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Comparison of XGBoost and LSTM Models for Stock Price Prediction
- 1 Warren College, University of California San Diego, San Diego, United States
* Author to whom correspondence should be addressed.
Abstract
Along with the development of technology, machine learning would take up a higher role in analyzing categories. Among those categories, predicting stock price meets the needs of most people–or most people who trade stocks. By referring to the predicting model, stock traders can decide whether they should trade in or trade out to make a profit in the stock market. Therefore, it is necessary to testify which model can make the prediction with higher accuracy. To analyze this problem, this article examines the performance of different models under different size of datasets. This paper compared XGBoost and LSTM model by collecting stock price data that are 3 years, 6 years, and 9 years ago from the year 2023. Then analyze the close price of stock prices those models. By comparing the figures and calculated rmse value in each year and each model, the impact of different dataset sizes on each model would be revealed. This paper discovered that XGBoost model has greater accuracy under large-size dataset overall, but LSTM can predict more accurate stock price under small-size dataset.
Keywords
XGBoost, LSTM, CVX Stock Price
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Cite this article
Li,Z. (2023). Comparison of XGBoost and LSTM Models for Stock Price Prediction. Advances in Economics, Management and Political Sciences,61,147-155.
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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