Emioma, C. C. and Edeki, S.O. (2021) Stock price prediction using machine learning on least-squares linear regression basis. In: International Conference on Recent Trends in Applied Research, 2021, Online.
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Abstract
Predicting the future of a stock price is a difficult task due to the high level of randomness in the movement of prices. This research aims to use a machine-learning algorithm to estimate the closing stock price of a dataset to help aid in the prediction of stock prices leading to higher accuracy in prediction. The intention of the model is for it to be used as a day trading guide. The algorithm being used is called the least-squares linear regression model. It takes in a dependent variable, in this case, would be our closing price of the stock and an independent variable, which is the day each stock price was recorded.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | Stock price, machine learning, prediction |
| Subjects: | Q Science > QA Mathematics |
| Divisions: | Faculty of Engineering, Science and Mathematics > School of Mathematics |
| Depositing User: | Mrs Patricia Nwokealisi |
| Date Deposited: | 02 Jun 2022 10:31 |
| Last Modified: | 02 Jun 2022 10:31 |
| URI: | http://eprints.covenantuniversity.edu.ng/id/eprint/15927 |
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