Pattern sets for financial prediction: A follow-up
Paper i proceeding, 2018

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.


Mattias Wahde

Chalmers, Mekanik och maritima vetenskaper, Fordonsteknik och autonoma system

Studies in Computational Intelligence

1860-949X (ISSN) 1860-9503 (eISSN)

Vol. 751 1-14
978-3-319-69265-4 (ISBN)

SAI Intelligent Systems Conference, IntelliSys 2016
London, United Kingdom,


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