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    • UNIR REVISTAS
    • Revista IJIMAI
    • 2021
    • vol. 6, nº 6, june 2021
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    • UNIR REVISTAS
    • Revista IJIMAI
    • 2021
    • vol. 6, nº 6, june 2021
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    Dynamic Generation of Investment Recommendations Using Grammatical Evolution

    Autor: 
    Martín, Carlos
    ;
    Quintana, David
    ;
    Isasi, Pedro
    Fecha: 
    06/2021
    Palabra clave: 
    dynamic strategy; evolutionary computation; finance; grammatical evolution; structural change; trading; IJIMAI
    Revista / editorial: 
    International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI)
    Tipo de Ítem: 
    article
    URI: 
    https://reunir.unir.net/handle/123456789/12981
    DOI: 
    https://doi.org/10.9781/ijimai.2021.04.007
    Dirección web: 
    https://www.ijimai.org/journal/bibcite/reference/2937
    Open Access
    Resumen:
    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.
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