Implication of inside-debt: signalling for bankruptcy probabilities within small firms

2014 ◽  
Vol 9 (2) ◽  
pp. 168-188 ◽  
Author(s):  
Raul Seppa
Keyword(s):  
Financial Risk ◽  
Small Firms ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Equity Capital ◽  
Bankruptcy Risk ◽  
Content Type ◽  
Inside Debt ◽  
Privately Held Firms ◽  
Retained Earnings

Purpose – Small privately held firms extensively use debt provided by principal owners and households (inside-debt) as an alternative capital source to straight equity capital. The purpose of the research study is to investigate inside-debt-bankruptcy relations. Design/methodology/approach – Inside-debt-bankruptcy relation is tested on three prominent bankruptcy prediction models using correlation and logit regression analysis. Sample consists of 314 Estonian small firms. Financial reports of 2007 are modelled against bankruptcies declared in 2009. Findings – Results imply that users of inside-debt are less profitable; they have weaker liquidity position and less retained earnings. Leverage is not found to be significant determinant between inside-debt users and non-users. Fundamental finding of the study suggests that the use of inside-debt is significantly and positively related to bankruptcy probability. While inside-debt carries no risk elements per se, findings are robust to indicate that the use of inside-debt has significant power to signal for increasing bankruptcy risk and as such, reducing information asymmetry of small firms. Research limitations/implications – This study is limited to single country data. Bankruptcy data fall to the period of economical recession. It is suggested to repeat the study in a normal economical situation and to extend sample size over different countries. Practical implications – Findings contribute to the understanding of firms' financial risk, firm behaviour and capital structure development. In a lending industry, results shall supplement to prudent credit risk assessment techniques and design of bankruptcy models in general. Originality/value – To the author's best knowledge, inside-debt-bankruptcy relation is not studied so far in the existing academic literature.

Bankruptcy prediction using multivariate grey prediction models

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yi-Chung Hu ◽  
Peng Jiang ◽  
Hang Jiang ◽  
Jung-Fa Tsai
Keyword(s):  
Time Series ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Financial Data ◽  
Grey Prediction ◽  
Grey System ◽  
Content Type ◽  
Business Environments ◽  
The Face ◽  
Financial Decision

PurposeIn the face of complex and challenging economic and business environments, developing and implementing approaches to predict bankruptcy has become important for firms. Bankruptcy prediction can be regarded as a grey system problem because while factors such as the liquidity, solvency and profitability of a firm influence whether it goes bankrupt, the precise manner in which these factors influence the discrimination between failed and non-failed firms is uncertain. In view of the applicability of multivariate grey prediction models (MGPMs), this paper aimed to develop a grey bankruptcy prediction model (GBPM) based on the GM (1, N) (BP-GM (1, N)).Design/methodology/approachAs the traditional GM (1, N) is designed for time series forecasting, it is better to find an appropriate permutation of firms in the financial data as if the resulting sequences are time series. To solve this challenging problem, this paper proposes GBPMs by integrating genetic algorithms (GAs) into the GM (1, N).FindingsExperimental results obtained for the financial data of Taiwanese firms in the information technology industries demonstrated that the proposed BP-GM (1, N) performs well.Practical implicationsAmong artificial intelligence (AI)-based techniques, GBPMs are capable of explaining which of the financial ratios has a stronger impact on bankruptcy prediction by driving coefficients.Originality/valueApplying MGPMs to a problem without relation to time series is challenging. This paper focused on bankruptcy prediction, a crucial issue in financial decision-making for businesses, and proposed several GBPMs.

