A Model for Quantifying Investment Decision-making Using Deep Reinforcement Learning (PPO Algorithm)

Research Article
Open access

A Model for Quantifying Investment Decision-making Using Deep Reinforcement Learning (PPO Algorithm)

Xiaochen Xiao 1* , Weifeng Chen 2
  • 1 PUniversity ofElectronic Science andTechnology of China, Zhongshan Guangdong 528402, China    
  • 2 PUniversity ofElectronic Science andTechnology of China, Zhongshan Guangdong 528402, China    
  • *corresponding author 971851309@qq.com
Published on 21 March 2023 | https://doi.org/10.54254/2754-1169/3/2022849
AEMPS Vol.3
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-915371-15-7
ISBN (Online): 978-1-915371-16-4

Abstract

Bitcoin has nowadays shown its importance in finance systems. With great interest to all kinds of investors in the financial sector, it is important to analyse the relationship between Bitcoin and gold. We consider bitcoin and gold as two stocks and calculate the correlation between bitcoin and gold. By introducing the calculation of dynamic penalty coefficient, the problem of dual-stock portfolio investment is transformed into the problem of single-stock purchase investment, which greatly reduces the difficulty of feature engineering and model application. In terms of decision-making model, deep reinforcement learning (PPO algorithm) is used to make quantitative investment decisions. Therefore, we use the expected data in SLTM as the input data of deep reinforcement learning, and combine it with deep reinforcement learning for training. Compared with machine learning to quantify investment decisions, after a period of training, the accuracy rate has improved by 10.038%.

Keywords:

(Recurrent Neural Network) LSTM algorithm, PPO (Proximal policy optimization), Time series analysis, Deep reinforcement learning, Quantitative investment, Gold, Bitcoin

Xiao,X.;Chen,W. (2023). A Model for Quantifying Investment Decision-making Using Deep Reinforcement Learning (PPO Algorithm). Advances in Economics, Management and Political Sciences,3,640-650.
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References

[1]. White H. Economic prediction using neural networks: the case of IBM daily stock returns[C]// IEEE International Conference on Neural Networks. 1988:451-458 vol.2 (1988).

[2]. Zhou Xu. Application of Bolin Belt Trend Breakthrough Strategy in digital currency Market [D]. Zhejiang University of Technology and Industry, (2021).

[3]. Su Chunlin. The application research of multi-factor model in digital currency market (master's degree thesis, University of Electronic Science and Technology). HTTPS://kns.cnki.net/kcms/detail/detail.aspx? dbname=CMFD202001&filename=1019853878.nh (2019)

[4]. Zhang Jing. The application of multiple models to quantify investment. Dynamic analysis of stock market (32),87 (2013).

[5]. Meng Ye, Yu Zhongqing & Zhou Qiang. A safe quantitative stock selection strategy-empirical evidence from A-share market. Financial theory and practice (08),102-107 (2018).

[6]. Huang Zepeng. modeling and application of gold price forecast based on in-depth learning (master's degree thesis, Shanghai jiaotong university). https://kns.cnki.net/kcms/detail/detail.aspx? dbname=CMFD201902&filename=1019880198.nh 2017()

[7]. Ye Wuyi, Sun Liping, Miao Baiqi. Dynamic cointegration study of gold and bitcoin-based on semiparametric MIDAS quantile regression model [J]. Systems Science and Mathematics, 40(07):1270-1285 (2020).

Cite this article

Xiao,X.;Chen,W. (2023). A Model for Quantifying Investment Decision-making Using Deep Reinforcement Learning (PPO Algorithm). Advances in Economics, Management and Political Sciences,3,640-650.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 6th International Conference on Economic Management and Green Development (ICEMGD 2022), Part Ⅰ

ISBN：978-1-915371-15-7(Print) / 978-1-915371-16-4(Online)
Editor：Javier Cifuentes-Faura, Canh Thien Dang
Conference website: https://www.icemgd.org/
Conference date: 6 August 2022
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.3
ISSN：2754-1169(Print) / 2754-1177(Online)

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References

[1]. White H. Economic prediction using neural networks: the case of IBM daily stock returns[C]// IEEE International Conference on Neural Networks. 1988:451-458 vol.2 (1988).

[2]. Zhou Xu. Application of Bolin Belt Trend Breakthrough Strategy in digital currency Market [D]. Zhejiang University of Technology and Industry, (2021).

[3]. Su Chunlin. The application research of multi-factor model in digital currency market (master's degree thesis, University of Electronic Science and Technology). HTTPS://kns.cnki.net/kcms/detail/detail.aspx? dbname=CMFD202001&filename=1019853878.nh (2019)

[4]. Zhang Jing. The application of multiple models to quantify investment. Dynamic analysis of stock market (32),87 (2013).

[5]. Meng Ye, Yu Zhongqing & Zhou Qiang. A safe quantitative stock selection strategy-empirical evidence from A-share market. Financial theory and practice (08),102-107 (2018).

[6]. Huang Zepeng. modeling and application of gold price forecast based on in-depth learning (master's degree thesis, Shanghai jiaotong university). https://kns.cnki.net/kcms/detail/detail.aspx? dbname=CMFD201902&filename=1019880198.nh 2017()

[7]. Ye Wuyi, Sun Liping, Miao Baiqi. Dynamic cointegration study of gold and bitcoin-based on semiparametric MIDAS quantile regression model [J]. Systems Science and Mathematics, 40(07):1270-1285 (2020).

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