Applying appropriate models to predict bankruptcy for Vietnamese listed construction companies

This study focuses on assessing the suitability and condition of various bankruptcy risk models applied to construction companies listed on the Vietnam Stock Market. In this study, the panel data were collected from the disclosed financial statements of the companies from 2012 to 2017. Through the assessment, bankruptcy risks are predicted for the companies that are experiencing initial signals such as delisting, compulsory supervision. In the next step, interviews were conducted to justify which of the following factors may indicate the companies at the risk of being bankrupted: asset management, capital structure, business size, and/or state management.

The Role of Family Ownership in Survival and Bouncing Back

The latest global economic and financial crisis has been a litmus test for companies, especially for SMEs. These companies have had to demonstrate their ability to be resilient, surviving first and then recovering. This chapter studies the role of family ownership in the survival and recovery of SMEs during a stressful event. From a perspective based on the complementarity or substitutability of goals that family firms pursue, the authors propose that family ownership has a positive effect on survival but a negative effect on recovery. Furthermore, they propose that the risk of bankruptcy before a crisis moderates the relationship between family ownership and survival. Hypotheses have been tested with a dataset of 3,133 Spanish manufacturing MEs finding evidence for the positive role of family ownership in survival and for the moderating effect of previous bankruptcy risk. The empirical data confirms good news for family-owned firms.

Buyer’s Bankruptcy Risk, Sourcing Strategy, and Firm Value: Evidence from the Supplier Protection Act

What should a distressed buyer’s sourcing strategy be? We find that this depends on the dynamics in a potential in-court bankruptcy. To establish causality, we use a novel sourcing data set in combination with a unique quasi-natural experimental setting provided by a regulatory shock that significantly strengthened the protection granted to suppliers when a distressed buyer files for bankruptcy: the Supplier Protection Act. We find that, following this regulatory change, the number of suppliers for buyers near financial distress (those most affected by the act, the treated group) increased by nearly 35% relative to financially sound firms (the control group). We also find that this shift allowed distressed buyers to obtain more trade credit, expand inventory holdings, and increase performance, leading to an overall increase in firm value of 7.2%. In turn, these effects led to a sizable reduction in the probability of the affected buyers defaulting and filing for bankruptcy. Our results have important implications for corporate executives: right-sizing the supply base can be critical for buyers near financial distress, and implementing policies to engage and protect suppliers can be the way out of distress. This paper was accepted by Vishal Gaur, operations management.

The nexus between financial failure and stock prices: Panel Pedroni, Panel Kao and Panel AR DL Co-Integration Tests in Turkey REIT s

In today’s globally competitive environment, companies must keep up with these competitive conditions to be successful. Failure of companies to show the expected financial performance, fulfil their financial obligations, or reach their financial targets is considered a financial failure or bankruptcy risk. Real Estate Investment Companies or Trusts (REICs or REITs) are capital market institutions that qualify as legal entities and are partnerships in a joint-stock company that provides financing to all kinds of real estate or real estate projects and bring together many investors for the desired real estate. REITs are an essential investment choice that continues its rapid development in Turkey. This study aims to examine the relationships between the ZScores calculated by periods of REIT companies traded in Borsa Istanbul between 2010–2019 and the stock price performances. In the study, primarily Altman Z-Score and Springate S-Score values of companies traded in Borsa Istanbul were calculated with the help of financial ratios. Then, Pedroni and Kao panel co-integration analysis and Dumitrescu-Hurlin panel causality analysis were performed. According to the analysis results, there is a long-term relationship between the financial failure scores of REIT companies and their stock prices. However, a causality relationship was found between the series.

An analysis of the effectiveness of bankruptcy prediction models – an industry approach

Research background: Bankruptcy prediction models are frequently used in research. However, an industry approach is not often carried out. Due to this, this study included trends observable between the number of bankruptcies and its prediction by models. Purpose: The aim of the paper is to verify if changes in the number of actual bankruptcy in individual industries are properly predicted by the models. Also, if analyzed models are providing consistent information according to the risk of bankruptcy between industries. Research methodology: The data were collected from the Orbis database and the Coface reports. The period included in the study is 2014–2019. 5 Polish bankruptcy prediction models were used: these by D. Hadasik, E. Mączyńska and M. Zawadzki, M. Pogodzińska and S. Sojak, D. Wierzba and the Poznan one. Results: The analyzed models do not properly predict changes in the number of bankruptcy in individual industries, however, 3 out of 5 correctly predicted the trend for the entire sample. Analyzed models often provide inconsistent information. Hence, it seems sensible to use more than a few models in any further analyzes. Novelty: In the literature of the subject, there are often carried out analyses focused on the effectiveness of bankruptcy prediction models regarding individual companies. This research is focused on the prediction of changes in the number of companies to be considered as at bankruptcy risk between industries, and also on comparing these models.

