典型文献
A new deep reinforcement learning model for dynamic portfolio optimization
文献摘要：
There are many challenging problems for dynamic portfolio optimization using deep reinforcement learning, such as the high dimensions of the environmental and action spaces, as well as the extraction of useful information from a high-dimensional state space and noisy financial time-series data. To solve these problems, we propose a new model struc-ture called the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method with multi-head attention reinforcement learning. This new model integrates data processing methods, a deep learning model, and a reinforcement learning model to improve the perception and decision-making abilities of investors. Empirical analysis shows that our proposed model structure has some advantages in dynamic portfolio optimization. Moreover, we find an-other robust investment strategy in the process of experimental comparison, where each stock in the portfolio is given the same capital and the structure is applied separately.
文献关键词：
中图分类号：
作者姓名：
Weiwei Zhuang;Cai Chen;Guoxin Qiu
作者机构：
International Institute of Finance,School of Management,University of Science and Technology of China,Hefei 230601,China;Department of Statistics and Finance,School of Management,University of Science and Technology of China,Hefei 230026,China;School of Business,Anhui Xinhua University,Hefei 230088,China
文献出处：
引用格式：
[1]Weiwei Zhuang;Cai Chen;Guoxin Qiu-.A new deep reinforcement learning model for dynamic portfolio optimization)[J].中国科学技术大学学报,2022(11):13-26
A类：
B类：
new,deep,reinforcement,learning,model,dynamic,portfolio,optimization,There,are,many,challenging,problems,using,such,high,dimensions,environmental,spaces,well,extraction,useful,information,from,dimensional,state,noisy,financial,series,data,To,solve,these,called,complete,ensemble,empirical,decomposition,adaptive,noise,CEEMDAN,multi,head,attention,This,integrates,processing,methods,improve,perception,decision,making,abilities,investors,Empirical,analysis,shows,that,our,proposed,structure,has,some,advantages,Moreover,find,other,robust,investment,strategy,experimental,comparison,where,each,stock,given,same,capital,applied,separately
AB值：
0.600898
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