An Empirical Estimation of Default Risk of the UK Real Estate Companies

Based on the Black and Scholes (Black, F., and M. Scholes. (1973). The Pricing of Options and Corporate Liabilities, Journal of Political Economy 81, 637–659) and Merton (Merton, R. C. (1974). On the Pricing of Corporate Debt: The Risk Structure of Interest Rates, Journal of Finance 29, 449–470) (BSM) contingent claims model, and KMV Corporation framework, we estimate the distance to default and the “risk neutral” default probabilities for a sample of 112 real estate companies over the period 1980 to 2001. Our empirical results classifies failed and non-failed companies into Type I error, cases that the BSM-type model fails to predict default when it did occur, and Type II error where BSM-type model predicts default when it did not occur. We find that none of the companies belong to the category of Type I error. Type II error is observed in 12 out of 112 companies. These results support the theoretical underpinnings of the BSM-type structural model in that the two driving forces of default are high leverage and high asset volatility.     

Linear models for minimizing misclassification costs in bankruptcy prediction

This paper illustrates how a misclassification cost matrix can be incorporated into an evolutionary classification system for bankruptcy prediction. Most classification systems for predicting bankruptcy have attempted to minimize misclassifications. The minimizing misclassification approach assumes that Type I and Type II error costs for misclassifications are equal. There is evidence that these costs are not equal and incorporating costs into the classification systems can lead to better and more desirable results. In this paper, we use the principles of evolution to develop and test a genetic algorithm (GA) based approach that incorporates the asymmetric Type I and Type II error costs. Using simulated and real-life bankruptcy data, we compare the results of our proposed approach with three linear approaches: statistical linear discriminant analysis (LDA), a goal programming approach, and a GA-based classification approach that does not incorporate the asymmetric misclassification costs. Our results indicate that the proposed approach, incorporating Type I and Type II error costs, results in lower misclassification costs when compared to LDA and GA approaches that do not incorporate misclassification costs. Copyright © 2001 John Wiley & Sons, Ltd.     

Predicting Corporate Failure Using a Neural Network Approach

This paper investigates the performance of Artificial Neural Networks for the classification and subsequent prediction of business entities into failed and non-failed classes. Two techniques, back-propagation and Optimal Estimation Theory (OET), are used to train the neural networks to predict bankruptcy filings. The data are drawn from Compustat data tapes representing a cross-section of industries. The results obtained with the neural networks are compared with other well-known bankruptcy prediction techniques such as discriminant analysis, probit and logit, as well as against benchmarks provided by directly applying the bankruptcy prediction models developed by Altman (1968) and Ohlson (1980) to our data set. We control the degree of ‘disproportionate sampling’ by creating ‘training’ and ‘testing’ populations with proportions of bankrupt firms ranging from 1% to 50%. For each population, we apply each technique 50 times to determine stable accuracy rates in terms of Type I, Type II and Total Error. We show that the performance of various classification techniques, in terms of their classification errors, depends on the proportions of bankrupt firms in the training and testing data sets, the variables used in the models, and assumptions about the relative costs of Type I and Type II errors. The neural network solutions do not achieve the ‘magical’ results that literature in this field often promises, although there are notable 'pockets' of superior performance by the neural networks, depending on particular combinations of proportions of bankrupt firms in training and testing data sets and assumptions about the relative costs of Type I and Type II errors. However, since we tested only one architecture for the neural network, it will be necessary to investigate potential improvements in neural network performance through systematic changes in neural network architecture.     

A quarterly time-series classifier based on a reduced-dimension generated rules method for identifying financial distress

Predicting financial distress has been and will remain an important and challenging issue. Many methods have been proposed to predict bankruptcies and detect financial crises, including conventional approaches and techniques involving artificial intelligence (AI). Financial distress information influences investor decisions, and investors depend on analysts’ opinions and subjective judgements in assessing such information, which sometimes results in investors making mistakes. In the light of the foregoing, this paper proposes a novel quarterly time series classifier, which reduces the sheer volume of high-dimensional data to be analysed and provides decision-makers with rules that can be used as a reference in assessing the financial situation of a company. This study employs the following six attribute selection methods to reduce the high-dimensional data: (1) the chi-square test, (2) information gain, (3) discriminant analysis, (4) logistic regression (LR) analysis, (5) support vector machine (SVM) and (6) the proposed Join method. After selecting attributes, this study utilises the rough set classifier to generate the rules of financial distress. To verify the proposed method, an empirically collected financial distress data-set is employed as the experimental sample and is compared with the decision tree, multilayer perceptron and SVM under Type I error, Type II error and accuracy criteria. Because financial distress data are quarterly time series data, this study conducts non-time series and time series (moving windows) experiments. The experimental results indicate that the LR and chi-square attribute selection combined with the rough set classifier outperform the listing methods under Type I, Type II error and accuracy criteria.     

