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Academic Journal of Business & Management, 2023, 5(8); doi: 10.25236/AJBM.2023.050819.

A Study of BOS Investment Strategy Based on BP Neural Network Prediction


Jialei Liu, Shuhan Yang, Qianjie Wang

Corresponding Author:
Jialei Liu

Business College, Xi’an International Studies University, Xi’an, 710000, China


As a new type of trading method, quantitative trading has been developing rapidly in recent years in developed countries such as the United States. By building BP neural network models and BOS models, we analyze the rise and fall of gold and bitcoin prices, develop the best trading strategies, and guide investors in their investment decisions. The future trading day prices of gold and bitcoin were predicted using the BP neural network model; based on the known historical data, the distribution law of the corresponding rise and fall and the distribution law of the number of consecutive rises and falls were obtained using the Apriori algorithm, respectively, to develop the BOS (buy or sell) system. The resulting return curves were found to have an annualized return of roughly 5.43% for gold and 56.86% for bitcoin, with a relatively smooth fluctuation for gold and a relatively large fluctuation for bitcoin. Then, the impact of the change in transaction cost on the BOS model and total return is investigated by varying the transaction cost several times. The model is discussed in three aspects to provide the best strategy.


BP neural network predicting model; The Apriori algorithm; BOS (buy-or- sell) system

Cite This Paper

Jialei Liu, Shuhan Yang, Qianjie Wang. A Study of BOS Investment Strategy Based on BP Neural Network Prediction. Academic Journal of Business & Management (2023) Vol. 5, Issue 8: 112-118. https://doi.org/10.25236/AJBM.2023.050819.


[1] Aldridge I. High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems [J]. April, 2013, 27(8):2267-2306.

[2] Bao W, Yue J, Rao Y. A deep learning framework for financial time series using stacked autoencoders and long-short term memory [J]. Plos One, 2017, 12(7):1-24.

[3] Baillie R T, Myers R J. Bivariate garch estimation of the optimal commodity futures Hedge [J]. Journal of Applied Econometrics, 2010, 6(2):109-124.

[4] Chen D, Pan H, Dai Y, et al. Investment strategy of colleges based on BP neural network and optimization program[C]// International Conference on Advances in Mechanical Engineering & Industrial Informatics. 2016.

[5] Lin Chunyan. Research on price Prediction of Stock based on Elman Network [J]. Computer applications, 2006, 26(2):477-484.