Research on Identification of Financial Abnormal Fluctuations in Pledged Repurchase Transactions Based on Machine Learning

Abstract

In order to improve the financial evaluation ability of pledged repo transactions, a method of identifying abnormal financial fluctuations of pledged repo transactions based on machine learning is proposed. Using the method of market risk identification, the pledge risk index system evaluation model for the financial evaluation of pledge type repo transactions is constructed. The balance of the capital flow channel of the pledge type repo financial system is controlled by using machine learning algorithm. Combined with machine learning to extract the abnormal fluctuation characteristics of the pledge type repo financial system, the fuzzy classification learning model of the data structure of the pledge type repo financial system is constructed. Spatial resampling method is used to reconstruct the abnormal financial volatility of pledge repurchase transactions and mining association rules. Clustering and matching the abnormal feature spectrum of the structural data of the financial system of pledge repurchase transactions by using machine learning algorithms. The model adopts the evaluation method of fluctuation synergy parameter. An adaptive learning algorithm is used to identify the abnormal financial fluctuations of pledge repurchase transactions. The simulation results show that this method has good clustering characteristics in identifying the abnormal financial fluctuations of pledge type repo transactions, effectively reducing the capital loss of the financial system structure of pledge type repo transactions, and improving the risk management ability.