Abstract

Considering the potential loss of decision-making power associated with share pledging, shareholders need to pledge appropriate share proportion based on their own shareholding situation. To assist shareholders in handling this decision-making challenge, we design a share pledging decision-making method with three-way decision, which fully considers the characteristics of share pledging. Firstly, we construct a novel calculation method of conditional probability based on left-tail systemic risk and share return data, which appropriately measures the probability of share price plummeting. Then, we propose a learning method of loss functions with shareholding proportion information, which considers pledged share proportions. To enhance the flexibility of the proposed method in handling practical share pledging issues, we discuss two scenarios including complete shareholding proportion information and limited shareholding proportion information. Furthermore, we design three-way proportion decision (TWPD) through introducing particle swarm optimization algorithm, which can determine the share proportion of pledge and redemption with minimizing the loss of decision-making power, and develop TWPD to sequential three-way proportion decision (STWPD) for solving multi-period share pledging problem. Finally, we apply the proposed method to deal with share pledging problem of a Chinese firm based on real data collected from China Stock Market & Accounting Research Database, and provide implications for shareholders and firms in risk management during share pledging based on the results of comparison experiments and sensitivity analysis.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.