Bankruptcy Prediction in the WorldCom Age

Similar documents
TW3421x - An Introduction to Credit Risk Management Default Probabilities Internal ratings and recovery rates. Dr. Pasquale Cirillo.

A STUDY OF APPLICATION OF ALTMAN Z SCORE MODEL FOR OMAN CEMENT COMPANY (SAOG), SOHAR SULTANATE OF OMAN

ASIAN JOURNAL OF MANAGEMENT RESEARCH Online Open Access publishing platform for Management Research

REHABCO and recovery signal : a retrospective analysis

Tendencies and Characteristics of Financial Distress: An Introductory Comparative Study among Three Industries in Albania

Part I: Distress Prediction Models and Some Applications

Lesson 9 Predicting Financial Distress

COMPREHENSIVE ANALYSIS OF BANKRUPTCY PREDICTION ON STOCK EXCHANGE OF THAILAND SET 100

Default Risk and Accounting Measures

DO BANKRUPTCY MODELS REALLY HAVE PREDICTIVE ABILITY? EVIDENCE USING CHINA PUBLICLY LISTED COMPANIES.

Web Extension 25A Multiple Discriminant Analysis

The CreditRiskMonitor FRISK Score

A COMPARATIVE ANALYSIS OF CREDIT RISK IN INVESTMENT BANKS : A CASE STUDY OF JP MORGAN, MERRILL LYNCH AND BANK OF AMERICA

International Journal of Multidisciplinary and Current Research

A PREDICTION MODEL FOR THE ROMANIAN FIRMS IN THE CURRENT FINANCIAL CRISIS

A Study on MeASuring the FinAnciAl health of Bhel (ranipet) using Z Score Model

Small and Medium Size Companies Financial Durability Altman Model Aplication

Distressed Firm and Bankruptcy Prediction in an International Context: A Review and Empirical Analysis of Altman s Z-Score Model

Using Altman's Z-Score Model to Predict the Financial Hardship of Firms Listed In the Trading Services Sector of Bursa Malaysia

Estimating Default Probabilities of Corporate Bonds over Various Investment Horizons

A Proposed Model for Industrial Sickness

University of Cape Town

CORPORATE DISTRESS PREDICTION MODELS IN A TURBULENT ECONOMIC AND BASEL II ENVIRONMENT. Edward I. Altman* Comments to:

Financial Distress Models: How Pertinent Are Sampling Bias Criticisms?

Economia Aziendale Online 2000 Web (2010) 1:

Predicting Financial Distress: Multi Scenarios Modeling Using Neural Network

ANALYSIS OF FINANCIAL DISTRESS ON INFRASTRUCTURE COMPANIES LISTED AT INDONESIA STOCK EXCHANGE USING S-SCORE MODEL

Z-score Model on Financial Crisis Early-Warning of Listed Real Estate Companies in China: a Financial Engineering Perspective Wang Yi *

Can Altman Z-score Model Predict Business failures in Pakistan? Evidence from Textile companies of Pakistan

Application and Comparison of Altman and Ohlson Models to Predict Bankruptcy of Companies

Corporate Failure & Reconstruction

Measuring Financial Distress of Public Sector Enterprises Using Z-Score Model

Z SCORES: AN EFFECTIVE WAY OF ANALYSING BANKS RISKS

EFFICACY OF ALTMAN S Z-SCORE TO PREDICT FINANCIAL UNASSAILABILITY: A MULTIPLE DISCRIMINANT ANALYSIS (MDA) OF SELECT AUTOMOBILE COMPANIES IN INDIA

Evaluating the Financial Health of Jordan International Investment Company Limited Using Altman s Z Score Model

ANALYSIS OF ROMANIAN SMALL AND MEDIUM ENTERPRISES BANKRUPTCY RISK

A Study on Financial Health of Arasu Rubber Corporation, Kanyakumari District of Tamilnadu: A Z Score Approach

Assessing the Probability of Failure by Using Altman s Model and Exploring its Relationship with Company Size: An Evidence from Indian Steel Sector

Survival Analysis Employed in Predicting Corporate Failure: A Forecasting Model Proposal

The Altman Z is 50 and Still Young: Bankruptcy Prediction and Stock Market Reaction due to Sudden Exogenous Shock (Revised Title)

The Determinants of Capital Structure: Evidence from Turkish Panel Data

PREDICTING CORPORATE FAILURE

FINANCIAL INSTABILITY PREDICTION IN MANUFACTURING AND SERVICE INDUSTRY

Junk bonds: Money makers or money takers?

Australian Journal of Basic and Applied Sciences

BANKRUPTCY PREDICTION USING ALTMAN Z-SCORE MODEL: A CASE OF PUBLIC LISTED MANUFACTURING COMPANIES IN MALAYSIA

Bankruptcy prediction in the construction industry: financial ratio analysis.

