Autor según el artículo: Saenz, JV; Quiroga, FM; Bariviera, AF
Departamento: Gestió d'Empreses
Autor/es de la URV: Fernández Bariviera, Aurelio
Palabras clave: Trading Stock price forecast Investment algorithms Financial reports Deep learning Clustering
Resumen: This paper explores the use of clustering models of stocks to improve both (a) the prediction of stock prices and (b) the returns of trading algorithms.
We cluster stocks using k-means and several alternative distance metrics, using as features quarterly financial ratios, prices and daily returns. Then, for each cluster, we train ARIMA and LSTM forecasting models to predict the daily price of each stock in the cluster. Finally, we employ the clustering-empowered forecasting models to analyze the returns of different trading algorithms.
We obtain three key results: (i) LSTM models outperform ARIMA and benchmark models, obtaining positive investment returns in several scenarios; (ii) forecasting is improved by using the additional information provided by the clustering methods, therefore selecting relevant data is an important preprocessing task in the forecasting process; (iii) using information from the whole sample of stocks deteriorates the forecasting ability of LSTM models.
These results have been validated using data of 240 companies of the Russell 3000 index spanning 2017 to 2022, training and testing with different subperiods.
Áreas temáticas: Matemática / probabilidade e estatística Interdisciplinar Finance Engenharias iv Engenharias iii Economics and econometrics Economia Direito Ciencias sociales Business, finance Administração, ciências contábeis e turismo Administração pública e de empresas, ciências contábeis e turismo
Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
Direcció de correo del autor: aurelio.fernandez@urv.cat
Identificador del autor: 0000-0003-1014-1010
Fecha de alta del registro: 2023-12-17
Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/
Referencia al articulo segun fuente origial: International Review Of Financial Analysis. 88 102657-
Referencia de l'ítem segons les normes APA: Saenz, JV; Quiroga, FM; Bariviera, AF (2023). Data vs. information: Using clustering techniques to enhance stock returns forecasting. International Review Of Financial Analysis, 88(), 102657-. DOI: 10.1016/j.irfa.2023.102657
DOI del artículo: 10.1016/j.irfa.2023.102657
Entidad: Universitat Rovira i Virgili
Año de publicación de la revista: 2023
Tipo de publicación: Journal Publications
Repositori URV