Research on insider trading identification based on PSO-HKELM
For the insider trading problems in the securities market,this paper collects the stock data of companies punishing for insider trading published by the China Securities Regulatory Commission from 2018 to 2022 as a sample,selects relevant indicators from three aspects:China's securities market performance,financial performance,equity structure and governance system,proposes an algorithm for particle swarm optimization HKELM,and establishes a corresponding insider trading behavior recognition model.The experimental results show that the PSO-HKELM model proposed in this paper has a good effect,with an average accuracy of 79.68%,which is 4.27%,6.32%and 11.22%higher than HKELM,ELM and RF.Results were optimal and stable with a time window of 90 days.It helps the regulatory authorities accurately grasp the insider transactions that occur and further improves the ability to identify insider transactions.
NETL
NSTL
万方数据
