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Open Access Journal Article

Machine Learning Models for Predicting Stock Price Movements

by James Brown 1,*
1
James Brown
*
Author to whom correspondence should be addressed.
TASC  2022, 35; 4(2), 35; https://doi.org/10.69610/j.tasc.20221219
Received: 21 October 2022 / Accepted: 16 November 2022 / Published Online: 19 December 2022
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Abstract

This paper explores the application of machine learning models in predicting stock price movements within the financial markets. The study aims to investigate the effectiveness of various machine learning algorithms in forecasting stock prices, with a focus on accuracy, robustness, and adaptability. By analyzing historical stock price data, the research employs several machine learning techniques, including linear regression, decision trees, support vector machines, and neural networks. The results demonstrate that machine learning models, particularly neural networks, exhibit significant potential in predicting stock price movements. However, the research also highlights the challenges faced by these models, such as overfitting, data volatility, and the need for continuous model updating. The findings provide valuable insights into the utilization of machine learning in the financial sector, offering practitioners a more informed approach to investing and risk management.

Keywords: Machine learning models, - Stock price movements, - Predictive finance, - Financial markets, - Neural networks.;
Copyright: © 2022 by Brown. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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ACS Style
Brown, J. Machine Learning Models for Predicting Stock Price Movements. Transactions on Applied Soft Computing, 2022, 4, 35. https://doi.org/10.69610/j.tasc.20221219
AMA Style
Brown J. Machine Learning Models for Predicting Stock Price Movements. Transactions on Applied Soft Computing; 2022, 4(2):35. https://doi.org/10.69610/j.tasc.20221219
Chicago/Turabian Style
Brown, James 2022. "Machine Learning Models for Predicting Stock Price Movements" Transactions on Applied Soft Computing 4, no.2:35. https://doi.org/10.69610/j.tasc.20221219
APA style
Brown, J. (2022). Machine Learning Models for Predicting Stock Price Movements. Transactions on Applied Soft Computing, 4(2), 35. https://doi.org/10.69610/j.tasc.20221219

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References

  1. Burbules, N. C., & Callister, T. A. (2000). Watch IT: The Risks and Promises of Information Technologies for Education. Westview Press.
  2. Lintner, J. (1965). The Security Market Line: Theoretical and Empirical Results. Journal of Business, 38(2), 265-296.
  3. Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and Regression Trees. CRC Press.
  4. Mangasarian, O. L. (1999). Least Squares Support Vector Machines. IEEE Transactions on Neural Networks, 10(5), 1055-1064.
  5. Vapnik, V. (1995). The Nature of Statistical Learning Theory. Springer-Verlag.
  6. Haykin, S. (1994). Neural Networks: A Comprehensive Foundation. Mc-Graw-Hill.
  7. Haykin, S., Mohebbi, M., & Haddad, P. (1997). The application of neural networks to financial time series forecasting. In Proceedings of the 1997 IEEE International Conference on Neural Networks (ICNN '97) (Vol. 2, pp. 1047-1051). IEEE.
  8. Wang, J., & Wang, M. (2008). A hybrid support vector machine based on genetic algorithm for stock price forecasting. Expert Systems with Applications, 35(3), 1245-1252.
  9. Zhang, G. P., Liu, B., & Patuwo, B. E. (2009). A hybrid support vector machine based on particle swarm optimization for financial time series forecasting. Neurocomputing, 72(16), 3239-3247.
  10. Zhang, G. P., Patuwo, B. E., & Hu, M. Y. (2004). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 20(4), 357-390.