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Article
Publication date: 9 May 2016

Mahdi Salehi and Mahmoud Mousavi Shiri

Bankruptcy, stock price fluctuations and making decisions to invest on the listed companies on the Tehran Stock Exchange show the need to have some tools for evaluating the…

Abstract

Purpose

Bankruptcy, stock price fluctuations and making decisions to invest on the listed companies on the Tehran Stock Exchange show the need to have some tools for evaluating the financial potential of companies. One of the tools for evaluating financial power to investment in companies is using analysis of financial ratios and obtaining the patterns for predicting the bankruptcy of companies. The purpose of this study is to modify the current patterns for predicting bankruptcy in proportion to the environmental status of Iran and to present a new pattern for determining the bankruptcy of the listed companies.

Design/methodology/approach

To modify the patterns and present a new pattern, in this research, the hypotheses pertinent to the ability for right classification of the companies are designed by the modified patterns of predicting the company’s bankruptcy and ranking, that is according to the envelopment analysis method and by comparison of the results and presenting a new prediction pattern.

Findings

The hypotheses test results show that modification of bankruptcy patterns and presentation of a new bankruptcy pattern are confirmed by the data envelopment analysis.

Originality/value

The current paper is almost the first paper which combined several different methods of bankruptcy prediction.

Details

International Journal of Law and Management, vol. 58 no. 3
Type: Research Article
ISSN: 1754-243X

Keywords

Article
Publication date: 2 March 2015

Martin Aruldoss, Miranda Lakshmi Travis and V. Prasanna Venkatesan

Bankruptcy is a financial failure of a business or an organization. Different kinds of bankruptcy prediction techniques are proposed to predict it. But, they are restricted as…

2009

Abstract

Purpose

Bankruptcy is a financial failure of a business or an organization. Different kinds of bankruptcy prediction techniques are proposed to predict it. But, they are restricted as techniques in predicting the bankruptcy and not addressing the associated activities like acquiring the suitable data and delivering the results to the user after processing it. This situation demands to look for a comprehensive solution for predicting bankruptcy with intelligence. The paper aims to discuss these issues.

Design/methodology/approach

To model Business Intelligence (BI) solution for BP the concept of reference model is used. A Reference Model for Business Intelligence to Predict Bankruptcy (RMBIPB) is designed by applying unit operations as hierarchical structure with abstract components. The layers of RMBIPB are constructed from the hierarchical structure of the model and the components, which are part of the reference model. In this model, each layer is designed based on the functional requirements of the Business Intelligence System (BIS).

Findings

This reference model exhibits the non functional software qualities intended for the appropriate unit operations. It has flexible design in which techniques are selected with minimal effort to conduct the bankruptcy prediction. The same reference model for another domain can be implemented with different kinds of techniques for bankruptcy prediction.

Research limitations/implications

This model is designed using unit operations and the software qualities exhibited by RMBIPB are limited by unit operations. The data set which is applied in RMBIPB is limited to Indian banks.

Originality/value

A comprehensive bankruptcy prediction model using BI with customized reporting.

Article
Publication date: 12 November 2018

Oliver Lukason

This study aims to find out whether firm failure processes are age- and size-dependent.

Abstract

Purpose

This study aims to find out whether firm failure processes are age- and size-dependent.

Design/methodology/approach

The sample consists of 333 bankrupted Estonian firms. Failure processes are detected with consecutive factor and cluster analyses of six financial variables calculated for three pre-failure years. Multinomial logistic regression is applied to study the interconnections between failure processes (dependent variable) and firm size and age (independent variables). In addition, the contingency between detected failure processes and failure causes obtained from court judgements are studied.

Findings

Three failure processes are detected, of which the predominant one accounting for 55 per cent of cases is a gradual failure process, indicating a step-by-step decline in the values of financial variables. The two minority processes are mixed, meaning that some financial variables are poor for many years before the bankruptcy and others decrease only shortly before bankruptcy declaration. With an increase in firm size, the gradual failure process becomes more common, but in turn, the presence of the gradual failure process is not age-dependent. Failure causes detected by trustees are not associated with failure processes.

Originality/value

This paper is the first one to specifically outline the age and size dependencies of firm failure processes. In addition, the interconnection of failure causes and firm failure processes detected with financial variables are rarely studied topics.

Details

International Journal of Law and Management, vol. 60 no. 6
Type: Research Article
ISSN: 1754-243X

Keywords

Article
Publication date: 21 March 2016

Scott Dellana and David West

The purpose of this paper is to apply survival analysis, using Cox proportional hazards regression (CPHR), to the problem of predicting if and when supply chain (SC) customers or…

3175

Abstract

Purpose

The purpose of this paper is to apply survival analysis, using Cox proportional hazards regression (CPHR), to the problem of predicting if and when supply chain (SC) customers or suppliers might file a petition for bankruptcy so that proactive steps may be taken to avoid a SC disruption.

