Dynamic futures portfolio strategy: A multi-criteria nested sequential three-state three-way decision model based on herd behavior

Abstract

The futures portfolio is a key tool for addressing market volatility and complexity in the financial markets. Traditional static strategies struggle to keep up with the rapidly shifting market sentiment and herd behavior, leading to delayed decision-making and risk management failures. To enhance investment efficiency and improve risk control, we propose a dynamic multi-criteria nested sequential three-state three-way decision (TS3WD) model based on herd behavior to identify and implement herd behaviors and optimize the futures portfolio strategy. Firstly, this paper proposes a method for determining optimistic and pessimistic conditional probabilities based on loss functions, deriving new TS3WD and simplified decision rules. Secondly, the herd behavior discrimination method is introduced to divide it into positive, neutral, and negative herd behaviors for holding futures contracts. Thirdly, four minimum adjustment optimization models for positive and negative herd behaviors under optimistic and pessimistic attitudes are constructed based on new decision rules, respectively, and a method based on the self-confidence principle for neutral herd behavior is presented, providing a quantitative model for implementing herd behaviors. Subsequently, a progressive dynamic algorithm based on a multi-criteria nested sequential TS3WD model is proposed to deduce the futures portfolio strategy, which dynamically identifies and adjusts loss functions to obtain the optimal futures investment behavior, forming a complete futures portfolio strategy. Finally, we apply the proposed method to solve the metal futures portfolio strategy in the Shanghai Futures Exchange, providing implications for investors in the futures market through sensitivity and comparative analyses.