Implementation of X-Gradient Boosting in Banking Stock Price Predictions

DOI

10.2991/978-94-6463-413-6_17How to use a DOI?

Keywords

XGBoost; Machine learning; Stock predictions; Banking

Abstract

The abstract should summarize the contents of the paper in short terms, i.e. 150–250 words. Accurate and computationally efficient prediction of stock price or stock return or any financial problem is required to gives ad- ditional point of view in investment. Machine learning methods have recently become a part of financial model prediction due to their robustness in dealing with large and complex data. In this paper, an extreme gradient boosting (XGBoost)-based machine learning method is introduced for predicting the stock return of five financial banking institution in Indonesia. The data were collected from January 2021 to November 2023. The technical indicator are investigated for each cases. The hyperparameter tuning through the grid search approach was performed to obtain an optimized XGBoost model. The performance of the XGBoost method is compared to the actual data. The validation evaluation results demonstrate that the XGBoost algorithm is highly accurate with the error less than one percent. This demonstrates the machine learning performance in the financial realm as a useful tools to help investors implement better prediction strategies.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Amelia Maharani Fatihah
AU  - Komang Dharmawan
AU  - Putu Veri Swastika
PY  - 2024
DA  - 2024/05/13
TI  - Implementation of X-Gradient Boosting in Banking Stock Price Predictions
BT  - Proceedings of the First International Conference on Applied Mathematics, Statistics, and Computing (ICAMSAC 2023)
PB  - Atlantis Press
SP  - 170
EP  - 181
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-413-6_17
DO  - 10.2991/978-94-6463-413-6_17
ID  - Fatihah2024
ER  -