The issues of farmers’ old-age security and land use have long been the focus of Chinese scholars’ and governmental attention. Land transfer plays a vital role in promoting agricultural scale operations, adjusting agricultural structures, and improving land utilization, while the old-age security function of land is one of the important factors affecting land transfer. Based on the data of the China Health and Retirement Longitudinal Study (CHARLS), this study uses the probit and structural equation models to explore social pension and family support mechanisms and pathways with regard to farmers’ land transfer. The results show that: (1) Social pension has a significant negative effect on farmers’ rent-out land, but a significant positive effect on rent-in land. Compared to farmers who do not participate in the New Rural Pension System (NRPS), the probability of rent-out land for farmers who participated in NRPS decreased by 2.44%, and rent-in land increased by 2.26%. (2) Family support has a significant positive effect on farmers’ rent-out land, but a negative effect on rent-in land. (3) Agricultural labor time plays a mediating role in the effect of social pension and family support on both farmers’ rent-out land and rent-in land.

Illegal insider trading identification is of great significance to the healthy development of the securities market. However, with the development of information technology, problems such as multidata sources and noise bring challenges to the insider trading identification work. Moreover, most of the current research on insider trading identification is based on single-task learning, which treats enterprises in different industries as a whole. This may ignore the differences between insider trading identification in different industries. In this article, we collect indicators from multiple sources to help regulators identify insider trading and then use information gain and correlation analysis to screen the indicators. Finally, we propose a multitask deep neural network with insider trading identification in different industries as different subtasks. The proposed model takes into account the correlations and differences between different tasks. Results of experiments show that compared with logistic, support vector machine, deep neural network, random forest, and extreme gradient boosting model, the proposed model can identify insider trading of enterprises in different industries more accurately and efficiently. This article provides new ideas for market regulators to maintain the order of the securities market through intelligent means.