Pattern Sets for Financial Prediction: A Follow-Up
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.
- 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
- 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)