Design/methodology/approach

CPHR is first compared to multiple discriminant analysis (MDA) and logistic regression (LR) to assess its suitability and accuracy to SC applications using three years of financial quarterly data for 69 non-bankrupt and 74 bankrupt organizations. A k-means clustering approach is then applied to the survival curves of all 143 organizations to explore heuristics for predicting the timing of bankruptcy petitions.

Findings

CPHR makes bankruptcy predictions at least as accurately as MDA and LR. The survival function also provides valuable information on when bankruptcy might occur. This information allows SC members to be prioritized into three groups: financially healthy companies of no immediate risk, companies with imminent risk of bankruptcy and companies with intermediate levels of risk that need monitoring.

Originality/value

The current paper proposes a new analytical approach to scanning and assessing the financial risk of SC members (suppliers or customers). Traditional models are able to predict if but not when a financial failure will occur. Lacking this information, it is impossible for SC managers to prioritize risk mitigation activities. A simple decision rule is developed to guide SC managers in setting these priorities.

Details

The Journal of Risk Finance, vol. 17 no. 2
Type: Research Article
ISSN: 1526-5943

Keywords

Book part
Publication date: 13 March 2023

Rahul Kumar, Soumya Guha Deb and Shubhadeep Mukherjee

Nonperforming assets in any banking system have stressed the economic health of nations. Resultantly, literature has given considerable impetus to predict failures and bankruptcy

Abstract

Nonperforming assets in any banking system have stressed the economic health of nations. Resultantly, literature has given considerable impetus to predict failures and bankruptcy. Past studies have focused on the outcome of failures, while, there is a dearth of studies focusing on ongoing firms in bad shape. We plug this gap and attempt to identify underlying communication patterns for firms witnessing prolonged underperformance. Using text mining, we extract and analyze semantic, linguistic, emotional, and sentiment-based features in non-numeric communication channels of these poor-performing firms and their peers. These uncovered patterns highlight the use of vocabulary and tone of communication, in correspondence to their financial well-being. Furthermore, using such patterns, we deploy various Machine Learning algorithms to identify loser firm(s) way ahead in time. We observe promising accuracy over a time window of five years. Such early warning signals can be of critical importance to various stakeholders of a firm. Exploration of writing style-related features for any firm would help its investors, lending agencies to assess the likelihood of future underperformance. Firm management can use them to take suitable precautionary measures and preempt the future possibility of distress. While investors and lenders can be benefitted from this incremental information to identify the likelihood of future failures.

Details

Advances in Accounting Behavioral Research
Type: Book
ISBN: 978-1-80455-798-3

Keywords

Article
Publication date: 17 March 2023

Stewart Jones

This study updates the literature review of Jones (1987) published in this journal. The study pays particular attention to two important themes that have shaped the field over the…

Abstract

Purpose

This study updates the literature review of Jones (1987) published in this journal. The study pays particular attention to two important themes that have shaped the field over the past 35 years: (1) the development of a range of innovative new statistical learning methods, particularly advanced machine learning methods such as stochastic gradient boosting, adaptive boosting, random forests and deep learning, and (2) the emergence of a wide variety of bankruptcy predictor variables extending beyond traditional financial ratios, including market-based variables, earnings management proxies, auditor going concern opinions (GCOs) and corporate governance attributes. Several directions for future research are discussed.

Design/methodology/approach

This study provides a systematic review of the corporate failure literature over the past 35 years with a particular focus on the emergence of new statistical learning methodologies and predictor variables. This synthesis of the literature evaluates the strength and limitations of different modelling approaches under different circumstances and provides an overall evaluation the relative contribution of alternative predictor variables. The study aims to provide a transparent, reproducible and interpretable review of the literature. The literature review also takes a theme-centric rather than author-centric approach and focuses on structured themes that have dominated the literature since 1987.

Findings

There are several major findings of this study. First, advanced machine learning methods appear to have the most promise for future firm failure research. Not only do these methods predict significantly better than conventional models, but they also possess many appealing statistical properties. Second, there are now a much wider range of variables being used to model and predict firm failure. However, the literature needs to be interpreted with some caution given the many mixed findings. Finally, there are still a number of unresolved methodological issues arising from the Jones (1987) study that still requiring research attention.

Originality/value

The study explains the connections and derivations between a wide range of firm failure models, from simpler linear models to advanced machine learning methods such as gradient boosting, random forests, adaptive boosting and deep learning. The paper highlights the most promising models for future research, particularly in terms of their predictive power, underlying statistical properties and issues of practical implementation. The study also draws together an extensive literature on alternative predictor variables and provides insights into the role and behaviour of alternative predictor variables in firm failure research.

