Pattern Sets for Financial Prediction: A Follow-Up

Conference paper
Part of the Studies in Computational Intelligence book series (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.

References

  1. 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)CrossRefGoogle Scholar
  2. 2.
    Caginalp, G., Laurent, H.: The predictive power of price patterns. Appl. Math. Financ. 5, 181–205 (1998)CrossRefMATHGoogle Scholar
  3. 3.
    Fama, E.F.: The efficient market hypothesis: a review of theory and empirical work. J. Financ. 25, 383–417 (1970)CrossRefGoogle Scholar
  4. 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)CrossRefGoogle Scholar
  5. 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)CrossRefGoogle Scholar
  6. 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. 7.
    Subrahmanyam, A.: Behavioural finance: a review and synthesis. Eur. Financ. Manag. 14, 12–29 (2008)CrossRefGoogle Scholar
  8. 8.
    Thaler, R.H.: The end of behavioral finance. Financ. Anal. J. 55, 12–17 (1999)CrossRefGoogle Scholar
  9. 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. 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)

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Chalmers University of TechnologyGöteborgSweden

Personalised recommendations