Development of an AI framework using neural process continuous reinforcement learning to optimize highly volatile financial portfolios-Reference-Cited by-同舟云学术

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Development of an AI framework using neural process continuous reinforcement learning to optimize highly volatile financial portfolios

Published:2024-09 Issue: Volume:300 Page:112017
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Kang Martin^ORCID,Templeton Gary F.,Kwak Dong-Heon^ORCID,Um Sungyong^ORCID

Publisher

Elsevier BV

Reference63 articles.

1. Probabilistic forecasting for energy time series considering uncertainties based on deep learning algorithms;Al-Gabalawy;Electr.Power Syst. Res.,2021

2. A constrained portfolio trading system using particle swarm algorithm and recurrent reinforcement learning;Almahdi;Expert Syst. Appl.,2019

3. Variational autoencoder based anomaly detection using reconstruction probability;An;Spec. Lect. IE,2015

4. The cross-section of volatility and expected returns;Ang;J. Finance,2006

5. The impact of sentiment and attention measures on stock market volatility;Audrino;Int. J. Forecast.,2020

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