STOCK MARKET PRICE PREDICTION USING ARTIFICIAL INTELLIGENCE TECHNIQUES
Main Article Content
Abstract
Predicting stock prices is a challenging task due to the inherent volatility and non-linearity of financial markets. This study explores the application of Support Vector Machine (SVM) with an RBF kernel and XGBoost for forecasting future stock price movements using historical data encoded as ordinal values. The SVM model demonstrated strong performance with an R-squared value of 0.968, low Mean Squared Error (MSE), and Mean Absolute Error (MAE), making it a reliable and stable approach for consistent predictions. Conversely, XGBoost achieved a higher R-squared value of 0.998, indicating superior trend-capturing ability but exhibited higher MSE, suggesting a tendency to overfit. Visualization of model performance revealed that XGBoost excels in capturing short-term price fluctuations, while SVM offers greater consistency with fewer errors. The hybridization of Support Vector Regression (SVR) with SVM is proposed to achieve optimal results, balancing predictability and stability. This study highlights the hybrid model using LSTM to learn patterns to predict the price with SVR with the strong decision making using SVM.
Downloads
Article Details
COPYRIGHT
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.