世界経済・社会統計 = World development indicators

Earnings Management and Investor Protection: An International Comparison

The corporate information environment develops endogenously as a consequence of information asymmetries and agency problems between investors, entrepreneurs, and managers. We provide a framework for analyzing the three main decisions that shape the corporate information environment in a capital markets setting: (1) managers’ voluntary reporting and disclosure decisions, (2) reporting and disclosures mandated by regulators, and (3) reporting decisions by third-party intermediaries (analysts). We review current research on disclosure regulation, information intermediaries, and the determinants and economic consequences of corporate disclosure and financial reporting decisions. We conclude that in the last ten years, research has generated a number of useful insights. Despite this progress, we call for researchers to consider interdependencies between the various decisions that shape the corporate information environment and highlight changes in the economic financial environment that raise new and interesting issues for researchers to address.

The Financial Reporting Environment: Review of the Recent Literature

The financial reporting environment: Review of the recent literature

Bankruptcy prediction in banks and firms via statistical and intelligent techniques – A review

Motivated by the European Union (EU) decision to mandate application of the International Financial Reporting Standards (IFRS) to the consolidated financial statements of all EU listed firms (Regulation (EC) 1606/2002), starting in December 2005, we compare the value relevance of accounting information in 14 European countries in the year prior to and the year of the mandatory adoption of the IFRS. We focus on three accounting information items for which measurements under IFRS are likely to differ considerably from measurements under domestic accounting practices across the EU countries prior to the introduction of the international standards: goodwill, research and development expenses (R&D), and asset revaluation. These three items, selected on an a priori basis, have been shown in previous research to differ in the effect of uncertainty on their future benefits. We use valuation models that include these three variables and in addition the book value of equity and earnings. Overall, our study suggests...

The Impact of Mandatory IFRS Adoption on Equity Valuation of Accounting Numbers for Security Investors in the EU

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. …

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

Although life insurance insolvency has not been a serious problem in the past, the incidence of such insolvencies is increasing. The scope of this paper is to review the financial operations of life insurance companies in order to detect variables which will be helpful in identifying potential insolvencies. Three multivariate analyses are used in this paper: Multidiscriminant Analysis (MDA), nonparametric analysis and a logit analysis. The NAIC-IRIS Tests, the decomposition measures, and other financial ratios were found to be accurate measures for classifying failures in a multivariate framework one and two years prior to insolvency. The analyses correctly classified between 82 and 91 percent of the life insurance companies one and two years prior to insolvency. Cross-sectional validation on 31 publicly traded life insurers indicated that these large insurers are reasonably safe. All these life insurers were correctly classified as solvent companies. However, further analyses of these models and a prospective probability model indicates that more than one type analysis may be required for measuring the probability of failure.

Classifying Financial Distress in the Life Insurance Industry

From 1994 to 1998, Bradshaw (2004) finds that analysts' stock recommendations relate negatively to residual income valuation estimates (scaled by current price) but positively to valuation heuristics based on the price‐to‐earnings‐to‐growth ratio and long‐term growth. These results are surprising, especially considering that future returns relate positively to residual income valuation estimates and negatively to heuristics. Using a large sample of analysts for the 1993–2005 period, we consider whether recent regulatory reforms affect this apparent inconsistent analyst behavior. Consistent with the intent of these reforms, we find that the negative relation between analysts' stock recommendations and residual income valuations is diminishing following regulations. We also show that residual income valuations, developed using analysts' earnings forecasts, relate more positively with future returns. However, we document that stock recommendations continue to relate negatively with future returns. ...

Do Analysts Practice What They Preach and Should Investors Listen? Effects of Recent Regulations

We test the ability of analyst characteristics to explain relative forecast accuracy across legal origins (common law versus civil law). Common law countries generally have more effective corporate governance mechanisms, including stronger investor protection laws and inputs provided through higher-quality financial reporting systems. In this type of environment, investors are more willing to compete for superior investment decisions because they expect to be equitably rewarded, and investors are more likely to demand information about accounting earnings because earnings have more value relevance. The increased demand by investors for earnings information increases the economic incentives of analysts to provide more accurate earnings forecasts. We predict that analysts with superior ability and resources in common law countries will more consistently outperform their peers because appropriate market-based incentives exist. In civil law countries, where the demand for earnings information is reduced because of weaker corporate governance mechanisms and lower-quality financial reporting, we predict that analysts with superior ability will less consistently provide superior forecasts. Results are consistent with our expectations and suggest an association between legal and financial reporting environments and analysts' forecast behavior.

Ran Barniv

Papers

The Impact of Mandatory IFRS Adoption on Equity Valuation of Accounting Numbers for Security Investors in the EU

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

Classifying Financial Distress in the Life Insurance Industry

Do Analysts Practice What They Preach and Should Investors Listen? Effects of Recent Regulations

The Association Between the Legal and Financial Reporting Environments and Forecast Performance of Individual Analysts