Version 1
: Received: 25 September 2024 / Approved: 25 September 2024 / Online: 26 September 2024 (04:00:48 CEST)
How to cite:
Sang, N.; Cai, W.; Yu, C.; Sui, M.; Gong, H. Enhanced Investment Prediction via Advanced Deep Learning Ensemble. Preprints2024, 2024092029. https://doi.org/10.20944/preprints202409.2029.v1
Sang, N.; Cai, W.; Yu, C.; Sui, M.; Gong, H. Enhanced Investment Prediction via Advanced Deep Learning Ensemble. Preprints 2024, 2024092029. https://doi.org/10.20944/preprints202409.2029.v1
Sang, N.; Cai, W.; Yu, C.; Sui, M.; Gong, H. Enhanced Investment Prediction via Advanced Deep Learning Ensemble. Preprints2024, 2024092029. https://doi.org/10.20944/preprints202409.2029.v1
APA Style
Sang, N., Cai, W., Yu, C., Sui, M., & Gong, H. (2024). Enhanced Investment Prediction via Advanced Deep Learning Ensemble. Preprints. https://doi.org/10.20944/preprints202409.2029.v1
Chicago/Turabian Style
Sang, N., Mujie Sui and Hao Gong. 2024 "Enhanced Investment Prediction via Advanced Deep Learning Ensemble" Preprints. https://doi.org/10.20944/preprints202409.2029.v1
Abstract
Accurate prediction of investment returns is crucial for financial decision-making. Traditional models often struggle with the complexity and variability inherent in financial data. This article presents an advanced predictive model integrating three deep neural networks, pretraining, and ensemble learning to enhance prediction accuracy. By leveraging a combination of DNNs for handling different complexities of data, and a CNN for efficient feature extraction, our approach significantly outperforms traditional methods. Our model incorporates sophisticated data handling techniques, including extensive preprocessing and data augmentation, and benefits from pretraining and ensemble methods to improve robustness and performance. This comprehensive framework offers a robust solution for investment return prediction, addressing the shortcomings of existing models and providing enhanced accuracy and reliability.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
