Intranet transaction identification method based on random forest and naive Bayes model

Abstract

The invention discloses an intranet transaction identification method based on a random forest and a naive Bayes model. The method comprises the following steps: acquiring an internal screen transaction sample data set under different time window periods, screening and constructing a characteristic index set by adopting a random forest model, constructing a Bayesian identification model of the internal screen transaction according to the screened characteristic index set, and performing internal screen transaction identification by adopting the Bayesian identification model to obtain a resultof whether the internal screen transaction exists or not; and after the event, supervising and verifying whether the internal transaction recognition result is correct, and training and updating the Bayesian recognition model according to the recognition result. According to the invention, the stock intranet transaction identification model is established, so that whether the test target is subjected to intranet transaction or not is accurately identified; a quasi-Newton method and a genetic algorithm are combined, so that parameters of the random forest model are quickly optimized to an optimal solution with high precision, and the solution of the optimal solution has small dependence on an initial value; the method is easy to implement and stable in performance, and robustness and accuracy can be further improved along with increase of sample data.

Description

technical field [0001] The invention belongs to the field of securities market supervision, and in particular relates to an insider trading identification method based on a random forest and a naive Bayesian model. Background technique [0002] Insider trading in the securities market seriously interferes with the normal operation of the stock market, especially harming the interests of small and medium investors. In recent years, how to identify insider trading has become a hot spot in academic circles. [0003] Compared with other classifiers, Naive Bayesian classifier is one of the classifiers with better learning efficiency and classification effect. Its advantages are simple algorithm logic, easy implementation, and stable algorithm performance. The basis of Naive Bayesian classification is probabilistic reasoning. The probability of occurrence of each condition is known, and the reasoning and decision-making tasks are completed. [0004] Genetic algorithm is a search...

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The genetic algorithm has a powerful global search ability, the initial search speed is very fast,

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