Financial performance assessment using Grey relational analysis (GRA)

Abstract

Purpose Traditional statistical methods to study the financial performance of any industry have many barriers and limitations in terms of the statistical distribution of the financial ratios, and, in particular, it considers only its positive values of it. The purpose of this paper is to estimate the financial performance of 24 Indian life insurance companies for the period from 2013 to 2016 using Grey relational analysis (GRA) proposed by Deng (1982) that accommodates the negative values in the analysis. Design/methodology/approach Financial performance of 24 Indian life insurance companies for the years from 2013–2014 to 2015–2016 is examined using a total of 14 indicators from capital adequacy ratios, liquidity ratios, operating ratios and profitability ratios (PR). The methodology used is GRA to obtain the Grey grades to rank the performance indicators, where higher relational grade shows better financial performance, and a lower score depicts the scope for improving the performance. Findings The results rank the insurance companies according to their financial performance in which Shriram insurance stands first with higher relational grade score, followed by the companies like IDBI Insurance, Sahara Insurance and Life Insurance Corporation of India. The main finding is that PR which have negative values are playing a crucial role in determining the financial performance of Indian life insurance companies. Practical implications This study has far-reaching practical implications in twofold: first, for the Indian life insurance industry, they have to concentrate more on PR for better financial health and, second, for any financial performance analysis, ignoring negative value ratios produce biased inference and GRA can be used for better inference. Originality/value This study is the first attempt to evaluate the financial performance of Indian life insurance using the GRA methodology. The advantage of GRA is that there is no restrictions on the statistical distribution of the data and it also accommodates the negative values, whereas all the other traditional methods insist on the statistical distribution of data, and, more importantly, they cannot handle negative values in the performance analysis.