Mathematical processing of trading strategy based on long short-term memory neural network model

Han-Yang Wang; An-Qi Li; Chao-Chen Tie; Chao-Jun Wang; Yun-Hua Xu

doi:10.3389/fncom.2022.1052140

Mathematical processing of trading strategy based on long short-term memory neural network model

Front Comput Neurosci. 2022 Nov 28:16:1052140. doi: 10.3389/fncom.2022.1052140. eCollection 2022.

Authors

Han-Yang Wang¹, An-Qi Li², Chao-Chen Tie², Chao-Jun Wang², Yun-Hua Xu³

Affiliations

¹ School of Economics, Anhui University of Finance and Economics, Bengbu, China.
² School of Finance, Anhui University of Finance and Economics, Bengbu, China.
³ School of Business Administration, Anhui University of Finance and Economics, Bengbu, China.

Abstract

At present, gold and bitcoin have become mainstream assets in market transactions. Due to the volatility of gold and bitcoin prices, we can buy and sell assets like gold and bitcoin the same way we buy and sell stocks. The research goal of this article is to develop an optimal trading strategy that maximizes our post-trade returns. By studying the relationship between the two, on the one hand, it supplements and enriches the theoretical research on the rate of return of gold and Bitcoin, on the other hand, it provides a certain reference for investors to construct investment strategies. The research on the cointegration relationship between them has important practical significance. At the same time, it has important practical significance for the research on the cointegration relationship between bitcoin and gold.

Keywords: Bitcoin; gold; long short-term memory network model; mathematical processing; trading strategy.