Analysis of food industry market using network approaches
Purpose ‐ The aim of this paper is to segment the US food industry market through a network representation of the market. Design/methodology/approach ‐ A tangible technique is implemented to study the structural properties of food industry market. A systematic procedure is described to interpret the US food industry sector market data as a graph, which provides the framework under which the market is studied. The maximum cliques and independent sets are found in the market graph, which provides an efficient way for clustering the financial instruments representing food industry. A statistical analysis on the degree distribution of food industry market graph is also performed to study the properties of the market graph. Findings ‐ The maximum cliques provided a classification of stocks with similar behaviour. Market graphs were empirically shown to follow the power-law model. The statistical analysis performed on the food industry market graph corroborated with this observation. Originality/value ‐ This research is an extension of the work by Boginski, Butenko and Pardalos as an application to the food industry. The study helps in efficient segmentation of the food industry market and provides more insights into the market structure.