Accruals and firm life cycle: Improving regulatory earnings management detection

Regulators have invested considerable energy into developing analytical tools to better detect earnings management. We propose that firms in similar life cycle stages (LCSs) face similar strategic concerns, managerial pressures, growth prospects, etc., and that the commonality in these factors contribute to the “normal” accruals generating process. Consistent with this prediction, we simulate various earnings management conditions and find that accruals models are misspecified in detecting manipulation within particular LCSs; in particular, introduction, shakeout, and decline firms are over-identified as manipulators, while growth and mature firms are under-identified as manipulators when LCS is not used to estimate accruals. Weighted average performance across life cycle stages reveals that LCS estimation of discretionary accruals substantially improves successful detection and reduces Type I errors relative to other grouping alternatives. The combined improvement across both Type I and Type II errors is over 70% for both the modified Jones and discretionary revenue models of accruals-based earnings management.     

An investigation of recent changes in going concern reporting decisions among Big N and non-Big N auditors

Corporate accounting failures and regulatory proceedings that led to the enactment of the Sarbanes–Oxley Act of 2002 increased the scrutiny of auditors. We investigate whether these events resulted in a change in auditor behavior with respect to going concern reporting. Generally speaking, we find that non-Big N auditors became more conservative while Big N auditors became more accurate. Specifically, non-Big N auditors issued more going concern opinions to both failing and non-failing clients post-2001, reducing their Type II misclassifications at the expense of increased Type I misclassifications. However, Big N auditors decreased their Type I misclassifications with no corresponding increase in Type II misclassifications. Thus, our findings suggest that increased auditor scrutiny resulted in performance improvements in the area of going concern reporting primarily for larger auditors. For smaller auditors, improved going concern accuracy for subsequently bankrupt clients came at the cost of more going concern opinions being issued to subsequently non-failing clients.     

U.S. Audit partner identification and auditor reporting

After a lengthy and protracted debate, the Public Company Accounting Oversight Board (PCAOB) adopted new rules requiring disclosure of the engagement partner’s name and information about other accounting firms on the new PCAOB Form AP, Auditor Reporting of Certain Audit Participants. We investigate the impact of this regulation on auditor behavior in the context of the auditor’s going concern report modification propensity. We document an increase in the propensity to issue a going concern report modification in the disclosure regime, accompanied by a corresponding increase in the Type I (‘false positives’) error rate. Thus, an unintended consequence of Rule 3211 is the potential reduction in the audit report's informativeness. Conceivably, a more significant repercussion is that going concern modifications can hasten bankruptcy for firms since financial institutions may be reluctant to lend money to firms with modified audit reports. An unjustified increase in the going concern modification rate as evinced in our paper may make U.S. capital markets potentially less attractive to young, upstart, albeit financially-distressed, companies.     

Risk‐Adjusted Measures of Value Creation in Financial Institutions

Abstract Many financial institutions assess portfolio decisions using RAROC, the ratio of expected return to risk (or ‘economic’) capital. We use asset pricing theory to determine the appropriate hurdle rate, finding that this varies with the skewness of asset returns. We quantify this discrepancy under a range of assumptions showing that the RAROC hurdle rate differs substantially, being higher by a factor of five or more for equity which has a right skew compared to debt which has a pronounced left skew, and also between different qualities of debt exposure. We discuss implications for both financial institution risk management and supervision.     

PREDICTING UNCERTAINTY AUDIT QUALIFICATIONS IN AUSTRALIA USING PUBLICLY AVAILABLE INFORMATION

This research project constructed a logit model to predict “subject to” qualified audit opinions using financial statement and market variables for 1,848 audit reports for Australian companies issued from 1984 to 1988. The model provided a better goodness of fit and was more efficient than two naive strategies for predicting “subject to” audit qualifications. The model explicitly incorporated the relative costs of Type II to Type I errors to account for the auditor's asymmetrical loss function. The model was reasonably accurate when a sensitivity analysis for the relative costs of Type II and Type I errors was considered. The accuracy rates for the estimation sample ranged from 70% to 95%. An inter-temporal holdout sample of 293 audit opinions for Australian firms issued during 1989 indicated that the model was useful for predicting “subject to” audit opinions. The accuracy rates for the holdout sample ranged from 72% to 90% over a range of relative Type II and Type I costs.     

