Study for Evaluating the Guiding Significance of Machine Learning-Based Predictions of Stock Price for Short-Term Investing

DOI

10.2991/978-94-6463-823-3_70How to use a DOI?

Keywords

Machine Learning; Model Training; Prediction; Training Strategy

Abstract

The stock market is filled with challenges and opportunities, serving as a focal point of considerable interest for a large audience. Alongside the development of Artificial Intelligence (AI), the use of Machine Learning models to capture potential information patterns in the stock market and subsequently predict its trends has gradually become mainstream. This paper aims to expand the intuitive guidance of using models to predict the stock market for short-term investments. By using NVIDIA everyday close price from 1996–01-01 to 2025–02-13, referencing common indices for assessing prediction accuracy (Coefficient of Determination (R2), Mean Squared Error (MSE)), along with incorporating practical significance, several new feature metrics have been defined, resulting in a relatively reasonable evaluation strategy for determining the strength of guidance of the models in short-term investment forecasts. This provides a potential reference for the selection and adjustment of models when predicting the stock market, as well as for the direction of model training aimed at maximizing short-term investment returns and the tuning of related hyperparameters.

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  - Yicheng Gao
PY  - 2025
DA  - 2025/08/31
TI  - Study for Evaluating the Guiding Significance of Machine Learning-Based Predictions of Stock Price for Short-Term Investing
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 713
EP  - 724
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_70
DO  - 10.2991/978-94-6463-823-3_70
ID  - Gao2025
ER  -