Identifying Financial Distress in the Insurance Industry: A Synthesis of Methodological and Empirical Issues

Abstract

12 The likelihood ratio test used in these results may be too conservative for testing hypotheses on the boundary of the parameter space. The problem may arise when comparing the EGB2 model with the probit model. 13 The NPDM slightly outperforms the MDA and most qualitative response models in the classification of insolvent firms, especially three years prior to insolvency, when the minimum number of misclassification criterion is used. References The Importance of Insolvency Prediction This study presents a methodological approach for identifying insolvent insurance companies. In this article, financial distress and insolvency are used interchangeably to describe insurers experiencing liquidation, receivership, conservatorship, restraining orders, rehabilitation, etc. Previous models for predicting financially distressed insurers are summarized and evaluated. More robust models for classifying and predicting financial distress in the insurance industry are presented, and an attempt is made to address methodological issues that previous studies have sometimes ignored. The problem of insolvency in the property-liability insurance industry merits special attention in view of the large number of failures. Since 1961, about 350 property-liability insurers have failed, more than 240 insurers have voluntarily retired, and over 500 companies have merged into other companies, resulting in more than 1,100 property-liability company retirements (compiled from A. M. Best Company, 1961-1990). Insolvency prediction models can help insurance commissioners determine whether an insurer is in danger of failing and can also help auditors decide whether an insurer is a going concern. The ability to classify and identify financial distress is important to regulators, legislators, policyholders, auditors, owners, bondholders, and even the general public. Statistical models of insolvency prediction can be constructed to help determine what accounting, financial, and other information could be employed by regulators in making decisions on the financial solidity of insurers. A number of empirical studies have compared statistical models that use insurers' financial data to predict insolvencies in the property-liability insurance industry (Trieschmann and Pinches, 1973; Pinches and Trieschmann, 1974, 1977; Harmelink, 1974; Cooley, 1975; Eck, 1982; Hershbarger and Miller, 1986; Harrington and Nelson, 1986; BarNiv and Raveh, 1986; BarNiv and Smith, 1987; Ambrose and Seward, 1988; Barniv, 1989; and McDonald, 1992). The models have impressive ability to predict insolvencies in the insurance industry. For example, Trieschmann and Pinches (1973) report that their multiple discriminant analysis (MDA) model correctly classifies 92 percent of insolvent insurers and 96 percent of solvent firms two years prior to the determination of insolvency or solvency; later studies report correct classifications ranging from 62 to 100 percent. Despite the classification success of previous studies, we should be concerned with the accuracy, reliability, and levels of significance for models and coefficients obtained by these studies. This article will review several of the important methodological issues that have been raised about models used to identify financial distress. This study's objectives are (1) to establish a general framework for multivariate prediction models that is applicable to the insurance industry; (2) to enumerate some of the methodological problems associated with insolvency prediction models for the insurance industry (most of which are relevant to binary state prediction models in general); and (3) to use the multivariate models to predict insolvencies with a high degree of accuracy and reliability by overcoming the methodological limitations encountered in previous studies. We present the current state of knowledge and illustrate the methodological considerations through the use of robust novel models and empirical applications. …