A framework for optimization of pattern sets for financial time series prediction
Paper i proceeding, 2016

Abstract—In this paper, a framework is introduced for generating human-interpretable structures, here called pattern sets, for short-term prediction of financial time series. The optimization is carried out using an evolutionary algorithm, which is able to modify both the structure and the parameters of the evolving pattern sets. The framework has been applied in two different modes: A tuning mode, in which the user provides a starting point in the form of loosely defined pattern set, and a discovery mode, in which the starting points consist of random pattern sets. The best results were obtained in the tuning mode, for which the top-performing pattern sets gave strongly statistically significant results in excess of one-day market returns (p−values below 0.0007 and, in many cases, even below 0.0001) over validation data (not used during optimization) for two data sets, involving stocks with large and small market capitalization, respectively. The average one-day returns ranged from 0.518 to 1.147%, with one-day Sharpe ratios ranging from 0.138 to 0.258.

Financial prediction

Evolutionary computation

Time series analysis

Författare

Mattias Wahde

Chalmers, Tillämpad mekanik, Fordonsteknik och autonoma system

Proceedings of 2016 SAI Intelligent Systems Conference (IntelliSys)

31-38

Ämneskategorier

Annan data- och informationsvetenskap

Övrig annan teknik

ISBN

978-1-5090-1121-6

Mer information

Skapat

2017-10-08