An adaptive consensus model for managing non-cooperative behaviors in portfolio optimization for large companies

Abstract

The mean–variance (MV) model provides numerous optimal portfolios for managing a firm's asset portfolio. Portfolio decisions in large corporations involve many interest groups, such as shareholders, bondholders, and employees, and require the assistance of large experts. However, experts from different departments with different cognitive levels and interests can differ or even conflict in their assessments of portfolios. To guarantee their interests, some experts may exhibit non-cooperative behavior, thus reducing the efficiency of reaching a consensus. To tackle this issue, the research aims to develop a large-scale group interactive portfolio optimization method that incorporates non-cooperative behaviors and leverages social network analysis (SN-LSGDM-NC-PO). First, various consensus feedback strategies based on minimum adjustment are formulated to provide advice during the negotiation process according to the global and local levels. Then, considering the acceptance of advice and the effect of expert adjustment on consensus, a new measure of non-cooperative behavior is designed. Non-cooperative behavior by experts can affect trust relations in a social network. Therefore, trust reward and penalty mechanisms, preference penalty mechanisms, and an exit mechanism are developed to manage different types of non-cooperative behavior. Experimental and comparison results demonstrate that the proposed SN-LSGDM-NC-PO algorithm can effectively manage the non-cooperative behaviors and reduce interaction consensus costs.