Stock Trend Prediction with tuned Machine Learning Models

DOI

10.2991/978-94-6463-823-3_71How to use a DOI?

Keywords

Stock prediction; Classification; Cross validation

Abstract

The stock market exhibits inherent volatility and hence a well-performed prediction model would be beneficial for investors to understand the market and develop a feasible trading strategy. The aim of this paper is to predict the short-term future trend of the closing price of a certain stock. By applying feature engineering, extra financial indicators will be added to the data, then 6 different machine learning models are used, and the performances are compared via metrics such as accuracy, AUC score and precision. In addition, two ways of improving the model performance are proposed, one is using methods from ensemble learning to combine results from existing models, the other is using cross validation to tune the parameters of the models. Both methods successfully increase the prediction accuracy by about 2%. The experimental result suggests that a well-tuned XGBoost Model with suitable features could reach a relatively promising result with an accuracy of 51.8%.

Copyright

© 2025 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.

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TY  - CONF
AU  - Chenye Yao
PY  - 2025
DA  - 2025/08/31
TI  - Stock Trend Prediction with tuned Machine Learning Models
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 725
EP  - 733
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_71
DO  - 10.2991/978-94-6463-823-3_71
ID  - Yao2025
ER  -