Financial Distress Prediction Research Articles

Analisis Financial Distress Dengan Pendekatan Altman Pada Awal Covid-19 Di Indonesia (Studi Empiris Perusahaan Transportasi dan Logistik Periode 2019)

The purpose of this study is to analyze financial distress with Altman's (Z-Score) approach to transportation and logistics manufacturing companies during the covid-19 pandemic. The financial ratios analyzed are Return On Assets, Return On Equity, Current Ratio, and Debt To Asset Ratio as independent variables and Financial Distress as dependent variables. The population and samples in this study are Transportation and Logistics manufacturing companies listed on the Indonesia Stock Exchange in 2019. The samples in this study used census sampling techniques and were processed using logistic regression analysis. From the results of the study, it shows that the profitability ratio measured by Return On Assets and liquidity ratio measured by current ratio affect the company's financial distress prediction. The profitability ratio measured by Return On Equity and the leverage ratio measured by the Debt To Asset Ratio have no effect on the company's financial distress.

ANALISIS PREDIKSI FINANCIAL DISTRESS DENGAN MENGGUNAKAN MODEL ALTMAN Z-SCORE PADA PERUSAHAAN RITEL TAHUN 2018-2020

<em><span lang="EN-US">The retail sector is one of the sectors affected by the covid-19 pandemic. Giant is one of the hypermarkets that has closed several outlets from 2019 to 2021 permanently closed. This is due to suboptimal financial performance and exacerbated by the impact of the covid-19 pandemic. The purpose of this study is to analyze financial distress predictions in the retail sector. This study uses a sample of retail public sector companies that run hypermarket, superstore and minimarket businesses. The research analysis technique used the original Altman Z-Score model, which was developed in 1968. This study indicates generally companies are in the “safe” zone for 2018 and 2019, except for HERO, which is in the “gray” zone. Furthermore, in 2020, several companies are still in the "safe" zone, except MIDI and RALS shifting to the "gray" zone and even HERO shifting to the "distress" zone. The implications of the results of this study provide information for retail companies to pay attention to their financial conditions so that they can determine long-term strategies to have long business sustainability.</span></em>

The Role of Board Independence and Ownership Structure in Improving the Efficacy of Corporate Financial Distress Prediction Model: Evidence from India

The study aimed to investigate the role of non-financial measures in predicting corporate financial distress in the Indian industrial sector. The proportion of independent directors on the board and the proportion of the promoters’ share in the ownership structure of the business were the non-financial measures that were analysed, along with ten financial measures. For this, sample data consisted of 82 companies that had filed for bankruptcy under the Insolvency and Bankruptcy Code (IBC). An equal number of matching financially sound companies also constituted the sample. Therefore, the total sample size was 164 companies. Data for five years immediately preceding the bankruptcy filing was collected for the sample companies. The data of 120 companies evenly drawn from the two groups of companies were used for developing the model and the remaining data were used for validating the developed model. Two binary logistic regression models were developed, M1 and M2, where M1 was formulated with both financial and non-financial variables, and M2 only had financial variables as predictors. The diagnostic ability of the model was tested with the aid of the receiver operating curve (ROC), area under the curve (AUC), sensitivity, specificity and annual accuracy. The results of the study show that inclusion of the two non-financial variables improved the efficacy of the financial distress prediction model. This study made a unique attempt to provide empirical evidence on the role played by non-financial variables in improving the efficiency of corporate distress prediction models.

A Deep Learning-Based Approach to Constructing a Domain Sentiment Lexicon: a Case Study in Financial Distress Prediction

Financial text mining has been widely viewed as a promising approach to analyzing questions related to financial issues. However, only a few studies have ever emphasized constructing financial domain sentiment lexicons, especially in a Chinese financial context. In light of this gap in research, the purpose of this study is to generate a Chinese financial domain sentiment lexicon (CFDSL). Then, that lexicon is applied to financial distress prediction (FDP). This study proposes a deep learning-based framework to construct a domain sentiment lexicon, employing words vector models and deep learning-based classifiers in the process. To evaluate the effectiveness of the CFDSL, this study applies the lexicon to analyzing annual reports; sentiment features are also regarded as predictive factors in FDP. The experiment results indicate that deep learning-based models can achieve satisfactory results in generating a CFDSL that mainly covers four aspects of sentiment words, including capital markets, stock markets, companies’ internal business conditions and politics. This study also discovers that sentiment features that are calculated four years prior to the predicted benchmark year enable the optimum performance. In addition, CFDSL-based sentiment features show advantages in FDP, compared with other lexicons. This study makes a novel contribution to existing research, as it expands the method of constructing financial domain sentiment lexicons. This paper also provides new findings that can have significant implications for the provision of early warning signals of Chinese listed companies’ financial risk.

