Predicting bankruptcy using the discrete-time semiparametric hazard model

The usual bankruptcy prediction models are based on single-period data from firms. These models ignore the fact that the characteristics of firms change through time, and thus they may suffer from a loss of predictive power. In recent years, a discrete-time parametric hazard model has been proposed for bankruptcy prediction using panel data from firms. This model has been demonstrated by many examples to be more powerful than the traditional models. In this paper, we propose an extension of this approach allowing for a more flexible choice of hazard function. The new method does not require the assumption of a parametric model for the hazard function. In addition, it also provides a tool for checking the adequacy of the parametric model, if necessary. We use real panel datasets to illustrate the proposed method. The empirical results confirm that the new model compares favorably with the well-known discrete-time parametric hazard model.     

Portfolio creation using artificial neural networks and classification probabilities: a Canadian study

Financial Markets and Portfolio Management - This study aims to verify whether using artificial neural networks (ANNs) to establish classification probabilities generates portfolios with higher...     

Feedforward neural networks in the classification of financial information

Financial research has given rise to numerous studies in which, on the basis of the information provided by financial statements, companies are classified into different groups. An example is that of the classification of companies into those that are solvent and those that are insolvent. Linear discriminant analysis (LDA) and logistic regression have been the most commonly used statistical models in this type of work. One feedforward neural network, known as the multilayer perceptron (MLP), performs the same task as LDA and logistic regression which, a priori, makes it appropriate for the treatment of financial information. In this paper, a practical case based on data from Spanish companies, shows, in an empirical form, the strengths and weaknesses of feedforward neural networks. The desirability of carrying out an exploratory data analysis of the financial ratios in order to study their statistical properties, with the aim of achieving an appropriate model selection, is made clear.     

Modelling non-linearity in economic classification with neural networks

In this paper results are presented of a study on economic classification with neural networks. Comparison is made between neural networks and linear modelling techniques and, in particular, comments are made on the problem of overfitting and the estimation of prediction errors in cases where the available data sets are relatively small. It is shown that selecting network parameters by k-fold cross-validation and weight decay training are effective remedies for these phenomena. The conclusions are illustrated in two cases: predicting the volume of the mortgage market in the Netherlands and the classification of bond ratings. © 1997 John Wiley & Sons, Ltd.     

Combining conditional volatility forecasts using neural networks: an application to the EMS exchange rates

The present paper examines the out-of-sample forecasting performance of four conditional volatility models applied to the European Monetary System (EMS) exchange rates. In order to provide improved volatility forecasts, the four models’ forecasts are combined through simple averaging, an ordinary least squares model, and an artificial neural network. The results support the EGARCH specification especially after the foreign exchange crisis of August 1993. The superiority of the EGARCH model is consistent with the nature of the EMS as a managed float regime. The ANN model performed better during the August 1993 crisis especially in terms of root mean absolute prediction error.     

Corporate bankruptcy prediction: a high dimensional analysis

Much bankruptcy research has relied on parametric models, such as multiple discriminant analysis and logit, which can only handle a finite number of predictors (Altman in The Journal of Finance 23 (4), 589–609, 1968; Ohlson in Journal of Accounting Research 18 (1), 109–131, 1980). The gradient boosting model is a statistical learning method that overcomes this limitation. The model accommodates very large numbers of predictors which can be rank ordered, from best to worst, based on their overall predictive power (Friedman in The Annals of Statistics 29 (5), 1189–1232, 2001; Hastie et al. 2009). Using a sample of 1115 US bankruptcy filings and 91 predictor variables, the study finds that non-traditional variables, such as ownership structure/concentration and CEO compensation are among the strongest predictors overall. The next best predictors are unscaled market and accounting variables that proxy for size effects. This is followed by market-price measures and financial ratios. The weakest predictors overall included macro-economic variables, analyst recommendations/forecasts and industry variables.     

客户体验呼唤优化升级

1个数据和100万个数据的力量孰大孰小?这里可以做一个分析:假如我们彻底湮没在100万个数据的海洋里,无法辨别数据的真伪和价值,其意义何从谈起?相反,如果那个唯一的数据能帮助我们快速搜索到目标对象,或者充分支持我们的判断,那么它的地位则不言自明.     

Erratum: Enterprise bankruptcy law of the People's Republic of China

The article above (DOI:10.1002/iir.155) was published in IIR 17:1 on pages 33–55. In the list of contents of that issue it was attributed in error to L. Qi. The correct attribution should have been to the translator of the document, Professor Li Shuguang. We apologise to Professor Li and his collaborators for this oversight.     

Market behaviour around bankruptcy announcements: evidence from the Australian Stock Exchange

The corporate distress literature to date has largely focused on the predictive power of accounting variables ( Altman, 2001 ). Following previous literature, this study examines the relevance of abnormal stock returns in discriminating between failed and non‐failed firms (e.g. Clark and Weinstein, 1983; Shumway, 2001). Our results confirm the findings of previous literature that investors in failed firms typically incur substantial negative stock returns leading up to failure announcements. However, in contrast to prior research we do not find evidence of an announcement effect (i.e. negative stock returns on the event day itself or the day preceding). We also document evidence that the bid‐ask spreads of failed firms widen substantially up to 7 months prior to failure, indicating the likelihood of significant information asymmetries across investors in failed firms.     

