Dynamic Generation of Investment Recommendations Using Grammatical Evolution Articles uri icon

publication date

  • April 2021

start page

  • 104

end page

  • 111

issue

  • 6

volume

  • 6

International Standard Serial Number (ISSN)

  • 1989-1660

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.

subjects

  • Computer Science

keywords

  • dynamic strategy; evolutionary; computation; finance; grammatical evolution; structural change; trading