PREDICTION OF COMPANY BANKRUPTCY USING STATISTICAL TECHNIQUES CASE OF CROATIA

Journal of Central Banking Theory and Practice, 2016, 3, pp Received: 16 March 2016; accepted: 16 June 2016

Amath 546/Econ 589 Introduction to Credit Risk Models

An Analysis of the Robustness of Bankruptcy Prediction Models Industrial Concerns in the Czech Republic in the Years

Developing a Bankruptcy Prediction Model for Sustainable Operation of General Contractor in Korea

Assessing the Probability to File for Troubled Debt Restructuring through Accounting Ratios Analysis: the Italian case

A STUDY ON FINANCIAL HEALTH OF DAIRY INDUSTRY IN ANDHRA PRADESH BASED ON Z SCORE ANALYSIS

ANALYSIS OF BANKRUPTCY PREDICTION MODELS AND THEIR EFFECTIVENESS: AN INDIAN PERSPECTIVE

~j (\J FINANCIAL RATIO ANALYSIS GEORGIA INSTITUTE OF TECHNOLOGY SCHOOL OF CIVIL ENGINEERING ATLANTA, GEORGIA 30332

Financial Distress Signaling & Corporate Social Responsibility

Measuring Firms Financial Health -A Study on Select Indian Automobile Companies

AN APPRAISAL OF FINANCIAL SOLVENCY OF ONGC A Z SCORE MODEL

Assessing Bankruptcy Probability with Alternative Structural Models and an Enhanced Empirical Model

Do Z-Score and Current Ratio have Ability to Predict Bankruptcy?

Creation Bankruptcy Prediction Model with Using Ohlson and Shirata Models

International Journal of Research and Review E-ISSN: ; P-ISSN:

A STUDY ON PREDICTION OF DEFAULT PROBABILITY OF AUTOMOBILE DEALERSHIP COMPANIES USING ALTMAN Z SCORE MODEL

A Study To Measures The Financial Health Of Selected Firms With Special Reference To Indian Logistic Industry: AN APPLICATION OF ALTMAN S Z SCORE

Revaluation and Altman`s Z-score the Case of the Serbian Capital Market

Financial Performance of Small and Medium Construction Firms (SMCFs) in Abuja, Nigeria

Online Open Access publishing platform for Management Research. Copyright 2010 All rights reserved Integrated Publishing association

Analysis of Financial Strength of select firms from Indian Textiles Industry using Altman s Z Score Analysis

Z score Estimation for Indian Companies With Reference To CNX Nifty Index of National Stock Exchange

COMPARISON OF THE MODELS OF FINANCIAL DISTRESS PREDICTION

Financial Risk Diagnosis of Listed Real Estate Companies in China Based on Revised Z-score Model Xin-Ning LIANG

Possibilities for the Application of the Altman Model within the Czech Republic

FINANCIAL SOUNDNESS OF SELECTED INDIAN AUTOMOBILE COMPANIES USING ALTMAN Z SCORE MODEL

A Comparison of Jordanian Bankruptcy Models: Multilayer Perceptron Neural Network and Discriminant Analysis

Research Chronicler: International Multidisciplinary Peer-Reviewed Journal ISSN: Print: ISSN: Online: X

CONTROVERSIES REGARDING THE UTILIZATION OF ALTMAN MODEL IN ROMANIA

COMPARING FINANCIAL DISTRESS PREDICTION MODELS BEFORE AND DURING RECESSION

The Role of Leverage to Profitability at a Time of Economic Crisis

Predicting the Financial Distress of Non-Banking Companies Listed on the Palestine Exchange (PEX)

FINANCIAL HEALTH OF SELECTED COMPANIES IN TELECOM SECTOR: A COMPARATIVE STUDY

Application of Altman Z Score Model on Selected Indian Companies to Predict Bankruptcy

FINAL EXAMINATION GROUP - IV (SYLLABUS 2012)

Modelling the Term Structure of Hong Kong Inter-Bank Offered Rates (HIBOR)

Testing and calibrating the Altman Z-score for the U.K.

The Z-Score Model for Predicting Periods of Financial Instability. Z-Score Estimation for the Banks Listed on Bucharest Stock Exchange

UNIVERSIDAD CARLOS III DE MADRID FINANCIAL ECONOMICS

Is the Structural Approach More Accurate than the Statistical Approach in Bankruptcy Prediction?

