Publication: Dynamic generation of investment recommendations using grammatical evolution
Loading...
Identifiers
Publication date
2021-04-22
Defense date
Advisors
Tutors
Journal Title
Journal ISSN
Volume Title
Publisher
Universidad Internacional de la Rioja (UNIR)
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.
Description
Keywords
dynamic strategy, evolutionary, computation, finance, grammatical evolution, structural change, trading
Bibliographic citation
Martín, C., Quintana, D., & Isasi, P. (2021). Dynamic Generation of Investment Recommendations Using Grammatical Evolution. International Journal Of Interactive Multimedia And Artificial Intelligence, 6(Regular Issue), 104-111. http://doi.org/10.9781/ijimai.2021.04.007