Predicting Financial Distress in the Indian Banking Sector: A Comparative Study Between the Logistic Regression, LDA and ANN Models

Financial distress is a socially and economically significant issue that affects almost every firm across the world. Predicting financial distress in the banking industry can substantially aid in the reduction of losses and can help avoid misallocation of banks’ financial resources. Models for financial distress prediction of banks are being increasingly employed as important tools to identify early warning signals for the whole banking system. This study attempts to forecast the financial distress of commercial banks by developing a bankruptcy prediction model for banks. The sample size for the study is 75 Indian banks. Logistic, linear discriminant analysis (LDA) and artificial neural network (ANN) models have been applied on the last 5 years’ (2015–2019) data of these banks. Data analysis results reveal the logistic and LDA models exhibiting similar prediction accuracy. The results of the ANN prediction model exhibit better prediction accuracy. It is expected that the results of this study will be useful for managers, depositors, regulatory bodies and shareholders to better manage their interests in the banking sector of the country.

Prediksi Financial Distress Dengan Menggunakan Pendekatan Logistic Regression (Logit)

Research conducted empirically tested the influence of financial factors on the possibility of financial distress. This research was conducted with quantitative methods using secondary data. The population of this research is listed companies on the Stock Exchange. Then the sample taken is based on purposive sampling with the criteria of a company that is not merged and has a complete financial report in accordance with research needs. This study uses logistic regression statistical tools because the dependent variable is a dummy variable, while the independent variable is a metric variable. The results showed that the leverage variable has a positive effect on the prediction of financial distress, then the profitability variable has a negative effect on the prediction of financial distress, the next variable is that the firm size variable has no influence on the prediction of financial distress.

Pengaruh Rasio RGEC, Bank Size, Market Value, serta Variabel Makroekonomi terhadap Prediksi Financial Distress Menggunakan CD-Index

The Bank is a financial institution that functions to accumulate and distribute funds to the public and providing other banking services. In channelling funds, banks are not protected from the internal and external risks that cause financial difficulties. This study examines the influence of RGEC factors, bank size, market value, and macroeconomic to predict financial distress using the Crisis and Default Index. RGEC factors used in this study is risk profile which is proxy by NPL and LDR, good corporate governance is proxy of the size of board directors, earnings proxied by ROA, ROE, NIM, BOPO, and capital is proxied by CAR. The variable of bank size and market value is proxied by PBV and PER. While the macroeconomic variables used are economic growth, inflation, and exchange rates. This study used 19 banks in the BUSN group as a research sample during 2015-2019. The result showed that internal factors and bank size did not significantly influence the prediction of financial distress. Market value variable proxied by PBV has a significant negative effect on the prediction of financial distress. This is important for internal bank parties to keep stock prices from falling and investors are interested in investing; PER and macroeconomic variables do not significantly affect the prediction of financial distress because good internal banking fundamentals face changes in economic conditions. There is no influence of variables with financial distress because these variables appropriate with Bank Indonesia and BUSN are implementing them correctly.

PREDIKSI FINANCIAL DISTRESS PADA PERUSAHAAN TRANSPORTASI YANG TERDAFTAR DI BURSA EFEK INDONESIA

Financial distress is a state in which a company is having financial problems prior to filing for bankruptcy. The company's financial difficulties can be caused by a variety of problems, both internal and external to the company. The study aims to analyze the prediction of financial distress in transportation companies listed on the Indonesia Stock Exchange (IDX) using financial ratios. The financial ratios used are liquidity ratio (QR), profitability ratio (NPM), leverage ratio (Long Term Debt to Total Asset), and activity ratio (Total Asset Turnover). This research was conducted during the 2016-2019 period with a total population of 43 transportation companies, and 25 company data samples were chosen using a non-probability sampling method known as purposive sampling. Logistic regression analysis was used to analyze the data. The results show that the liquidity ratio (QR) cannot contribute to the financial distress, profitability ratio (NPM) can contribute to the financial distress, leverage ratio (LTDTA) cannot contribute to the financial, and the activity ratio (TATO) can contribute to the financial distress of transportation companies listed on the Indonesia Stock Exchange.