Tracing back to the source: Understanding the corporate governance of boards of directors in Chinese SOEs

Based on the relevant theories of corporate governance and the special institutional background of Chinese state-owned enterprises (SOEs), this paper systematically reviews the literature on the independence and governance effect of SOE boards. We find that the governance effect of SOE boards is driven by the dual characteristics of SOEs: state involvement in ownership and market incentives. With the state involved in ownership, SOEs adhere to the leadership of the Communist Party of China (CPC), which results in an enhanced governance effect. Under market incentives, SOEs tend to have an optimal board structure that helps mitigate both the shareholder–management agency problem (Type I agency problem) and the controlling shareholder–minority shareholder agency problem (Type II agency problem). In terms of the governance effect of boards, directors appointed by non-controlling shareholders are effective in alleviating Type I and Type II agency problems, and this highlights the importance of mixed-ownership reforms in SOEs. Independent directors, especially those with a professional background, also play a role in improving corporate governance. However, independent directors in SOEs have relatively weak incentives to monitor, which limits their governance effect. This paper shows positive implications for promoting mixed-ownership reforms and improving board governance in SOEs.     

Incorrect Inferences When Using Residuals as Dependent Variables

We analyze a procedure common in empirical accounting and finance research where researchers use ordinary least squares to decompose a dependent variable into its predicted and residual components and use the residuals as the dependent variable in a second regression. This two‐step procedure is used to examine determinants of constructs such as discretionary accruals, real activities management, discretionary book‐tax differences, and abnormal investment. We show that the typical implementation of this procedure generates biased coefficients and standard errors that can lead to incorrect inferences, with both Type I and Type II errors. We further show that the magnitude of the bias in coefficients and standard errors is a function of the correlations between model regressors. We illustrate the potential magnitude of the bias in accounting research in four commonly used settings. Our results indicate significant bias in many of these settings. We offer three solutions to avoid the bias.     

Detecting insider trading: The theory and validation in Korea Exchange

This paper provides an empirical basis for identifying insider transactions by deriving a theoretical model, which incorporates the relationship between insider transactions and time series of stock returns. Thus, this model enables us detecting insider transactions by applying stock return time series. We show that when there is an insider transaction in the market, time series can be derived as an ARMA(1,1) process having closed solution coefficients. For validation of the model, we test publicly released insider transactions and reverse takeover events using the minute-by-minute stock price data. The selected events show higher pass rate of the detection criteria than the current detection system which shows that our model produces smaller Type II error than the existing post transaction-based cumulative abnormal return model.     

INSOLVENCY PREDICTION IN THE PRESENCE OF DATA INCONSISTENCIES

In this paper we use data inconsistencies as an indicator of financial distress. Traditional models for insolvency prediction normally ignore inconsistent data, either by removing or replacing it. Instead of removing that information, we propose a new variable to capture it; using it together with traditional accounting variables (based on financial ratios) for the purpose of insolvency prediction. Computational tests use three datasets based on the financial results of 2033 Brazilian Health Maintenance Organizations over 7 years (2001 to 2007). Sixteen classification methods were used to evaluate whether or not the new variable impacted solvency prediction. Tests show a statistically significant improvement in classification accuracy – average results improve 1.3 (p = 0.003) and 1.8 (p = 0.006) percentage points, for 10‐fold and leave‐one‐out cross‐validations respectively. In addition, the analysis of false positives and false negatives shows that the new variable reduces the potentially harmful misclassification of false negatives (i.e. financially distressed companies being classified as financially healthy) and also reduces the estimated overall error rate. Regarding the extensibility of the results, even though this work uses data from Brazilian companies only, the calculation of the financial ratios variables, as well as the inconsistencies, could be extended to most companies worldwide subject to governmental accounting regulations aligned with the International Financial Reporting Standards. Copyright © 2014 John Wiley & Sons, Ltd.     

