Financial Fraud Detection Study - Based on Logit Model

The problem of financial fraud has always been a key issue of concern for researchers at home and abroad, and financial fraud of listed companies occurs from time to time at home and abroad, which produces a huge loss of interest for investors as well as the downturn and instability of the capital market, for this reason, it is necessary to carry out a study on the detection of financial fraud. This paper analyzes the financial fraud detection of Chinese listed companies by establishing a Logit model, firstly, obtain the financial statement sample data of Chinese listed companies from CSMAR, and divide the sample into training set and test set with the ratio of 9:1 to preprocess the sample data and fill in the missing values; secondly, this paper selects the financial statement data, and based on the previous research, selects the characteristics that are related to the risk of financial fraud and constitute the risk of financial fraud. high features, and constitute the feature indicators for financial fraud detection; again, since detecting financial fraud is a binary classification problem, the sample is divided into fraudulent companies and normal companies, so this paper studies the financial fraud problem through the discrete choice model in the econometric model, constructs a Logit model, conducts a goodness-of-fit test on the sample data of CSMAR, and estimates the parameters using the method of maximum likelihood estimation ; finally, the test set data is used to test the model's ability to predict financial fraud.