Enhancing NVIDIA Stock Price Prediction Using Search Engine Trend Data and Long Short-Term Memory Models

DOI

10.2991/978-94-6463-823-3_67How to use a DOI?

Keywords

Stock Price Prediction; Google Trends; Long Short-Term Memory

Abstract

In recent years, machine learning has become a widely adopted approach in financial data analysis. Numerous financial institutions and investors are looking for higher returns from this, so stock price forecasting has become one of the focal issues. This study explores the enhancement of NVIDIA stock price prediction by integrating search engine trend data with Long Short-Term Memory (LSTM) models. Utilizing Google Trends data as an additional feature, the research aims to capture public interest patterns and their influence on stock price behavior. The study empirically demonstrates that incorporating Google Trends data as a feature enhances the predictive power of LSTM models over traditional methods with historical stock data alone. The results indicate that search engine trend data can serve as a valuable leading indicator, refining short-term stock price forecasts. This study improves prediction by introducing new features to help investors make more informed decisions, ultimately aiding investors in making more informed decisions.

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  - Qiuyu Wang
PY  - 2025
DA  - 2025/08/31
TI  - Enhancing NVIDIA Stock Price Prediction Using Search Engine Trend Data and Long Short-Term Memory Models
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 684
EP  - 694
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_67
DO  - 10.2991/978-94-6463-823-3_67
ID  - Wang2025
ER  -