Merton models or credit scoring: modelling default of a small business

ON THE RISK RETURN CHARACTERISTICS OF THOSE FIRMS EXPERIENCING THE HIGHEST FREE CASH FLOW YIELDS

ANALYSIS OF THE FINANCIAL STATEMENTS

Black Scholes Equation Luc Ashwin and Calum Keeley

Equity fund raising and creative accounting practices: Indications from Athens Stock Exchange for the period

Loss Given Default: Estimating by analyzing the distribution of credit assets and Validation

THE PROPOSITION VALUE OF CORPORATE RATINGS - A RELIABILITY TESTING OF CORPORATE RATINGS BY APPLYING ROC AND CAP TECHNIQUES

The Role of Cash Flow in Financial Early Warning of Agricultural Enterprises Based on Logistic Model

Overview. We will discuss types and characteristics of loans made by U.S. FIs, models for measuring credit risk. Important for purposes of:

FINANCIAL MANAGEMENT AGAINST CRISIS IN ENTERPRISES: EVIDENCE FROM UZBEKISTAN

An Analysis of a Dynamic Application of Black-Scholes in Option Trading

VALIDITY OF ALTMAN S Z-SCORE MODEL IN PREDICTING FINANCIAL DISTRESS OF LISTED COMPANIES AT THE NAIROBI SECURITIES EXCHANGE

Transcription:

Bankruptcy Prediction in the WorldCom Age Nikolai Chuvakhin* L. Wayne Gertmenian * Corresponding author; e-mail: nc@ncbase.com Abstract For decades, considerable accounting and finance research was directed at creating a model for predicting bankruptcy. Early attempts to discover a single best accountingbased predictor eventually gave way to more comprehensive models involving both accounting- and market-based indicators. This paper reviews one of the most popular of those bankruptcy prediction models, the Z-score model developed by Edward Altman in 1968. Also included is a brief discussion of later developments in credit analysis.

Introduction According to Pate (2002), 257 public companies, with total assets of $256 billion, filed for bankruptcy in 2001. That this is the highest number of bankruptcy filings since 1980 is alarming. Furthermore, it is uncomfortably large compared to the number of filings during the last recession (125 filings in 1991 and 91 filings in 1992). Pate further estimates the likely number of public company bankruptcy filings in 2002 will be about 200, 22 percent below the 2001 level, but still well above the 1986-2000 average of 113. Another clearly visible trend is the increase in the number of large companies going bankrupt. Altman (2000) points out that bankruptcy in firms with large asset size, while quite rare prior to 1966, became more common in 1970s. According to Altman, since the enactment of the current U.S. Bankruptcy Code in 1978, there were at least 100 Chapter 11 bankruptcies of firms whose asset size exceeded $1 billion. In this environment, business leaders and finance professionals would be well advised to refresh their knowledge of bankruptcy prediction models. Fortunately, those models have been around for a while. The present paper is intended to serve as an introduction into one of the most popular bankruptcy prediction models, the Z-score model developed by Edward Altman in his pioneering 1968 paper. The Z-score Model For decades, considerable accounting and finance research was directed at finding a ratio that would serve well as a predictor of bankruptcy. One of the most comprehensive studies of that early era was Beaver (1967). Beaver studied the 2

performance of various ratios as bankruptcy predictors and concluded that the cash flow to debt ratio was the single best predictor. The critical breakthrough in bankruptcy prediction came with Altman (1968). Altman decided to abandon the search for a single best ratio and built a comprehensive, statistical model using a technique called multiple discriminant analysis (MDA). MDA allows a researcher to group observations into several pre-determined categories based on several characteristics of an observation. Altman selected a sample of 33 manufacturing companies that filed for bankruptcy in 1946-65 and matched them with another 33 companies selected on a stratified (by both industry and asset size) random basis. Altman then started with 22 ratios that seemed to be intuitively plausible as bankruptcy predictors. After every run, he excluded the ratio that contributed least to the explanatory power of the model. Eventually, he came up with a model that contained only five ratios. The original Altman model took the following form: Z = 0.012 X 1 + 0.014 X 2 + 0.033 X 3 + 0.006 X 4 + 0.999 X 5 (1) where X 1 = working capital/total assets, X 2 = retained earnings/total assets, X 3 = earnings before interest and taxes/total assets, X 4 = market value of equity/book value of total liabilities, and X 5 = sales/total assets In the initial 1968 study, Altman used a cutoff Z-score of 2.675. In other words, if the Z-score was below the cutoff line, the firm was classified as bankrupt (i.e., 3

