Financial Forecasting with Machine Learning: Price Vs Return

dc.contributor.author Kamalov, Firuz
dc.contributor.author Gurrib, Ikhlaas
dc.contributor.author Rajab, Khairan
dc.date.accessioned 2021-04-12T07:58:33Z
dc.date.available 2021-04-12T07:58:33Z
dc.date.copyright © 2021
dc.date.issued 2021
dc.description This 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.2021.251.264 en_US
dc.description.abstract Forecasting 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 Creative en_US
dc.identifier.citation Kamalov, 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.2021.251.264 en_US
dc.identifier.issn 15493636
dc.identifier.uri https://doi.org/10.3844/jcssp.2021.251.264
dc.identifier.uri http://hdl.handle.net/20.500.12519/379
dc.language.iso en en_US
dc.publisher Science Publications en_US
dc.relation Authors 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.ispartofseries Journal of Computer Science;Volume 17, Issue 3
dc.rights Creative Commons Attribution 4.0 International (CC BY 4.0) License
dc.rights.holder Copyright : © 2021 Firuz Kamalov, Ikhlaas Gurrib and Khairan Rajab. This open access article is distributed under a Creative
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.subject CNN en_US
dc.subject LSTM en_US
dc.subject Neural Networks en_US
dc.subject RSI en_US
dc.subject Stock Price Forecasting en_US
dc.title Financial Forecasting with Machine Learning: Price Vs Return en_US
dc.type Article en_US
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