Advances in Economics, Management and Political Sciences

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Proceedings of the 2nd International Conference on Financial Technology and Business Analysis

Series Vol. 61 , 28 December 2023


Open Access | Article

Comparison of XGBoost and LSTM Models for Stock Price Prediction

Zhuoran Li * 1
1 Warren College, University of California San Diego, San Diego, United States

* Author to whom correspondence should be addressed.

Advances in Economics, Management and Political Sciences, Vol. 61, 147-155
Published 28 December 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Zhuoran Li. Comparison of XGBoost and LSTM Models for Stock Price Prediction. AEMPS (2023) Vol. 61: 147-155. DOI: 10.54254/2754-1169/61/20231181.

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

References

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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
ISBN (Print)
978-1-83558-223-7
ISBN (Online)
978-1-83558-224-4
Published Date
28 December 2023
Series
Advances in Economics, Management and Political Sciences
ISSN (Print)
2754-1169
ISSN (Online)
2754-1177
DOI
10.54254/2754-1169/61/20231181
Copyright
© 2023 The Author(s)
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated