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Dynamic stock-decision ensemble strategy based on deep reinforcement learning · Study

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ArticleDynamic stock-decision ensemble strategy based on deep reinforcement learning

Xiaoming Yu2022

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Articlebronze2022

Dynamic stock-decision ensemble strategy based on deep reinforcement learning

Xiaoming Yu · Wenjun Wu · Xingchuang Liao · Yong Han

36 citations·0/0 files stored·Applied Intelligence

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Stock Market Forecasting Methods Financial Markets and Investment Strategies Financial Analysis and Corporate Governance

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References

2016Deep Direct Reinforcement Learning for Financial Signal Representation and Trading 2020Adaptive stock trading strategies with deep reinforcement learning methods 2020Multi-DQN: An ensemble of Deep Q-learning agents for stock market forecasting 2018Forward Forecast of Stock Price Using Sliding-Window Metaheuristic-Optimized Machine-Learning Regression 2016An extension of fuzzy TOPSIS for a group decision making with an application to tehran stock exchange 2020Minimax and Biobjective Portfolio Selection Based on Collaborative Neurodynamic Optimization 2020A multi-layer and multi-ensemble stock trader using deep learning and deep reinforcement learning 2017Portfolio selection problems with Markowitz’s mean–variance framework: a review of literature

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Year2022

Typearticle

Published2022-05-09

VenueApplied Intelligence

ISSN-L0924-669X

Languageen

Citations36

Files0 public / 0

ShelfStock Market Forecasting Methods

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doi10.1007/s10489-022-03606-0

openalex_workW4229376254

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