Detecting Potential Market Corner Risk of WTI: A Hybrid Anomaly Detection Approach

The price of crude oil is one of the most critical factors of the world economy, as it is volatile and sensibly affected by the macro-economic, thus attracting large-scale speculative activities. Well known its vulnerability, some of the price fluctuations are affected by external factors, such as financial crisis, oil trade war, while others are due to manipulation. Due to such facts, this article aims to clarify the characteristics and features of the market corner in the crude oil future market and detecting potential market corner risk in West Texas Intermediate (WTI), and a hybrid anomaly detection approach is proposed. After a detailed overview of the characteristics and definitions of the market corner, the features are extracted through the processing of actual market data. We first detect suspicious market corners with abnormal prices and volume through the local outlier factor algorithm (LOF). Next, the detected results are used as pseudo-labels, and the entire month’s trading behavior is trained and classified through the Support Vector Machine (SVM) and Random Forest (RF) algorithms to identifying potential market corners. Experimental results show that the proposed model has excellent accuracy, precision, recall, and F-score, indicating that the model is feasible and has strong robustness. Furthermore, based on the successful detection of potential market corner risk, the model can be further used for individual risk control and overall supervision of the crude oil futures market.