Corporate bankruptcy prediction models applied to emerging economies

2007 ◽  
Vol 17 (4) ◽  
pp. 295-311 ◽  
Author(s):  
Ariel R. Sandin ◽  
Marcela Porporato
Keyword(s):  
Information Content ◽  
Emerging Economies ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Data Availability ◽  
Business Failure ◽  
Multiple Discriminant Analysis ◽  
Public Companies ◽  
Content Type ◽  
Profitability Ratios

PurposeThe paper's aim is to test the usefulness of ratio analysis to predict bankruptcy in a period of stability of an emerging economy, such as the case of Argentina in the 1990s.Design/methodology/approachFinancial profiles of 22 bankrupt and healthy companies are examined and a model is built using the multiple discriminant analysis technique, thus providing comparability with previous studies.FindingsThe set of models tested in this paper show that the financial data of Argentine companies in the 1990s do have information content, but the model to use depends on the preferences of the decision maker. Comparing models it is observed a common use of solvency ratios in terms of total assets and profitability ratios in terms of sales.Research limitations/implicationsData availability constitutes the primary limitation of this and similar studies, here is reflected in the sample size: 11 healthy and 11 bankrupt.Practical implicationsThe model can be used to assist investors, creditors, and regulators in Argentina and other emerging economies to predict business failure. The Z ′‐score model of Altman can be used for public companies in emerging economies because it pays attention to solvency indicators, but in rapid changing environment, profitability ratios should also be considered.Originality/valueThe incremental information content of profitability and solvency in predicting bankruptcy is examined and a simple and reliable failure prediction model for large Argentinean firms is developed. Also this paper offers a classification method that is publicly available to all investors and creditors interested in Argentinean companies.

Diagnosing the Financial Distress in Oil Drilling and Exploration Sector of India through Discriminant Analysis

2019 ◽  
Vol 23 (4) ◽  
pp. 364-373
Author(s):  
Anita Nandi ◽  
Partha Pratim Sengupta ◽  
Abhijit Dutta
Keyword(s):  
Discriminant Analysis ◽  
Manufacturing Industry ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Total Asset ◽  
Financial Health ◽  
Z Score ◽  
Working Capital ◽  
Oil Drilling ◽  
Retained Earnings

The present study is mainly devoted to the bankruptcy prediction models and their ability to assess a bankruptcy probability for oil drilling and exploration sector of Indian. The study puts an effort to determine the financial health of 12 selected companies from this sector of India for a period of 5 years. These companies serve the backbone of many other industries such as transport industry, manufacturing industry, automobile industry and so on of the Indian economy. The study has taken the reference of Altman’s Z-score model, where ratios such as working capital to total asset, retained earnings to total asset, earnings before interest and tax to total assets, market value of equity to book value of debt and sales to total assets have been taken. The discriminant analysis is conducted to validate the outcomes of Altman’s model to predict group membership and to forecast the overall industry condition. The study reveals that 75 per cent of the companies are in financially healthy zone. The results indicate that working capital/total assets can very well explain the Z-score. The research on financial health using Altman’s score is very limited in Indian context. Therefore, this study will add value to the existing body of literature for financial risk.

scholarly journals Bankruptcy Prediction Models Based on Value Measures

2020 ◽  
Vol 14 (1) ◽  
pp. 6
Author(s):  
Andrzej Jaki ◽  
Wojciech Ćwięk
Keyword(s):  
Discriminant Function ◽  
Prediction Models ◽  
Statistical Significance ◽  
Bankruptcy Prediction ◽  
Warning Signs ◽  
Discriminant Functions ◽  
Corporate Risk Management ◽  
Bankruptcy Risk ◽  
Corporate Risk ◽  
Diagnostic Variables

In the existing studies devoted to predicting bankruptcy, the authors of such models only used book measures. Considering the fact that the evolution of corporate measure efficiency (in addition to book measures) brought into existence and exposed the importance of cash measures, market measures, and measures based on the economic profit concept, it is justified to carry out research into the possibility of using these measures as variables within the discriminant function. The studied dataset was divided into a training set and a testing set based on two variants of the sample division. The assessment of the statistical significance of the built discriminant functions as well as the diagnostic variables was conducted using the STATISTICA package. The research was conducted separately for each variant. In the first step, a total of 30 discriminant models were created. This enabled us to select 20 diagnostic variables that were considered within the two models that were characterised by the highest predictive abilities—one for each variant. The discriminant function that was estimated for the first variant was based on the use of eight diagnostic variables, and 13 diagnostic variables were used in the function that was estimated for the second variant. The conducted analysis has proven that shareholder value measures are a useful tool that can be applied for the needs of corporate risk management in the area of the assessment of a firm’s bankruptcy risk. Using two variants of the division of the research sample into the training and testing sets, it turned out that the division affects the predictive efficiency of the discriminant functions. At the same time, the obtained findings tend to claim that the presence of the value measures from all four of the studied groups in the output set of the diagnostic variables is necessary for possibly building the most efficient tool for the early warning signs of bankruptcy risk.

