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: 2023-02-19
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: http://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
Repositori URV