Research on Risk Features and Prediction of China’s Crude Oil Futures Market Based on Machine Learning - Guo, Yaoqi; Zhang, Shuchang; Liu, Yanqiong (Frontiers in Energy Research, 2022)
Facing the rapidly changing domestic and foreign futures markets, how to accurately and immediately predict the price trend of crude oil futures in order to avoid the risks caused by price fluctuations is very important for all participants in the crude oil futures market. Based on the 5-minute high-frequency trading data of China's crude oil futures market in recent three years, this paper uses the EMD-MFDFA model combined with multifractal detrended fluctuation analysis (MF-DFA) and empirical mode decomposition (EMD), unsupervised K-means clustering and Gaussian mixture model (GMM) to identify the risk status of each trading day. Further, Support vector machine (SVM), extreme gradient lifting (XGBoost) and their improved algorithms are used to predict the risk state of China's crude oil futures market. The empirical results are as follows: first, There are obvious multifractal features in the return rate series of China's crude oil futures market and its single trading day; Second, compared with the traditional SVM model, the improved Twin Support Vector Machine (TWSVM) based on solving the sample imbalance issue has better prediction ability for China's crude oil futures risk.; Third, The XGBoost has a great impact on the prediction of China's crude oil risk, and the Focal-XGBoost with focal loss function performs the best in predicting the risk of China's crude oil futures market. market.