scholarly journals Deep Reinforcement Learning (DRL) for Portfolio Allocation

2020 ◽  
Author(s):  
Eric Benhamou ◽  
David Saltiel ◽  
Jean-Jacques Ohana ◽  
Jamal Atif ◽  
Rida Laraki
Keyword(s):  
Reinforcement Learning ◽  
Portfolio Allocation

A Multi-Agent Reinforcement Learning Approach for Stock Portfolio Allocation

2020 ◽  
Author(s):  
Prahlad Koratamaddi ◽  
Karan Wadhwani ◽  
Mridul Gupta ◽  
Dr. Sriram G. Sanjeevi
Keyword(s):  
Reinforcement Learning ◽  
Portfolio Allocation ◽  
Learning Approach ◽  
Multi Agent ◽  
Stock Portfolio

Continuous-Action Reinforcement Learning for Portfolio Allocation of a Life Insurance Company

2021 ◽  
pp. 237-252
Author(s):  
Carlo Abrate ◽  
Alessio Angius ◽  
Gianmarco De Francisci Morales ◽  
Stefano Cozzini ◽  
Francesca Iadanza ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Life Insurance ◽  
Portfolio Allocation ◽  
Insurance Company ◽  
Life Insurance Company ◽  
Continuous Action

scholarly journals Deep Reinforcement Learning for Stock Recommendation

2021 ◽  
Vol 2050 (1) ◽  
pp. 012012
Author(s):  
Yifei Shen ◽  
Tian Liu ◽  
Wenke Liu ◽  
Ruiqing Xu ◽  
Zhuo Li ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Industrial Sector ◽  
Minimum Variance ◽  
Portfolio Allocation ◽  
Selection Strategy ◽  
Stock Selection ◽  
Industrial Sectors ◽  
Learning Agent ◽  
Stock Portfolio ◽  
Relation Graph

Abstract Recommending stocks is very important for investment companies and investors. However, without enough analysts, no stock selection strategy can capture the dynamics of all S&P 500 stocks. Nevertheless, most existing recommending strategies are based on predictive models to buy and hold stocks with high return potential. But these strategies fail to recommend stocks from different industrial sectors to reduce risks. In this article, we propose a novel solution that recommends a stock portfolio with reinforcement learning from the S&P 500 index. Our basic idea is to construct a stock relation graph (RG) which provide rich relations among stocks and industrial sectors, to generate diversified recommendation result. To this end, we design a new method to explore high-quality stocks from the constructed relation graph with reinforcement learning. Specifically, the reinforcement learning agent jumps from each industrial sector to select stock based on the feedback signals from the market. Finally, we apply portfolio allocation methods (i.e., mean-variance and minimum-variance) to test the validity of the recommendation. The empirical results show that the performance of portfolio allocation based on the selected stocks is better than the long-term strategy on the S&P 500 Index in terms of cumulative returns.

Bayes factors for reinforcement-learning models of the Iowa gambling task.

Decision ◽  
2016 ◽  
Vol 3 (2) ◽  
pp. 115-131 ◽  
Author(s):  
Helen Steingroever ◽  
Ruud Wetzels ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Reinforcement Learning ◽  
Iowa Gambling Task ◽  
Bayes Factors ◽  
Gambling Task ◽  
Learning Models ◽  
Reinforcement Learning Models
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