An application of a cardinality-constrained multiple benchmark tracking error model on a plant enterprise selection problem. (31st March 2018)

• Record Type: Journal Article
• Authors: Ma, Qiuzhuo; Paudel, Krishna P; Gu, Liting; Wen, Xiaowei
• Is Part Of: European review of agricultural economics. Volume 45:Number 5(2018)
• Page Start: 677
• Page End: 721
• Publication Date: 2018-03-31
• Subjects: cardinality constraint -- multiple benchmarks tracking error -- plant enterprise selection; Agriculture -- Economic aspects -- Periodicals; Agriculture -- Economic aspects -- Europe -- Periodicals; 338.109405
• DOI: 10.1093/erae/jby004
• Languages: English
• ISSNs: 0165-1587

Abstract

Abstract: 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.