An application of a cardinality-constrained multiple benchmark tracking error model on a plant enterprise selection problem

数学优化 基数(数据建模) 计算机科学 多元化(营销策略) 跟踪误差 波动性(金融) 启发式 运筹学 计量经济学 经济 数学 业务 数据挖掘 营销 人工智能 控制(管理)
作者
Qiuzhuo Ma,Krishna P. Paudel,Liting Gu,Xiaowei Wen
出处
期刊:European Review of Agricultural Economics [Oxford University Press]
卷期号:45 (5): 677-721
标识
DOI:10.1093/erae/jby004
摘要

Yield and return of plants grown in a region are generally closely related. Agricultural scientists are less likely to recommend a single-plant enterprise for a region because of risk and return concerns. From a risk/return perspective, a plant enterprise selection problem can be considered as a portfolio optimisation problem. We use a multiple benchmark tracking error (MBTE) model to select an optimal plant enterprise combination under two goals. A cardinality constraint (CC) is used to efficiently balance multiple objectives and limit over-diversification in a region. We use Chinese national and province level datasets from multiple plant enterprises over 25 years to identify the best plant enterprise combination with two objectives under consideration: return maximisation and risk minimisation. A simulated case using discrete programming is applied in order to analyse a farmer's choice of specific plant enterprise and the transaction cost during rotation. In the continuous problem, the MBTE model is found to be efficient in choosing plant enterprises with high returns and low risk. The inclusion of a CC in the MBTE model efficiently reduces the plant enterprise number and volatility while creating smaller tracking errors than the MBTE model alone in an out-of-sample test. In the discrete problem, a CC can be used to search for the optimal number of plant enterprises to obtain high returns and low risk. The study and methods used can be helpful in choosing an optimal enterprise combination with multiple objectives when there are over-diversification concerns.
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