Does intellectual capital help predict bankruptcy?

2018 ◽  
Vol 19 (2) ◽  
pp. 321-337 ◽  
Author(s):  
Velia Gabriella Cenciarelli ◽  
Giulio Greco ◽  
Marco Allegrini
Keyword(s):  
Performance Measures ◽  
Intellectual Capital ◽  
Financial Stability ◽  
Prediction Models ◽  
Predictive Ability ◽  
Time Frame ◽  
Bankruptcy Prediction ◽  
Superior Performance ◽  
Content Type

Purpose The purpose of this paper is to explore whether intellectual capital affects the probability that a particular firm will default. The authors also test whether including intellectual capital performance in bankruptcy prediction models improves their predictive ability. Design/methodology/approach Using a sample of US public companies from the period stretching from 1985 to 2015, the authors test whether intellectual capital performance reduces the probability of bankruptcy. The authors use the VAIC as an aggregate measure of corporate intellectual capital performance. Findings The findings show that the intellectual capital performance is negatively associated with the probability of default. The findings also indicate that the bankruptcy prediction models that include intellectual capital have a superior predictive ability over the standard models. Research limitations/implications This paper contributes to prior research on intellectual capital and firm performance. To the best of the knowledge, this is the first study to show that the benefits of intellectual capital extend from superior performance to long-term financial stability. The research can also contribute to bankruptcy studies. By using a time frame covering decades, the findings suggest that intellectual capital performance measures can be included in bankruptcy prediction models and can effectively complement traditional performance measures. Originality/value This paper highlights that intellectual capital is associated with long-term financial stability and a lower bankruptcy risk. Firms realising the potential of their intellectual capital can produce a virtuous circle between higher performance and greater financial stability.

scholarly journals Dynamic Bankruptcy Prediction Models for European Enterprises

2019 ◽  
Vol 12 (4) ◽  
pp. 185 ◽  
Author(s):  
Tomasz Korol
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Decision Makers ◽  
Research Approach ◽  
Bankruptcy Risk ◽  
Testing Sample ◽  
Forecasting Models ◽  
Two Samples

This manuscript is devoted to the issue of forecasting corporate bankruptcy. Determining a firm’s bankruptcy risk is one of the most interesting topics for investors and decision-makers. The aim of the paper is to develop and to evaluate dynamic bankruptcy prediction models for European enterprises. To conduct this objective, four forecasting models are developed with the use of four different methods—fuzzy sets, recurrent and multilayer artificial neural network, and decision trees. Such a research approach will answer the question of whether changes in indicators are relevant predictors of a company’s coming financial crisis because declines or increases in values do not immediately indicate that the company’s economic situation is deteriorating. The research relies on two samples of firms—the learning sample of 50 bankrupt and 50 non-bankrupt enterprises and the testing sample of 250 bankrupt and 250 non-bankrupt firms.

Extracting Key Phrases as Predictors of Corporate Bankruptcy: Empirical Analysis of Annual Reports by Text Mining

2011 ◽  
Vol 8 (1) ◽  
pp. 31-44 ◽  
Author(s):  
Cindy Yoshiko Shirata ◽  
Hironori Takeuchi ◽  
Shiho Ogino ◽  
Hideo Watanabe
Keyword(s):  
Annual Report ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Annual Reports ◽  
Financial Reports ◽  
Nonfinancial Information ◽  
Financial Position ◽  
Key Phrases ◽  
Retained Earnings ◽  
A Company

ABSTRACT Bankruptcy predictions have been one of the most interesting topics for accounting researchers. Most bankruptcy prediction models are developed by using financial ratios. However, signs of the changing financial position of a company may appear in the nonfinancial information earlier than we can identify the changes in the financial numbers. In recent years, analysis of qualitative information has become remarkably important, because frequent changes in accounting standards have made it difficult to compare financial numbers between years. In this study, we analyzed the sentences in financial reports in Japan and extracted key phrases/descriptions to predict bankruptcy. Our research revealed that if some particular expressions appear together with the word “dividend” or “retained earnings” in the same section of an annual report, they were effective in distinguishing between bankrupt companies and non-bankrupt companies.

