis: a review of theory and empirical work. J. Financ. 25, 383–417 (1970)

    Article  Google Scholar 

  4. Lu, T.-H., Shiu, Y.-M., Liu, T.-C.: Profitable candlestick trading strategies—the evidence from a new perspective. Rev. Financ. Econ. 21, 63–68 (2012)

    Article  Google Scholar 

  5. Lu, T.-H., Shiu, Y.-M.: Can 1-day candlestick patterns be profitable on the 30 component stocks of the DJIA? Appl. Econ. 48, 3345–3354 (2016)

    Article  Google Scholar 

  6. Park, C.-H., Irwin, S.H.: The profitability of technical analysis: a review. Technical Report AgMAS Project Research Report 2004-04 (2004)

    Google Scholar 

  7. Subrahmanyam, A.: Behavioural finance: a review and synthesis. Eur. Financ. Manag. 14, 12–29 (2008)

    Article  Google Scholar 

  8. Thaler, R.H.: The end of behavioral finance. Financ. Anal. J. 55, 12–17 (1999)

    Article  Google Scholar 

  9. Wahde, M.: A framework for optimization of pattern sets for financial time series prediction. In: Proceeding of the SAI Intelligent Systems Conference (Intellisys), pp. 31–38 (2016)

    Google Scholar 

  10. Wu, M., Huang, P., Ni, Y.: Investing strategies as continuous rising (falling) share prices released. J. Econ. Finan. https://doi.org/10.1007/s12197-016-9377-3 (2016)

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Authors and Affiliations

  1. Chalmers University of Technology, SE-412 96, Göteborg, Sweden

    Mattias Wahde

Authors
  1. Mattias Wahde
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Correspondence to Mattias Wahde .

Editor information

Editors and Affiliations

  1. School of Computing, Ulster University at Jordanstown, Newtownabbey, County Antrim, United Kingdom

    Yaxin Bi

  2. The Science and Information (SAI) Organization, Bradford, United Kingdom

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, United Kingdom

    Rahul Bhatia

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Wahde, M. (2018). Pattern Sets for Financial Prediction: A Follow-Up. In: Bi, Y., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2016. Studies in Computational Intelligence, vol 751. Springer, Cham. https://doi.org/10.1007/978-3-319-69266-1_1

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  • DOI: https://doi.org/10.1007/978-3-319-69266-1_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69265-4

  • Online ISBN: 978-3-319-69266-1

  • eBook Packages: EngineeringEngineering (R0)

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