A Deep Residual Shrinkage Neural Network-based Deep Reinforcement Learning Strategy in Financial Portfolio Management

Sun, Ruoyu; Jiang, Zhengyong; Su, Jionglong

doi:10.1109/icbda51983.2021.9403210

Cited by 16 publications

(7 citation statements)

References 22 publications

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“…Thus, numerous variants of DNN may function as independent evaluators to optimize the algorithm. The cryptocurrency market is often used in this type of research to evaluate the effectiveness of the DNN-based strategy compared to traditional portfolio management strategies (Sun et al, 2021 ). Some authors add fuzzy neural networks to the market forecasting when conditions change (Ghahtarani, 2021 ) dramatically.…”

Section: Constructing the Optimal Portfoliomentioning

confidence: 99%

Enhancing portfolio management using artificial intelligence: literature review

Sutiene,

Schwendner,

Sipos

et al. 2024

Front. Artif. Intell.

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Building an investment portfolio is a problem that numerous researchers have addressed for many years. The key goal has always been to balance risk and reward by optimally allocating assets such as stocks, bonds, and cash. In general, the portfolio management process is based on three steps: planning, execution, and feedback, each of which has its objectives and methods to be employed. Starting from Markowitz's mean-variance portfolio theory, different frameworks have been widely accepted, which considerably renewed how asset allocation is being solved. Recent advances in artificial intelligence provide methodological and technological capabilities to solve highly complex problems, and investment portfolio is no exception. For this reason, the paper reviews the current state-of-the-art approaches by answering the core question of how artificial intelligence is transforming portfolio management steps. Moreover, as the use of artificial intelligence in finance is challenged by transparency, fairness and explainability requirements, the case study of post-hoc explanations for asset allocation is demonstrated. Finally, we discuss recent regulatory developments in the European investment business and highlight specific aspects of this business where explainable artificial intelligence could advance transparency of the investment process.

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Section: Constructing the Optimal Portfoliomentioning

confidence: 99%

Enhancing portfolio management using artificial intelligence: literature review

Sutiene,

Schwendner,

Sipos

et al. 2024

Front. Artif. Intell.

View full text Add to dashboard Cite

show abstract

“…With evolution in cryptocurrency and advances in the creation of centralized and decentralized exchanges, accurate information on prices has become accessible and therefore studies are emerging in this line of research using Neural Networks and Deep Learning to analyze market volatility (Bu & Cho, 2018; Miura et al, 2019), forecast future prices (Betancourt & Chen, 2021b; Bu & Cho, 2018; Ji et al, 2019; Lahmiri & Bekiros, 2019, 2021; Lee, 2020; Li et al, 2020; Livieris et al, 2021; Loh & Ismail, 2020; Lucarelli & Borrotti, 2019; Miura et al, 2019; Nithyakani et al, 2021; Sattarov et al, 2020; Sun et al, 2021; Zanc et al, 2019), and managing portfolios with Bitcoin in an automated way (Betancourt & Chen, 2021a; Jiang & Liang, 2016; Ren et al, 2021; Shi et al, 2019; Sun et al, 2021).…”

Section: Systematic Reviewmentioning

confidence: 99%

“…As can be seen in Table 2, most authors prefer to work with deep learning and machine-learning algorithms to forecast the prices of cryptocurrencies and manage smart portfolios. with Bitcoin in an automated way (Betancourt & Chen, 2021a;Jiang & Liang, 2016;Ren et al, 2021;Shi et al, 2019;Sun et al, 2021).…”

Section: Bibliometric Analysismentioning

confidence: 99%

“…One disadvantage of this method is that it is not possible to simulate a portfolio in real time. Sun et al (2021) used the same database (Jiang & Liang, 2016) with the 12 largest cryptocurrencies compared with volume and combined reinforcement learning algorithms with deep learning. A comparison with the main portfolio selection strategies, such as Online Moving Average Reversion (OLMAR) and Weighted Moving Average Mean Reversion (WMAMR), and the strategy used in their study surpassed the others that did not have learning by reinforcement.…”

Section: Portfolio Managementmentioning

confidence: 99%

See 1 more Smart Citation

Cryptocurrencies trading algorithms: A review

Silva

Hadad

Oliveira

2022

Journal of Forecasting

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This study conducts a bibliometric analysis and systematic review of cryptocurrency trading algorithms to identify existing gaps in the area. From our standpoint, this is the first study to carry out a deep analysis of price forecasts and portfolio management in cryptocurrencies in addition to analyzing the most relevant studies and authors, trend topics of the area, and identifying countries with the most published studies. During our research, we identified some gaps that can be used for further research. Currently, there are approximately 16,000 cryptocurrencies; however, in majority of the papers, the authors have only used the top 10 ranking market capitalization cryptocurrencies, leaving aside potential minor cryptocurrencies. Thus, trading strategies using Big Data can be a potential research topic, considering the greater number of emerging cryptocurrencies.

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“…However, in different cycles, investors' actions are also crucial. Therefore, some scholars combine deep learning and reinforcement learning methods, using CNN or DRSN for feature extraction, and then using DQN or DDPG algorithms for decision-making, making the action space of the agent continuous [2][3][4][5][6][7]. Some scholars also use Q-learning and Actor-Critic algorithms for decision-making, and use GAN to generate noise and enhance generalization ability [8][9].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Trading of Stocks Based on TD3 Algorithm

2023

HSET

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Intelligent and efficient stock analysis can help investors and institutions judge stock trends, improve investment returns, and avoid investment risks. The use of deep reinforcement learning methods to process stock data and provide investment recommendations has important research value. This question proposes to use the TD3 algorithm to implement a deep reinforcement learning model, introduce commonly used stock technical indicators, design reward and action functions, and use them to backtest recent stock trading data. Finally, comparing it with the moving average strategy and other deep reinforcement learning models, it was found that in the past decade of historical data, the annualized rate of the moving average strategy was 10% -15%, while the annualized rate of the TD3 algorithm was 23% -25%. This indicates that the TD3 algorithm can help investors or institutions make judgments and effectively improve investment returns.

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A Deep Residual Shrinkage Neural Network-based Deep Reinforcement Learning Strategy in Financial Portfolio Management

Cited by 16 publications

References 22 publications

Enhancing portfolio management using artificial intelligence: literature review

Enhancing portfolio management using artificial intelligence: literature review

Cryptocurrencies trading algorithms: A review

Quantitative Trading of Stocks Based on TD3 Algorithm

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