Dynamic Generation of Investment Recommendations Using Grammatical Evolution

Author
Keywords
Abstract
The attainment of trading rules using Grammatical Evolution traditionally follows a static approach. A single rule is obtained and then used to generate investment recommendations over time. The main disadvantage of this approach is that it does not consider the need to adapt to the structural changes that are often associated with financial time series. We improve the canonical approach introducing an alternative that involves a dynamic selection mechanism that switches between an active rule and a candidate one optimized for the most recent market data available. The proposed solution seeks the flexibility required by structural changes while limiting the transaction costs commonly associated with constant model updates. The performance of the algorithm is compared with four alternatives: the standard static approach; a sliding window-based generation of trading rules that are used for a single time period, and two ensemble-based strategies. The experimental results, based on market data, show that the suggested approach beats the rest.
Year of Publication
2021
Journal
International Journal of Interactive Multimedia and Artificial Intelligence
Volume
6
Issue
Regular Issue
Number
6
Number of Pages
104-111
Date Published
06/2021
ISSN Number
1989-1660
URL
https://www.ijimai.org/journal/sites/default/files/2021-05/ijimai_6_6_11.pdf
DOI
10.9781/ijimai.2021.04.007
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