Author(s)

Kaixuan Xu, Xingji Chen

Corresponding Author

Xingji Chen

ABSTRACT

In this paper, in order to obtain the maximum benefit with the minimum risk, we construct a quantitative trade forecasting and quantitative trade decision-making model by adopting the grey forecasting model, the LSTM neural network model, the information entropy risk measurement model, and the risk-optimized threshold return model. In addition, the robustness and sensitivity of the considered models were investigated. The model we built develops the best daily trading strategy by predicting the price of gold and bitcoin. It can help market traders make better decisions every day and maximize returns within a manageable risk range.

KEYWORDS

Grey forecast, LSTM EVaR, sensitivity binary linear regression models