AI-Driven Stock Return Prediction: Evaluating CNN, LSTM, and RF for Nvidia

Zonghan Wu

2024

Abstract

This paper presents a new approach for estimating Nvidia stock returns using advanced learning algorithms, including Convolutional Neural Network (CNN), Longg Short Term Memory (LSTM), and Random Forest (RF). The system methodology focuses on identifying complex market dynamics by analyzing daily stock returns. Features are preprocessed through normalization to stabilize variance. The CNN architecture involves three 1-D convolutional layers with 64, 128, and 256 filters to scan temporal patterns, followed by two LSTM layers with 50 neurons each to capture long-term dependencies. Random Forest with 100 trees balances computational complexity and predictive performance. Models are trained on 80% of the data, with 20% reserved for testing. Evaluation results indicate that the LSTM model outperforms CNN and Random Forest based on RMSE and MAE metrics. However, the models do not account for external factors like news events and economic indicators, limiting predictability. This study demonstrates the effectiveness of LSTM in predicting stock returns and lays the groundwork for future enhancements in AI-based financial models, with potential applications in algorithmic trading and risk management.

Paper Citation

in Harvard Style

Wu Z. (2024). AI-Driven Stock Return Prediction: Evaluating CNN, LSTM, and RF for Nvidia. In Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML; ISBN 978-989-758-754-2, SciTePress, pages 80-84. DOI: 10.5220/0013487800004619

in Bibtex Style

@conference{daml24,
author={Zonghan Wu},
title={AI-Driven Stock Return Prediction: Evaluating CNN, LSTM, and RF for Nvidia},
booktitle={Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML},
year={2024},
pages={80-84},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013487800004619},
isbn={978-989-758-754-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML
TI - AI-Driven Stock Return Prediction: Evaluating CNN, LSTM, and RF for Nvidia
SN - 978-989-758-754-2
AU - Wu Z.
PY - 2024
SP - 80
EP - 84
DO - 10.5220/0013487800004619
PB - SciTePress