Financial Distress Prediction Research Articles

Pengaruh Likuiditas, Profitabilitas, Leverage, dan Aktivitas dalam Memprediksi Financial Distress pada Perusahaan Subsektor Retail Trade yang Terdaftar di BEI

The purpose of this study is to analyze how financial ratios can affect the prediction of financial distress or financial distress in retail trade sub-sector companies listed on the Indonesia Stock Exchange. In this study, liquidity is represented by the Current Ratio, profitability is proxied by the rate of return on assets (ROA). Leverage is represented by the Debt to Equity Ratio, and the activity ratio is represented by total asset turnover (TATO). The dependent variable used is financial distress with the Modified Altman Z-Score Model as an indicator. The research population is all retail trade sub-sector companies listed on the IDX in 2018-2020. Sampling using purposive sampling technique and obtained 25 companies that fit the criteria with a 3 year research period. Logistic regression analysis was used as a data analysis method. From the results of logistic regression analysis, it shows that liquidity and leverage indicators have a significant effect in estimating financial distress or financial distress of retail trade sub-sector companies. Meanwhile, indicators of profitability and activity cannot influence the forecast of financial distress in retail trade sub-sector companies.

Using Glow-worm algorithm to predict companies’ financial distress

One important research issue in the risk management area is to predict the financial distress of companies. This case has received great attention from banks, companies, managers, and investors. Although there are many studies on this case, the hybrid models (mixed feature selection and classifier models) have been used by researchers in recent years. The main objective of this study is to propose a high-performance predictive model and compare its results with other models that are commonly used for financial distress prediction. To do this, the Glowworm optimization algorithm-based hybrid neural network model was employed. Moreover, the neural network and logistic regression model, which is one of the statistical classifier models were used. The results indicated that the glowworm optimization algorithm (also known as firefly optimization algorithm)-based hybrid neural network model had higher performance compared to the neural network and logistic regression models.

Prediction of Financial Distress With Financial Ratio Analysis

This study aims to prove the effect of liquidity, profitability and leverage on financial distress. The study was conducted in mining companies listed on the Indonesia Stock Exchange. The population of this study was the financial statements of mining companies listed on the Indonesia Stock Exchange in 2014-2019. The sample was determined using purposive sampling using certain criteria. The test was carried out using multiple regression analysis with SPSS software assistance. The results of this study indicate that the liquidity variable has no effect on financial distress, while profitability and leverage have an effect on financial distress.

The Accuracy of Financial Distress Prediction During the COVID-19 Pandemic on Health Sub Sector Companies

During the recent COVID-19 pandemic, most countries are in a phase of slowing economic growth that causes long-term financial distress and leads to bankruptcy. This paper describes the accuracy of financial distress prediction of the healthcare sub-sector companies using the Altman Modified Z-Score, Springate, and Zmijewski methods. The level of accuracy is determined based on the suitability of the calculation results of the three models with the company’s bankruptcy data published on the Indonesia Stock Exchange and strengthened by the analysis based on the calculation of the type error I and II. Based on the level of accuracy and error types I and II, the Springate is the most accurate method in analyzing the financial distress prediction of the healthcare sub-sector companies with an accuracy rate of 91.4275. Comparing financial performance before and after the COVID-19 pandemic, the mean difference test shows that there is no significant difference in financial performance before and after the COVID-19 pandemic.

FINANCIAL DISTRESS PREDICTION OF FINANCIAL SECTOR SERVICE COMPANIES ON INDONESIAN STOCK EXCHANGE USING COX PROPORTIONAL HAZARD

A company that cannot compete with its competitors is likely to experience financial difficulties or commonly referred to as financial distress. Financial distress is a stage of a decline in the company's financial condition or a situation of financial difficulty that occurred before the company went bankrupt. This study aims to determine the factors that can predict a company experiencing financial distress. The factors suspected in this study include leverage, profitability, company size, free cash flow and sales growth. The method used is the Cox Proportional Hazard model. The research data is data on financial sector service companies listed on the Indonesia Stock Exchange (IDX) for 5 years of observation, namely from 2016 to 2020. Based on the results of the analysis of financial distress predictions using the Cox Proportional Hazard model, it is found that the factors that have a significant effect on predicting companies experiencing financial distress are: financial distress, namely profitability and company size.

Financial Distress Prediction with Stacking Ensemble Learning

Previous studies have used financial ratios extensively to build their predictive model of financial distress. The Altman ratio is the most often used to predict, especially in academic studies. However, the Altman ratio is highly dependent on the validity of the data in financial statements, so other variables are needed to assess the possibility of manipulation of financial statements. None of the previous studies combined the five Altman Ratios with the Beneish M-Score. We use Stacking Ensemble Learning to classify crisis companies and perform a comprehensive analysis. This insight helps the investment public make lending decisions by mixing all the financial indicator information and assessing it carefully based on long-term and short-term conditions and possible manipulation of financial statements.

