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Technology Industry Stock Price Prediction Based on OLS, Random Forest, and Extreme Gradient Boosting
- 1 University of Wisconsin-Madison
* Author to whom correspondence should be addressed.
Abstract
Contemporarily, the marriage of artificial computer intelligence and the financial stock market has gained increasing interest in recent years. In recent years, forecasting stock prices has also been a more prevalent topic of conversation. Investors lack a coherent knowledge of the model mechanism and prediction results behind stock price forecasts. Hence, this paper will examine Apple, Microsoft, and Amazon, the three largest technology businesses. The three models OLS, Random Forest, and XGBoost were used to predict and evaluate historical data from the past five years. The OLS model has a superior performance structure when dealing with data sets with low data frequency, and its anticipated outcomes are also more accurate, according to the research. In addition, different machine learning models are employed for diverse data sets to produce predictions, hence enhancing the accuracy and dependability of the future predictions. Overall, these results shed light on guiding further exploration of investor investments in stocks and researcher studies theories and models.
Keywords
stock market prediction, machine learning, OLS, random forest, extreme gradient boosting
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Cite this article
Guo,C. (2023). Technology Industry Stock Price Prediction Based on OLS, Random Forest, and Extreme Gradient Boosting. Advances in Economics, Management and Political Sciences,22,1-8.
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 2023 International Conference on Management Research and Economic Development
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