Comparison with Deep Learning Methods For Predicting Stock Prices
DOI:
https://doi.org/10.24203/gsn42814Keywords:
Stock market prediction, Deep Learning, KNN, Linear Regression, Support Vector MachineAbstract
Recently, machine learning has been an essential tool for analysis in diverse fields, including science, sports management, and economics. In particular, the stock market comprises a complex network of buyers and sellers engaged in stock trading. So, predicting stock prices has been developed using machine-learning techniques to significantly enhance such forecasts' accuracy. Recent advancements have improved the performance of several algorithms, such as Linear Regression, Support Vector Machines (SVM), and K-nearest neighbors (KNN) to predict stock prices. Stock price datasets typically contain information such as opening and closing prices, high and low values, dates, trading volume, and adjusted closing prices provided by Yahoo Finance. Based on the data, this research evaluates the prediction accuracy of each machine-learning method and presents the results through data visualizations, including box plots and tables. The compiled results will assist in identifying the most effective model for stock price prediction.
References
[1] Hai Phan, Seonguk Kim, Numerical Approaches of Pricing European Options in the Cox-Ross-Rubinstein Models, Universal Journal of Applied Mathematics, Vol.10, No.3, pp. 43-48, 2022.
[2] Destiny Rankins, Dewayne A. Dixon, Yeona Kang, Seonguk Kim " Analysis of the Convolutional Neural Network Model in Detecting Brain Tumor," International Journal of Computer and Information Technology, Volume 11 – Issue 4, August 2022.
[3] Borrellas P, Unceta I. The Challenges of Machine Learning and Their Economic Implications, Entropy (Basel), Vol 25, No.3, pp.275, 2021.
[4] Hock Chuan Yeo, Kumar Selvarajoo, Machine learning alternative to systems biology should not solely depend on data, Briefings in Bioinformatics, Vol 23, No.6, pp.1-6, 2022.
[5] Weiwei Jiang, Applications of deep learning in stock market prediction: Recent progress, Expert Systems with Applications, Vol.184, 2021.
[6] B. Panwar, G. Dhuriya, P. Johri, S. Singh Yadav and N. Gaur, Stock Market Prediction Using Linear Regression and SVM, 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), Greater Noida, India, pp. 629-631, 2021.
[7] Li, Y., Pan, Y. A novel ensemble deep learning model for stock prediction based on stock prices and news. Int J Data Sci Anal Vol.13, pp.139–149, 2022.
[8] Hakob GRIGORYAN, A Stock Market Prediction Method Based on Support Vector Machines (SVM) and Independent Component Analysis (ICA). Database Systems Journal vol. VII, no. 1/2016
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Seonguk Kim, Colton Nutter, Nayeong Kong
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The articles published in International Journal of Computer and Information Technology (IJCIT) is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