Regulating the timing of disclosure: Insights from the acceleration of 10-K filing deadlines

We study the effects of regulating the timing of disclosure on the quality of accounting information, using a 2003 US regulatory change that accelerates 10-K filing deadlines as a research setting. Employing a difference-in-differences design, we find that the likelihood of issuing financial statements that are later restated increases for firms that are required to file more quickly, relative to firms whose filing practices are not affected by the regulatory change. This effect is particularly pronounced during the audit busy season, when auditors also face significant time pressure. These results are consistent with a tradeoff between how quickly accounting reports are required to be filed and the reliability of the resulting reports.     

Can value-based stock selection criteria yield superior risk-adjusted returns: an application of neural networks

This study examines whether superior investment returns can be earned by using neural network modeling procedures to perform forecasts based on a set of financial ratios reflecting traditional value based investment strategies. The study covers a 20-year period. We find that the value ratio provides useful information that permits the selection of portfolios that provide investment returns superior to the DJIA and the S&P 500, and a group of randomly selected securities. The risk-adjusted returns for the portfolios selected by the neural network are greater than those achieved using other forecasting methods.     

Firms’ performance under different bankruptcy systems: a Europe–USA empirical analysis

This study empirically analyses the effect that the bankruptcy law has on firms’ performance based on its financial situation. To do this, we considered the different types of efficiency and their influence on firms’ value. The study was carried out for Germany, Spain, the United States, France and the United Kingdom. We applied System‐GMM estimation to dynamic panel data. The main results show that under creditor‐oriented systems, there is a decrease in the value of both financially distressed firms and those filing for bankruptcy.     

Predicting tail-related risk measures: The consequences of using GARCH filters for non-GARCH data

We investigate the consequences for Value-at-Risk and expected shortfall purposes of using a GARCH filter on various mis-specified processes. In general, we find that the McNeil and Frey (McNeil, A.J. and R. Frey, 2000, Estimation of Tail-Related Risk Measures for Heteroscedastic Financial Time Series: An Extreme Value Approach, Journal of Empirical Finance 7, 271–300.) two step procedure has very good forecasting properties. Using an unconditional non-filtered tail estimate also appears to perform satisfactorily for expected shortfall measurements but less so for VaR computations. Methods assuming specific densities such as the Gaussian or Student-t may yield wrong predictions. Thus, the use of an adequacy test for filtered data to given densities appears relevant. The paper builds on recent techniques to obtain thresholds for extreme value computations. Statistical tests for the expected shortfall, based on the circular bootstrap, are developed.     

Optimization of a firm's capital structure: A quantitative approach based on a probabilistic prognosis of risk and time of bankruptcy

This article develops a new probabilistic approach to the problem of optimization of a firm's capital structure. The main idea of the approach is straightforward. As a possible firm's bankruptcy is the principal factor restricting the amount of borrowed capital, we assess the probabilities of bankruptcy at various time horizons in the future dependent on the proportion of debt capital and other indices of a firm's current financial position and then calculate how these probabilities influence the firm's value.We identify a set of factors determining conditions of existence and the value of the optimal debt/equity ratio. These include the characteristics of a firm's debt (proportion of short-term component of the debt, cost of service, and maturity horizons of long-term component), characteristics of a firm's ability to pay the debt, and some macroeconomic factors.We represent dependencies of optimal debt/equity ratio and gains in a firm's value on the main influencing factors.The approach is based on real data of real firms and does not use superfluously formalized models. We believe it can be used in practical capital structure decisions although specific calculations must be fulfilled for each firm that needs such decision.     

Building legal indexes to explain recovery rates: An analysis of the French and English bankruptcy codes

The paper analyzes the characteristics of bankruptcy procedures that may impact on creditors’ recoveries. We propose 132 legal indexes accounting for the main functions of bankruptcy codes: namely, the accessibility of the procedures, their ability to disclose information, the protection of debtor’s assets, the coordination of the claimants and their decision power, and the sanction of management. The French procedures are more protective of the debtor’s assets and prioritize the coordination of claims. In England, liquidation procedures protect more secured claims, while unsecured creditors have more decision power under reorganization procedures. Our indexes are then used to explain recovery rates on a set of 833 bankrupt SMEs. Several bankruptcy rules are associated with higher recoveries: namely, accessibility of the procedure, protection of the debtor’s assets, protection of claims, and sanction of faulty management. On the contrary, information disclosure has negative impact on recoveries, probably due to the breach in confidentiality.     

Informal networks: the company behind the chart

A glance at an organizational chart can show who's the boss and who reports to whom. But this formal chart won't reveal which people confer on technical matters or discuss office politics over lunch. Much of the real work in any company gets done through this informal organization with its complex networks of relationships that cross functions and divisions. According to consultants David Krackhardt and Jeffrey Hanson, managers can harness the true power in their companies by diagramming three types of networks: the advice network, which reveals the people to whom others turn to get work done; the trust network, which uncovers who shares delicate information; and the communication network, which shows who talks about work-related matters. Using employee questionnaires, managers can generate network maps that will get to the root of many organizational problems. When a task force in a computer company, for example, was not achieving its goals, the CEO turned to network maps to find out why. He discovered that the task force leader was central in the advice network but marginal in the trust network. Task force members did not believe he would look out for their interests, so the CEO used the trust map to find someone to share responsibility for the group. And when a bank manager saw in the network map that there was little communication between tellers and supervisors, he looked for ways to foster interaction among employees of all levels. As companies continue to flatten and rely on teams, managers must rely less on their authority and more on understanding these informal networks. Managers who can use maps to identify, leverage, and revamp informal networks will have the key to success.