Business Continuity in the Face of Fraud and Organisational Change

We examine business continuity in the context of fraud and accounting for an organisation as a going concern. The issues addressed are timely and focus on two points. First, fraudulent activities in business are increasing worldwide with related costs reaching trillions of US dollars. Second, the conventional accounting concept of a going concern that typically signifies business continuity is arguably formed on a static view of business. As such, this view does not help mitigate opportunities for fraudulent statements of account. We contribute to the accounting literature by emphasising the dynamic nature of business and in doing so extend the discussion on Type 1 and Type 2 going concern errors. In that context we provide evidence of a possible Type 3 going concern error in an organisation's financial reporting. Drawing on an international fraud case involving an Indian company, Satyam, we illustrate the adaptive behaviour of resilient business organisations. The findings of our study show that even in the face of fraud dynamic, adaptive organisations can achieve business continuity.     

Black economic empowerment,legitimacy and the value added statement: evidence from post‐apartheid South Africa

We examine why companies in South Africa voluntarily provide a value added statement (VAS). The VAS can be used by management to communicate with employees and thereby establish a record of legitimacy. Since we want to establish if the VAS is used to establish symbolic or substantive legitimacy, we examine whether production of a VAS is associated with actual performance in labour‐related areas. To measure labour‐related performance, we use an independent Black Economic Empowerment (BEE) rating. We find that BEE performance is significantly and positively related to the voluntary publication of a VAS. Our results suggest that BEE performance and disclosure of a VAS are two elements of a strategy used by South African companies to establish their substantive legitimacy with labour.     

Implications of Stratified Sampling for Fair Lending Binary Logit Models

We examine the effect of sample design on estimation and inference for disparate treatment in binary logistic models used to assess for fair lending. Our Monte Carlo experiments provide information on how sample design affects efficiency (in terms of mean squared error) of estimation of the disparate treatment parameter and power of a test for statistical insignificance of this parameter. The sample design requires two decision levels: first, the degree of stratification of the loan applicants (Level I Decision) and secondly, given a Level I Decision, how to allocate the sample across strata (Level II Decision). We examine four Level I stratification strategies: no stratification (simple random sampling), exogenously stratifying loan cases by race, endogenously stratifying cases by loan outcome (denied or approved), and stratifying exogenously by race and endogenously by outcome. Then, we consider five Level II methods: proportional, balanced, and three designs based on applied studies. Our results strongly support the use of stratifying by both race and loan outcome coupled with a balanced sample design when interest is in estimation of, or testing for statistical significance of, the disparate treatment parameter.     

Investing at home and abroad: Different costs,different people?

We investigate US households’ direct investment in stocks, bonds and liquid accounts and their foreign counterparts, in order to identify the different participation hurdles affecting asset investment domestically and overseas. To this end, we estimate a trivariate probit model with three further selection equations that allows correlations among unobservables of all possible asset choices. Our results point to the existence of a second hurdle that stock owners need to overcome in order to invest in foreign stocks. On the other hand, we find little evidence for additional pecuniary or informational costs associated with investment in foreign bonds and liquid accounts.     

Business Cycle and Management Earnings Forecasts

This study examines the effects of the economic cycle on the properties of management earnings forecasts. Although a large volume of accounting literature examines the determinants of managerial earnings forecasts, the properties of such forecasts, and the response of market participants to earnings forecasts (Cameron 1986; King et al., 1990; Hirst et al., 2008), research on management earnings forecasting incentivized by macro‐economic factors has received scant empirical investigation. We use the National Bureau of Economic Research economic cycle definition to operationalize economic recession, and consider some commonly used management earnings forecast characteristics, including forecast likelihood, forecast frequency, forecast error, forecast pessimism, and forecast precision. We find that the likelihood of providing management earnings forecasts and frequency of forecasts increases during economic recession. We also find that economic recession is positively associated with forecast error, but negatively associated with forecast precision. Our findings suggest macro‐economic factors as an important determinant of management earnings forecasts properties.     

Determinants of capital budgeting methods and hurdle rates in Nordic firms

We study the determinants for the choice of capital budgeting methods and the setting of hurdle rates (WACCs) in five Nordic countries. Combining survey data with a rich set of determinants, including ownership data, CFO characteristics, and financial data, we find that the use of the Net Present Value method and the sophistication of the capital budgeting are related to firm characteristics, variables proxying for real option features in investments and CFO characteristics (age and education). We also find support for significantly higher hurdle rates than motivated by economic theory. The premium is weakly positively related to managerial short‐term pressure and strongly negatively related to the sophistication level of the firm’s capital budgeting.