Author(s)

Siyu Wang ¹, Shangkun Zhu ¹, Anran Liu ¹

Affiliation(s)

¹ School of Economics, Beijing Technology and Business University, Beijing, 100048, China

Corresponding Author

Siyu Wang

ABSTRACT

In order to provide investors with some effective strategies to adapt their investments to various objective conditions, increase their returns and reduce their losses as much as possible, we develop mathematical models for the changes in the value of the two assets and derive the optimal investment strategies and evaluation rationale. First, this thesis build time series forecasting models on the value of gold and bitcoin; then use BP neural network prediction model on the values of gold and bitcoin. Then we build dynamic programming based optimal decision optimization algorithm model.

KEYWORDS

finance, trading strategies, mental network forecasting, dynamic programming

CITE THIS PAPER

Siyu Wang, Shangkun Zhu, Anran Liu, Investment model based on BP neural network and dynamic programming. Financial Engineering and Risk Management (2022) Vol. 5: 1-6. DOI: http://dx.doi.org/10.23977/ferm.2022.050401.

REFERENCES

[1] Shen C, Zhang W. Economic analysis on tax model based on BP neural network[C]//2009 International Conference on Communications, Circuits and Systems. IEEE, 2009: 569-572.
[2] Zhou W, Zhao Y, Chen W, et al. Research on investment portfolio model based on neural network and genetic algorithm in big data era[J]. EURASIP Journal on Wireless Communications and Networking, 2020, 2020(1): 1-12.
[3] Cao J, Wang J. Exploration of stock index change prediction model based on the combination of principal component analysis and artificial neural network[J]. Soft Computing, 2020, 24(11): 7851-7860.
[4] Qun Liu, Shuxin Liu, Guoyin Wang, Shuyin Xia. Social relationship prediction across networks using tri-training BP neural networks[J]. Neurocomputing, 2020, 401(prepublish).
[5] Minrong Chen, Bipeng Chen, Guoqiang Zeng, Kangdi Lu, Ping Chu. An adaptive fractional-order BP neural network based on extremal optimization for handwritten digits recognition[J]. Neurocomputing, 2020, 391(prepublish).

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