Articles producció científica> Gestió d'Empreses

Forecasting high-frequency stock returns: a comparison of alternative methods

  • Dades identificatives

    Identificador: imarina:9242970
    Autors:
    Akyildirim, ErdincBariviera, Aurelio F.Duc Khuong NguyenSensoy, Ahmet
    Resum:
    We compare the performance of various advanced forecasting techniques, namely artificial neural networks, k-nearest neighbors, logistic regression, Naive Bayes, random forest classifier, support vector machine, and extreme gradient boosting classifier to predict stock price movements based on past prices. We apply these methods with the high frequency data of 27 blue-chip stocks traded in the Istanbul Stock Exchange. Our findings reveal that among the selected methodologies, random forest and support vector machine are able to capture both future price directions and percentage changes at a satisfactory level. Moreover, consistent ranking of the methodologies across different time frequencies and train/test set partitions prove the robustness of our empirical findings.
  • Altres:

    Autor segons l'article: Akyildirim, Erdinc; Bariviera, Aurelio F.; Duc Khuong Nguyen; Sensoy, Ahmet;
    Departament: Gestió d'Empreses
    Autor/s de la URV: Fernández Bariviera, Aurelio
    Paraules clau: Stock market Prediction Machine learning Index Forecasting Artificial neural-networks Algorithmic trading
    Resum: We compare the performance of various advanced forecasting techniques, namely artificial neural networks, k-nearest neighbors, logistic regression, Naive Bayes, random forest classifier, support vector machine, and extreme gradient boosting classifier to predict stock price movements based on past prices. We apply these methods with the high frequency data of 27 blue-chip stocks traded in the Istanbul Stock Exchange. Our findings reveal that among the selected methodologies, random forest and support vector machine are able to capture both future price directions and percentage changes at a satisfactory level. Moreover, consistent ranking of the methodologies across different time frequencies and train/test set partitions prove the robustness of our empirical findings.
    Àrees temàtiques: Saúde coletiva Operations research & management science Medicina i Matemática / probabilidade e estatística Management science and operations research Interdisciplinar General decision sciences Ensino Engenharias iv Engenharias iii Engenharias i Economia Decision sciences (miscellaneous) Decision sciences (all) Ciencias sociales Ciências agrárias i Ciência da computação Administração, ciências contábeis e turismo Administração pública e de empresas, ciências contábeis e turismo
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    Adreça de correu electrònic de l'autor: aurelio.fernandez@urv.cat
    Identificador de l'autor: 0000-0003-1014-1010
    Data d'alta del registre: 2024-09-07
    Versió de l'article dipositat: info:eu-repo/semantics/acceptedVersion
    Enllaç font original: https://link.springer.com/article/10.1007/s10479-021-04464-8
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referència a l'article segons font original: Annals Of Operations Research. 313 (2): 639-690
    Referència de l'ítem segons les normes APA: Akyildirim, Erdinc; Bariviera, Aurelio F.; Duc Khuong Nguyen; Sensoy, Ahmet; (2022). Forecasting high-frequency stock returns: a comparison of alternative methods. Annals Of Operations Research, 313(2), 639-690. DOI: 10.1007/s10479-021-04464-8
    DOI de l'article: 10.1007/s10479-021-04464-8
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2022
    Tipus de publicació: Journal Publications
  • Paraules clau:

    Decision Sciences (Miscellaneous),Management Science and Operations Research,Operations Research & Management Science
    Stock market
    Prediction
    Machine learning
    Index
    Forecasting
    Artificial neural-networks
    Algorithmic trading
    Saúde coletiva
    Operations research & management science
    Medicina i
    Matemática / probabilidade e estatística
    Management science and operations research
    Interdisciplinar
    General decision sciences
    Ensino
    Engenharias iv
    Engenharias iii
    Engenharias i
    Economia
    Decision sciences (miscellaneous)
    Decision sciences (all)
    Ciencias sociales
    Ciências agrárias i
    Ciência da computação
    Administração, ciências contábeis e turismo
    Administração pública e de empresas, ciências contábeis e turismo
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