Performance Efficiency of Islamic Banks in Pakistan: An Application of CAMEL Model

This paper aims at evaluating the soundness of Islamic banks working in Pakistan for the period 2008 to 2015. The current study comprises of five full fledge Islamic banks, actively working in Pakistan. The study applied CAMEL parameters to achieve its purpose. It has utilized capital adequacy, Asset quality, Management ability, Earnings ability and liquidity ratios of selected banks. The findings of the study show that although Islamic banks in Pakistan have adequate capital, yet they have limited asset management ability and substandard earnings ability during the selected time period. The study also depicted that Islamic banks have high degree of liquidity thus enjoying low bankruptcy risk. The findings of this study is of prime interest for the management and shareholders of the selected banks. On one hand, the results of this study provide an insight into the performance of these banks. On the other hand, the results also contain useful information for managers and policy makers as they could find and correct easily, the weak areas of their respective institutions.

IMPACTS OF EARNINGS MANAGEMENT ON CORPORATE FAILURE: A CASE STUDY OF WIRECARD

The purpose of this study is to find out whether earnings management has impacts on bankruptcy risk based on the data of Wirecard Company. The M-score of Beneish's (1999) model has been used to detect the probability of earnings management. On the other hand, the Z"-Score of Altman's (1968) model has been applied to detect Corporate Failure. Both the models are widely used models in their respective fields. The data from 2002 to 2019 were collected from the annual reports of the Wirecard Company. The result of M-Score indicates that earnings management has a significant impact on the corporate failure (Z-Score) of the company. This finding specifies that a financially distressed firm adopts earnings manipulations. The finding also implies that earnings manipulations harm the financial health of a firm. According to the findings, it can be suggested that to know the financial aspects of a company, both the (Beneish M-model and Altman Z-score model) models could be used concurrently. Beneish M-model is for detecting earnings management and the Altman Z-score model is for determining corporate failure. The novelty of the study is that no study was done on Wirecard Company focusing on the association between earnings management & bankruptcy risk.

THE ASSESSMENT OF BANKRUPTCY RISK OF COMPANIES FROM THE SECTOR OF DRYING AND STORAGE OF CROPS AND PLANTS

Contemporary market conditions make all business entities confront various risks – credit risk, liquidity risk, cash flow risk and market risk. Companies should predict these particular business risks and manage them adequately in order to minimize their influence. The assessment of creditworthiness and bankruptcy risk has a major role in the process of prediction and management of company risks. The creditworthiness of companies presents their ability to meet financial obligations to their creditors contemporary business conditions recognize several methods for assessment of credit ability of entities. One of the most commonly used models for credit ability valuation, as well as prediction of bankruptcy likelihood in a company is the Altman's Z-score model. The main scope of this article is the assessment of bankruptcy risk by using Altman's Z-score model for the group of companies from the agricultural branch of drying and storage of fruit.

Predicting Bank Failures: A Synthesis of Literature and Directions for Future Research

Risk management has been a topic of great interest to Michael McAleer. Even as recent as 2020, his paper on risk management for COVID-19 was published. In his memory, this article is focused on bankruptcy risk in financial firms. For financial institutions in particular, banks are considered special, given that they perform risk management functions that are unique. Risks in banking arise from both internal and external factors. The GFC underlined the need for comprehensive risk management, and researchers since then have been working towards fulfilling that need. Similarly, the central banks across the world have begun periodic stress-testing of banks’ ability to withstand shocks. This paper investigates the machine-learning and statistical techniques used in the literature on bank failure prediction. The study finds that though considerable progress has been made using advanced statistical and computational techniques, given the complex nature of banking risk, the ability of statistical techniques to predict bank failures is limited. Machine-learning-based models are increasingly becoming popular due to their significant predictive ability. The paper also suggests the directions for future research.