Prediction modelling the financial distress using corporate governance indicators in Indonesia

We examine whether the indicators of company governance procedures are associated with the risk of bankruptcy or financial distress in Indonesia. An empirical study we conducted using a causal model of corporate governance indicators in forecasting financial distress. The data used in this study is panel data. Using samples from assembling companies registered on the Indonesia Stock Exchange during the 2017-2019 period, we obtained as many as 105 observations selected by the purposive sampling method. Our results indicate that financial distress can be predicted by corporate governance mechanisms, although statistically it is only proven by a few indicators in our study. Specifically, our results demonstrate that institutional ownership, managerial ownership, and independent commissioners do not affect financial distress. Furthermore, our study shows evidence of a significant influence between the size of the board of directors and audit committee on financial distress. Our interpretation is that research on financial distress prediction models using corporate governance indicators has provided empirical evidence.

Financial Distress Prediction: The Ownership Structure and Management Agency Cost

This research aimed to predict the financial distress through ownership structure and management agency cost. The ownership structure was tested by managerial ownership and institutional ownership. Meanwhile, the management agency cost was tested by administrative cost ratio. The population of this research were all companies listed in Indonesia Sharia Stock Index year 2016-2018 by using purposive sampling technique. Based on the criteria that have been determined, the total samples were 129 companies. The analysis of the data in this research used logistic regression analysis. The results showed that institutional ownership has a significant negative effect towards the financial distress. Despite, the managerial ownership and management agency cost have a insignificant negative effect toward the financial distress.

Corporate financial distress prediction based on controlling shareholder’s equity pledge

ABSTRACT In the past literature, the prediction model of financial distress mainly used the information of corporate finance and corporate governance, and less considered the predictive effect of controlling shareholder information. Based on the sample of A-share listed companies in China from 2014 to 2019, this paper empirically investigates the incremental effect of controlling shareholder’s equity pledge on improving the accuracy of financial distress prediction. It is found that the information of controlling shareholder’s equity pledge can significantly improve the accuracy of financial distress prediction. After changing the definition of the explained variable and the data source, the conclusion remains robust. This paper verifies the role of controlling shareholder’s equity pledge in predicting financial distress, extends the selection of financial distress prediction model variables to the controlling shareholder level, and enriches the research on the economic consequences of equity pledge.

Dynamic financial distress prediction based on class-imbalanced data batches

This study proposes two approaches for dynamic financial distress prediction (FDP) based on class-imbalanced data batches by considering both concept drift and class imbalance. One is based on sliding time window and synthetic minority over-sampling technique (SMOTE) and the other is based on sliding time window and majority class partition. Support vector machine, multiple discriminant analysis (MDA) and logistic regression are used as base classifiers in the experiments on a real-world dataset. The results indicate that the two approaches perform better than the pure dynamic FDP (DFDP) models without class imbalance processing and the static FDP models either with or without class imbalance processing.

Analisis Finansial Distress Untuk Memprediksi Risiko Kebangkrutan Perusahaan Sub Sektor Energi

This study aims to determine and analyze the financial performance of the energy sub-sector using the Almant Z-Score method. This research is a quantitative descriptive study. The population in this study are energy sub-sector companies listed on the Indonesia Stock Exchange 2015-2019. The sample in this study amounted to 4 energy sub-sector companies, namely PT. Perusahaan Gas Negara, PT. Leyand International, PT. Rukun Raharja, and PT. Mitra Energi Persada. The sampling technique in this study was using purposive sampling technique. The results of the financial distress predictions for the energy sub-sector companies, amounting to 4 publicly traded companies during the 2015 to 2019 research period using the Altman Z-Score method. This results in three companies in financial distress and one company in a safe area.
 Keywords: Finansial Distress, Bankrupty, Energy Sub-sector

