Financial Forecasting with Machine Learning: Price Vs Return

dc.contributor.authorKamalov, Firuz
dc.contributor.authorGurrib, Ikhlaas
dc.contributor.authorRajab, Khairan
dc.date.accessioned2021-04-12T07:58:33Z
dc.date.available2021-04-12T07:58:33Z
dc.date.copyright© 2021
dc.date.issued2021
dc.descriptionThis article is not available at CUD collection. The version of scholarly record of this article is published in Journal of Computer Science (2021), available online at: https://doi.org/10.3844/jcssp.2021.251.264en_US
dc.description.abstractForecasting directional movement of stock price using machine learning tools has attracted a considerable amount of research. Two of the most common input features in a directional forecasting model are stock price and return. The choice between the former and the latter variables is often subjective. In this study, we compare the effectiveness of stock price and return as input features in directional forecasting models. We perform an extensive comparison of the two input features using 10-year historical data of ten large cap US companies. We employ four popular classification algorithms as the basis of the forecasting models used in our study. The results show that stock price is a more effective standalone input feature than return. The effectiveness of stock price and return equalize when we add technical indicators to the input feature set. We conclude that price is generally a more potent input feature than return value in predicting the direction of price movement. Our results should aid researchers and practitioners interested in applying machine learning models to stock price forecasting. © 2021 Firuz Kamalov, Ikhlaas Gurrib and Khairan Rajab. This open access article is distributed under a Creativeen_US
dc.identifier.citationKamalov, F., Gurrib, I. & Rajab, K. (2021). Financial Forecasting with Machine Learning: Price Vs Return. Journal of Computer Science, 17(3), 251-264. https://doi.org/10.3844/jcssp.2021.251.264en_US
dc.identifier.issn15493636
dc.identifier.urihttps://doi.org/10.3844/jcssp.2021.251.264
dc.identifier.urihttp://hdl.handle.net/20.500.12519/379
dc.language.isoenen_US
dc.publisherScience Publicationsen_US
dc.relationAuthors Affiliations : Kamalov, F., Faculty of Engineering, Canadian University Dubai, United Arab Emirates; Gurrib, I., Faculty of Management, Canadian University Dubai, United Arab Emirates; Rajab, K., College of Computer Science and Information System, Najran University, Saudi Arabia
dc.relation.ispartofseriesJournal of Computer Science;Volume 17, Issue 3
dc.rightsCreative Commons Attribution 4.0 International (CC BY 4.0) License
dc.rights.holderCopyright : © 2021 Firuz Kamalov, Ikhlaas Gurrib and Khairan Rajab. This open access article is distributed under a Creative
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCNNen_US
dc.subjectLSTMen_US
dc.subjectNeural Networksen_US
dc.subjectRSIen_US
dc.subjectStock Price Forecastingen_US
dc.titleFinancial Forecasting with Machine Learning: Price Vs Returnen_US
dc.typeArticleen_US

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