insolvent or headed that way), if above, as non-bankrupt. This allowed him to correctly classify 94% of the bankrupt firms and 97% of the non-bankrupt firms one year prior to the filing of bankruptcy. An attempt to predict bankruptcy two years in advance yielded lower, but still impressive, accuracies of 72% and 94%. It is important to emphasize that the original Altman model is intended for use in cases of publicly traded manufacturing firms. However, Altman has used the same approach to develop other models; Z' for privately-held firms and Z'' for nonmanufacturing firms. In the original version, all ratios were stated in percentage points, except X 5, stated as an absolute value. For example, if EBIT/total assets ratio were 15%, or 0.15, X 3 would be assumed to equal 15. Eventually, a more convenient specification was proposed: Z = 1.2 X 1 + 1.4 X 2 + 3.3 X 3 + 0.6 X 4 + 1.0 X 5 (2) In this specification, an EBIT/total assets ratio of 15% would result in X 3 =0.15. X 5, similarly to the original version, is stated as an absolute value. Altman himself used this version in Altman and LaFleur (1981). After conducting three subsequent tests (86 companies gone bankrupt in 1969-75, 110 in 1976-95, and 120 in 1997-99), Altman recommended a lower cutoff score of 1.81. Interestingly enough, Altman (2000) noted that in 1999, 20% of U.S. industrial firms referenced in Compustat data tapes had Z-scores below 1.81. In other words, the unusually high incidence of bankruptcy in 2001-02 was to be expected! 4

But What If the Books Are Cooked? An interesting feature of the Z-score model is its ability to withstand certain types of accounting irregularities. Consider a recent high-profile bankruptcy of WorldCom, where management improperly recorded billions of dollars as capital expenditures instead of operating expenses. Such a treatment would have a twofold impact on financial statements: (1) overstating earnings, and (2) overstating assets. Overstated earnings would increase the X 3 ratio in the Z-score model, while overstated assets would actually decrease three ratios, X 1, X 2, and X 5 (all three are calculated with total assets in the denominator). The overall impact of these accounting improprieties on the company s Z- score, therefore, is likely to be downward. To examine the validity of this reasoning in a limited case-study setting, we computed Z-scores for WorldCom for fiscal years ending December 31, 1999, 2000, and 2001 based on its annual 10-K reports filed with the U.S. Securities and Exchanges Commission (see Table 1). We found that the company indeed experienced a rapid deterioration in its Z-score. Obviously, this limited test has to be taken with a grain of salt (especially given that WorldCom is not a manufacturing company), but it does show how this particular type of accounting impropriety can affect the Z-score. Later Developments As noted above, one innovative aspect of Altman (1968) was its radical departure from the search for a single best ratio. Rather, he sought a simple, yet comprehensive, multivariate model. Another equally innovative and equally radical idea was to use a combination of accounting and market-based indicators to forecast bankruptcy. At the 5

time, finance scholars often questioned the validity of accounting measures, while accounting researchers thought that observing the equity market had little to do with debtrelated issues such as bankruptcy. The significance of this synthesis was not fully understood until the advent of option pricing models. First, in a seemingly unrelated development, Black and Scholes (1973) and Merton (1973) discovered a mechanism for rational option pricing, which, incidentally, depended on both the price of underlying shares and the volatility of that price. Then, Vasicek and Kealhofer (see Crosbie (2002) for an extensive discussion of the Vasicek-Kealhofer model) proposed to view equity as a call option on the firm s assets with a strike price equal to the book value of the firm s liabilities. This approach permits an estimate of the probability of default within a specified period of time based on both accounting (the value of liabilities) and market (the share price and volatility) data. These and other related developments have lead to the emergence of a new school in credit analysis and fixed-income portfolio management. The underlying mathematics of the Vasicek-Kealhofer model and other modern credit risk models is sometimes quite complex, but the general idea first proposed by Altman a comprehensive synthesis of accounting and market-based measures still remains the cornerstone of contemporary credit analysis. References Altman, E. (1968) Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy, Journal of Finance 23, September. 6

(2000) Predicting Financial Distress of Companies: Revisiting the Z-score and ZETA Models (working paper; http://pages.stern.nyu.edu/~ealtman/zscores.pdf) Altman, E., and J. LaFleur (1981) Managing a Return to Financial Health, Journal of Business Strategy, Summer. Beaver, W. (1967) Financial Ratios as Predictors of Failures, in Empirical Research in Accounting, selected studies, 1966, in supplement to the Journal of Accounting Research, January. Black, F., and M. Scholes (1973) The Pricing of Options and Corporate Liabilities, Journal of Political Economy, Vol. 81. Crosbie, Peter (2002) Modeling Default Risk, KMV, January (available online at http://www.kmv.com/insight/). Merton, R.C. (1973) Theory of Rational Option Pricing, Bell Journal of Economics and Management Science, Vol. 4. Pate, Carter (2002) Business Chapter 11 Bankruptcies: Recent Levels and Implications for 2002, PricewaterhouseCoopers, March. 7

Table 1. Z-score Analysis for WorldCom (accounting data prior to restatements) Ratio Definition 1999 2000 2001 X 1 Working capital/total assets (0.08) (0.08) (0.00) X 2 Retained earnings/total assets (0.01) 0.03 0.04 X 3 Earnings before interest and taxes/total assets 0.08 0.08 0.02 X 4 Market value of equity/book value of total liabilities 3.58 1.13 0.54 X 5 Sales/total assets 0.39 0.40 0.34 Z Z-score 2.697 1.274 0.798 8