On the determinants and prediction of corporate financial distress in India

PurposeThe main aim of the study is to identify some critical microeconomic determinants of financial distress and to design a parsimonious distress prediction model for an emerging economy like India. In doing so, the authors also attempt to compare the forecasting accuracy of alternative distress prediction techniques.Design/methodology/approachIn this study, the authors use two alternatives accounting information-based definitions of financial distress to construct a measure of financial distress. The authors then use the binomial logit model and two other popular machine learning–based models, namely artificial neural network and support vector machine, to compare the distress prediction accuracy rate of these alternative techniques for the Indian corporate sector.FindingsThe study’s empirical results suggest that five financial ratios, namely return on capital employed, cash flows to total liability, asset turnover ratio, fixed assets to total assets, debt to equity ratio and a measure of firm size (log total assets), play a highly significant role in distress prediction. The study’s findings suggest that machine learning-based models, namely support vector machine (SVM) and artificial neural network (ANN), are superior in terms of their prediction accuracy compared to the simple binomial logit model. Results also suggest that one-year-ahead forecasts are relatively better than the two-year-ahead forecasts.Practical implicationsThe findings of the study have some important practical implications for creditors, policymakers, regulators and other stakeholders. First, rather than monitoring and collecting information on a list of predictor variables, only six most important accounting ratios may be monitored to track the transition of a healthy firm into financial distress. Second, our six-factor model can be used to devise a sound early warning system for corporate financial distress. Three, machine learning–based distress prediction models have prediction accuracy superiority over the commonly used time series model in the available literature for distress prediction involving a binary dependent variable.Originality/valueThis study is one of the first comprehensive attempts to investigate and design a parsimonious distress prediction model for the emerging Indian economy which is currently facing high levels of corporate financial distress. Unlike the previous studies, the authors use two different accounting information-based measures of financial distress in order to identify an effective way of measuring financial distress. Some of the determinants of financial distress identified in this study are different from the popular distress prediction models used in the literature. Our distress prediction model can be useful for the other emerging markets for distress prediction.

Experimental Comparison of Financial Distress Prediction Models Using Imbalanced data sets

From machine learning perspective, the problem of predicting financial distress is challenging because the distribution of the classes is extremely imbalanced. The goal of this study was comparing the performance of financial distress prediction models for the imbalanced data sets with different proportions. In this study, the data of the previous year before financial distress was used for 760 company year for the time period of 2007-2017. Besides using traditional classifications such as logistic regression, linear discriminant analysis, artificial neural network, and the classification models of least square support vector machine with four kernel functions, random forest and the Knn algorithm, the measures of the area under the curve and Friedman and Nemenyi tests were also utilized to determine the average rank and the difference significance of the Auc of the models. For selecting the models´ optimal parameters, the combined method of grid search optimization and cross validation was used. The results of this experimental study showed that for the balanced and imbalanced datasets with lower proportions, the best performance was for the random forest. For more imbalanced datasets, the best performance belonged to the least square support vector machine with sigmoid, radial, and linear kernel functions; performance of Knn algorithm had no significant difference from the other models and the performance of the artificial neural network was average or appropriate. Also, the performances of the linear logistic regression and linear discriminant analysis were weaker than other nonlinear models.

Predicting Firms’ Financial Distress: An Empirical Analysis Using the F-Score Model

Financial performance of firms is very important to bankers, shareholders, potential investors, and creditors. The inability of firms to meet their liabilities will affect all its stakeholders and will result in negative consequences in the wider economy. The objective of the study is to explore the applicability of a distress prediction model which uses the F-Score and its components to identify firms which are at high risk of going into default. The study incorporates a prediction model and vast literature to address the research questions. The sample of the study is collected from publicly listed firms of the United States. In total, 81 financially distressed firms wereextracted from the UCLA-LoPucki Bankruptcy Research Database during 2009–2017. This study found that the relationship of the F-Score and probability of firms going into financial distress is significant. This study also demonstrated that firms which are at risk of distress tend to record a negative cash flow from operations (CFO) and showed a greater decline in return on assets (ROA) in the year prior to default. This study extends the existing literature by supporting a model which has not been widely used in the area of financial distress predictions.