Details

Journal of Accounting Literature, vol. 45 no. 2
Type: Research Article
ISSN: 0737-4607

Keywords

Article
Publication date: 18 June 2020

Yi-Chung Hu, Peng Jiang, Hang Jiang and Jung-Fa Tsai

In the face of complex and challenging economic and business environments, developing and implementing approaches to predict bankruptcy has become important for firms. Bankruptcy

Abstract

Purpose

In 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/approach

As 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).

Findings

Experimental 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 implications

Among 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/value

Applying 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.

Details

Grey Systems: Theory and Application, vol. 11 no. 1
Type: Research Article
ISSN: 2043-9377

Keywords

Article
Publication date: 8 January 2021

Youjin Jang, Inbae Jeong and Yong K. Cho

The 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…

Abstract

Purpose

The 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/approach

This 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.

Findings

The 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/value

This 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.

Details

Engineering, Construction and Architectural Management, vol. 28 no. 10
Type: Research Article
ISSN: 0969-9988

Keywords

Article
Publication date: 19 April 2022

Abedalqader Rababah, Homa Molavi and Shayan Farhang Doust

The aim of this study is to examine the effect of financial leverage impact on customer satisfaction and marketing costs including research and development (R&D) and advertisement…

Abstract

Purpose

The aim of this study is to examine the effect of financial leverage impact on customer satisfaction and marketing costs including research and development (R&D) and advertisement costs. Furthermore, the authors aim to investigate whether customer satisfaction as well as financial distress moderates the effect of financial leverage impact on customer satisfaction and marketing costs including R&D and advertisement costs.

Design/methodology/approach

The statistical population of this study consists of listed companies on the Tehran Stock Exchange manually obtained from different industries in 2017. Multivariate regression based on data compilation methodology is used to test research hypotheses.

Findings

The results indicate that financial leverage is negatively and significantly associated with customer satisfaction and this negative relationship is more pronounced in companies with lower sale growth. Furthermore, the authors' results suggest that customer satisfaction negatively (positively) and significantly affects firm value in companies with lower (higher)-financial leverage. The authors also demonstrate that there is no significant relationship between financial leverage caused by financial flexibility and firm value caused by customer's satisfaction (CS). The authors' findings also suggest that financial distress significantly affects the relationship between financial leverage and customer satisfaction. Finally, the authors' find that financial leverage significantly affects firms' R&D and advertisement costs.

Research limitations/implications

Since the fundamental institutional assumptions underpinning the Western and even East Asia financial models are not valid in the institutional environment of Iran, the authors' findings could provide substantial implications for the authors' understanding of the relationship between finance and R&D costs and contribute substantially to customer satisfaction and firm value literature as well. The sample country of the present paper has recently experienced a spate of financial collapses that somewhat contributes, indirectly, to financial distress incurred by the Iranian firms. Moreover, R&D costs are growing among the Iranian quoted firms.

Originality/value

Since the fundamental institutional assumptions underpinning the Western and even East Asia financial models are not valid in the institutional environment of Iran, the authors' findings could provide substantial implications for our understanding of the relationship between finance and R&D costs and contribute substantially to customer satisfaction and firm value literature as well. The sample country of the present paper has recently experienced a spate of financial collapses that somewhat contributes, indirectly, to financial distress incurred by the Iranian firms. Moreover, R&D costs are growing among the Iranian quoted firms.

Details

Journal of Applied Accounting Research, vol. 23 no. 4
Type: Research Article
ISSN: 0967-5426

Keywords

Article
Publication date: 1 July 2000

M.L. Nasir, R.I. John, S.C. Bennett, D.M. Russell and A Patel

An appropriate use of neural computing techniques is to apply them to corporate bankruptcy prediction, where conventional solutions can be hard to obtain. Having said that…

1007

Abstract

An appropriate use of neural computing techniques is to apply them to corporate bankruptcy prediction, where conventional solutions can be hard to obtain. Having said that, choosing an appropriate Artificial Neural Network topology (ANN) for predicting corporate bankruptcy would remain a daunting prospect. The context of the problem is that there are no fixed rules in determining the ANN structure or its parameter values, a large number of ANN topologies may have to be constructed with different structures and parameters before determining an acceptable model. The trial‐and‐error process can be tedious, and the experience of the ANN user in constructing the topologies is invaluable in the search for a good model. Yet, a permanent solution does not exist. This paper identifies a non trivial novel approach for implementing artificial neural networks for the prediction of corporate bankruptcy by applying inter‐connected neural networks. The proposed approach is to produce a neural network architecture that captures the underlying characteristics of the problem domain. The research primarily employed financial data sets from the London Stock Exchange and Jordans financial database of major public and private British companies. Early results indicate that an ANN appears to outperform the traditional approach in forecasting corporate bankruptcy.

Details

Journal of Applied Accounting Research, vol. 5 no. 3
Type: Research Article
ISSN: 0967-5426

Keywords

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