Academic Journal of Computing & Information Science, 2022, 5(4); doi: 10.25236/AJCIS.2022.050414.
Man Jeoi Lau1, 2, Shiyi Bai1, Kevin Chan1, Fang Lin1, 3
1Pittsburgh Institute, Sichuan University, Chengdu, Sichuan, 610065, China
2School of Economics, Sichuan University, Chengdu, Sichuan, 610065, China
3College of Physics, Sichuan University, Chengdu, Sichuan, 610065, China
Quantitative trading is an emerging financial investment strategy. In this paper, based on the historical data prediction-guided trading strategy problem, small-sample prediction and machine learning are used to build a prediction trading model. To solve the problem of insufficient training samples in the early stage, we build a gray prediction model for small sample forecasting, and train the neural algorithm and use it for later predictions. In the gray prediction model, the optimal prediction parameters are obtained by tuning the parameters for the differential equation. The whale algorithm was introduced to optimize the BP neural network to obtain a threshold closer to the optimal solution, which accelerated the degree of threshold optimal approximation by reducing the mean absolute percentage error (MAPE) by 26%.
Grey prediction; BP neural network; Quantitative trading
Man Jeoi Lau, Shiyi Bai, Kevin Chan, Fang Lin. Research on Financial Product Price Prediction Based on Grey Prediction and WOA-BP Neural Network. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 4: 75-78. https://doi.org/10.25236/AJCIS.2022.050414.
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