Financial Information Fraudulence and Financial Distress: Evidence from Singapore

We investigate if Singapore listed companies engaged in financial information fraud during financial distressed after two years of US subprime mortgage crisis. We also investigate the impact of financial information fraudulence in bankruptcy prediction and misclassification errors. This study used consumer product companies listed on the main board and the timeframe is from 2011 till 2015. The Altman Z score indicates that 55 out of 110 companies are financially distressed. Meanwhile, the M score shows that 49 out of 351 observations are engaged in financial information fraudulence. However, these results are relatively low because the samples are taken from the main board and fraudulence in their financial statements might be done in lower magnitude in order to avoid sanctions by the Security Exchange Commission. Logistic regression was used to measure the predicting accuracy. The result of the overall accuracy percentage slightly improved by 2.4 after eliminating fraudulent companies. The confusion matrix result i.e. before and after the removal of financial information fraudulent companies, the misclassification errors for type one has improved by 1.7 percent and 3 percent for type two. This result met objective three, as the upward bias of financial information fraudulence is one of the explanations for the decline in financial distress prediction. This research will be beneficial to governments, monitoring agencies, and all involved in the insolvency process.

Analisis Rasio Kebangkrutan Perusahaan pada Masa Pandemi Covid-19

This research was conducted with the aim of knowing financial distress and bankruptcy predictions in the hotel, restaurant & tourism sub-sector companies during the pandemic of Covid-19 in 2020 using the Altman Z”-Score ratio analysis. The population of this research is all service and hospitality companies listed on the Indonesian Stock Exchange before 2018, the sample was selected based on predetermined purposive sampling criteria. From a sample of 25 companies, using the Z”-Score ratio, it is predicted that there was an increase in the category of bankruptcy (red zone) of 3 companies only within a quarter during the crisis. Companies that were initially included in the safe category as well as having a low risk of bankruptcy (healthy) also decreased, adding 2 companies to the vulnerable category (gray area). This indicates that there are difficulties both financially and in liquidity as well as a decrease in the company's ability to grow, which may lead to bankruptcy.
 Keywords: Financial Distress, Altman Z”-Score, Bankruptcy, Covid-19.

Impact Of Financial Information Fraudulence To Financial Distress In Malaysia.

We investigate if Malaysian listed companies engaged in financial information fraud during financial distressed after two years of US subprime mortgage crisis.We also investigate the impact of financial information fraudulence in bankruptcy prediction and misclassification errors. This study used consumer product companies listed on the main board and the timeframe is from 2011 till 2015. The Altman Z score indicates that 37 out of 133 Malaysian consumer product companies are financially distressed. Meanwhile, the M score shows that 28 out of 224observations are engaged in financial information fraudulence. However, these results are relatively low because the samples are taken from the main board and fraudulence in their financial statements might be done in lower magnitude in order to avoid sanctions by the Security Exchange Commission. Logistic regression was used to measure the predicting accuracy. The result of the overall accuracy percentage slightly improved by 0.9 after eliminating fraudulent companies. The confusion matrix result i.e. before and after the removal of financial information fraudulent companies, the misclassification errors especially type one has improved. This finding satisfied objective three, whereby one of the reasons for the deterioration in financial distress prediction is due to the upward bias of financial information fraudulence.Governments, monitoring bodies, and all those involved in an insolvency process would benefit from this study.

Financial Distress Prediction of Airlines Companies

The purpose of this study is to compare three financial failure models: the Altman Z-Score Model, the Springate Model, and the Zmijewski Model, in terms of predicting financial difficulty among airlines in Asia and the Middle East. Based on the results of this study, it is proven by the result of the analysis done for Airlines in Asia and the Middle East that all the three models have predicted that these companies are in financial distress. But, the Altman Z-Score model is the most significant model to forecast financial distress. Although the models employ different ratios in their analyses, this study demonstrates that there is a substantial difference in the analysis of these three models. Another independent T-test demonstrates that the Altman Z-Score Model and the Zmijewski Model, as well as the Springate Model and the Zmijewski Model, have substantial differences. The study employed a descriptive and comparative analysis method, and this model was created to compare the independent variables. The Altman Z-Score model is the most significant model for predicting the financial failure of enterprises, according to the descriptive analysis in this study. While the comparison findings show a large difference between the Altman Z-Score Model and the Zmijewski Model, there is also a significant difference between the Springate Model and the Zmijewski Model. The Altman Z-Score Model and the Springate Model, on the other hand, imply that there is no significant model.