Financial Forecasting with Machine Learning: Price Vs Return

Kamalov, Firuz
Gurrib, Ikhlaas
Rajab, Khairan
Journal Title
Journal ISSN
Volume Title
Science Publications
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
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:
CNN, LSTM, Neural Networks, RSI, Stock Price Forecasting
Kamalov, F., Gurrib, I. & Rajab, K. (2021). Financial Forecasting with Machine Learning: Price Vs Return. Journal of Computer Science, 17(3), 251-264.