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Stock Market Price Prediction Using Machine Learning Models
- 1 Beijing University of Posts and Telecommunications
* Author to whom correspondence should be addressed.
Abstract
Stock forecasting has historically been a popular and lucrative field of study. It has been demonstrated that machine learning applications improve accuracy and return in the area of finance forecasting and prediction. This study chose data from the Yahoo Finance database that represented Apple's (AAPL) close price for research. This study categorized articles using a series of machine learning models, encompassing Linear Regression, Random Forest and so on. This paper also examines each article's dataset, variable, model, and findings. The survey in use showcases the findings using the most popular performance metrics. Recent models that combine LSTM with other techniques, For instance, RF has received a lot of study. Deep learning techniques like reinforcement learning and others produced excellent results. In conclusion, the use of deep learning-based techniques for financial modeling has become growing in popularity over the past few years.
Keywords
Apple stock market prediction, machine learning, regression
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Cite this article
Guo,Z. (2023). Stock Market Price Prediction Using Machine Learning Models. Advances in Economics, Management and Political Sciences,45,102-111.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
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