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

2021 ◽  
Vol 21 (2) ◽  
pp. 76-96
Author(s):  
Bartłomiej Pilch
Keyword(s):  
Research Methodology ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Entire Sample ◽  
Bankruptcy Risk ◽  
Inconsistent Information ◽  
The Subject ◽  
Consistent Information

Abstract 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.

Identifying impact of variables in deep learning models on bankruptcy prediction of construction contractors

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Youjin Jang ◽  
Inbae Jeong ◽  
Yong K. Cho
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Shapley Value ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Content Type ◽  
Macroeconomic Variables ◽  
Input Variables ◽  
The Impact

PurposeThe study seeks to identify the impact of variables in a deep learning-based bankruptcy prediction model, which has achieved superior performance to other prediction models but cannot easily interpret hidden processes.Design/methodology/approachThis study developed three LSTM-RNN–based models that predicted the probability of bankruptcy before 1, 2 and 3 years using financial, the construction market and macroeconomic variables as input variables. Then, the impacts of the input variables that affected prediction accuracy in each model were identified by using Shapley value and compared among the three models. This study also investigated the prediction accuracy using variants of input variables grouped sequentially by high-impact ranking.FindingsThe results showed that the prediction accuracies were largely impacted by “housing starts” in all models. As the prediction period increased, the effects of macroeconomic variables on prediction accuracy increased, whereas the impact of “return on assets” on prediction accuracy decreased. It also found that the “current ratio” and “debt ratio” significantly influenced the prediction accuracies in all models. Also, the results revealed that similar prediction accuracies could be achieved using only 8, 10, and 10 variables out of a total of 18 variables for the 1-, 2-, and 3-year prediction models, respectively.Originality/valueThis study provides a Shapley value-based approach to identify how each input variable in a deep-learning bankruptcy prediction model. The findings of this study can not only assist in obtaining better insights into the underlying concept of bankruptcy but also use to select variables by removing those identified as less significant.

The capital constraint paradox in micro and small family and nonfamily firms

2016 ◽  
Vol 5 (1) ◽  
pp. 38-62 ◽  
Author(s):  
Anders Bornhäll ◽  
Dan Johansson ◽  
Johanna Palmberg
Keyword(s):  
Small Firms ◽  
Firm Growth ◽  
Family Firms ◽  
Equity Capital ◽  
Special Focus ◽  
Firm Level ◽  
Content Type ◽  
Section Data ◽  
External Capital ◽  
And Control

Purpose – The purpose of this paper is to investigate the importance of the entrepreneur’s quest for independence and control over the firm for governance and financing strategies with a special focus on family firms and how they differ from nonfamily firms. Design/methodology/approach – The analysis is based on 1,000 telephone interviews with Swedish micro and small firms. The survey data are matched with firm-level data from the Bureau van Dijks database ORBIS. Findings – The analysis shows that independence is a prime motive for enterprises, statistically significantly more so for family owners. Family owners are more prone to use either their own savings or loans from family and are more reluctant to resort to external equity capital. Our results indicate a potential “capital constraint paradox”; there might be an abundance of external capital while firm growth is simultaneously constrained by a lack of internal funds. Research limitations/implications – The main limitation is that the study is based on cross-section data. Future studies could thus be based on longitudinal data. Practical implications – The authors argue that policy makers must recognize independence and control aversion as strong norms that guide entrepreneurial action and that micro- and small-firm growth would profit more from lower personal and corporate income taxes compared to policy schemes intended to increase the supply of external capital. Originality/value – The paper offers new insights regarding the value of independence and how it affects strategic decisions within the firm.

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