Prediction of Financial Distress on Properties and Real Estate in Indonesia

Financial distress is a stage of decline in financial conditions which occurred before the onset of bankruptcy. This study was conducted with the aim of knowing the effect of Operating Capacity, Sales Growth, and Debt to Equity Ratio in predicting Financial Distress in property and real estate companies on Indonesian Stock Exchange in 2018-2020. The research method which is used is quantitative research. The sampling technique used purposive sampling method, namely the selection of samples with criteria that have been determined by the researcher. The data used is secondary data in the form of financial statements of property and real estate companies listed on the Indonesian Stock Exchange for the 2018-2020 period. The companies selected as the research sample were 38 companies so that the total observations were 114 observations. Financial distress is included as dummy variable, and it is categorized as 1 (one) for companies which experiencing financial distress and 0 (zero) for companies which is not experiencing in financial distress. The result of this study shows that operating capacity sales growth and debt to equity ratio do not affect the company’s probability of experiencing financial distress.

Prediction of Financial Distress on Properties and Real Estate in Indonesia

Prediction of Financial Distress on Properties and Real Estate in Indonesia

Analisis Rasio Keuangan untuk Memprediksi Financial Distress pada PT Austen Jaya di Bandar Lampung

The purpose of this study was to predict financial distress using financial ratio analysis at PT AUSTEN JAYA in Bandar Lampung. The ratio analysis used is the current ratio, return on assets, and leverage. The research data used was obtained directly through interviews and equipped with financial reports for four years from 2018 to 2021. The research method used is descriptive method on the results of financial ratio analysis. Based on the results of the study, it was found that returns on assets decreased continuously from 10.05% in 2018 to 9.04% in 2019, and fell again in 2020 by 6.15%, and there was a slight increase in 2021 to 6.25%. This situation describes the prediction of the occurrence of financial distress in accordance with the results of previous studies that profitability affects the prediction of financial distress. Another thing that supports is the curent ratio which continues to decline on the other hand, the leverage continues to increase. This situation shows the existence of financial difficulties indicated by the company's inability to earn profits, difficulty paying obligations, and the company's financing is increasingly dependent on outside parties. It is hoped that the company will immediately take steps to improve its finances, especially be careful with debt and secondly, do efficiency seriously.

Prediction of corporate financial distress based on digital signal processing and multiple regression analysis

Abstract In order to reduce the default rate of corporate bond market, the author proposes to use digital signal processing and multiple regression analysis to study the prediction system of financial distressed companies. First, design the research method, Logistic regression model is the most commonly used multivariate statistical method when modeling binary dependent variables, it can solve the problem of nonlinear classification, it has no specific requirements for the distribution of variables, and the accuracy of judgment is high. The author selects 32 financial ratios from the perspectives of solvency, operating ability, profitability, development ability, per share index, and risk level. Taking special treatment (ST) due to abnormal financial status as a sign of financial distress in listed companies, when selecting samples, the matching principle is adopted to select non-ST companies as matching samples. Two methods of logistic regression and support vector machine are used for empirical testing, and both in-sample testing and out-of-sample prediction are performed. The results show that when using the logistic regression method, the propensity to default indicator (TTD) reflected in the text content, it can indeed improve the out-of-sample prediction accuracy of the financial distress prediction model, and it is consistent with the in-sample test, this is mainly reflected in the reduction of the first type of error, that is, the probability of misjudging a financially distressed company as a normal company. Changes in the proportions have little effect on the relative importance of financial ratio variables when modeling with support vector machines, the propensity to default indicator (TTD) entered the top ten important variables in both ratios, and ranked fourth among all indicators when the ratio was 1:2, importance has increased significantly. From this it can be seen that, when using support vector machine to build a financial distress prediction model, the propensity to default indicator (TTD) has played an important role. In the case of using the support vector machine method, adding the default tendency indicator (TTD) reflected by the text information can also improve the accuracy of the financial distress prediction model.

Disclosure delay and financial distress prediction: based on Chinese annual reports disclosure system

ABSTRACT Based on the data of Shanghai and Shenzhen A-share listed companies from 2009 to 2018, we explore the impact of delayed disclosure of annual report on the accuracy of financial distress prediction. We find that the information of disclosure delay can significantly improve the accuracy of financial distress prediction. After using alternative measurement of the disclosure delay and considering the endogeneity problem, our conclusions are robust. Our study provides reference for regulatory authorities to standardize the companies’ disclosure behaviours and prevent and resolve their major risks.

Cost-Sensitive Metaheuristic Optimization-Based Neural Network with Ensemble Learning for Financial Distress Prediction

Financial distress prediction is crucial in the financial domain because of its implications for banks, businesses, and corporations. Serious financial losses may occur because of poor financial distress prediction. As a result, significant efforts have been made to develop prediction models that can assist decision-makers to anticipate events before they occur and avoid bankruptcy, thereby helping to improve the quality of such tasks. Because of the usual highly imbalanced distribution of data, financial distress prediction is a challenging task. Hence, a wide range of methods and algorithms have been developed over recent decades to address the classification of imbalanced datasets. Metaheuristic optimization-based artificial neural networks have shown exciting results in a variety of applications, as well as classification problems. However, less consideration has been paid to using a cost sensitivity fitness function in metaheuristic optimization-based artificial neural networks to solve the financial distress prediction problem. In this work, we propose ENS_PSONNcost and ENS_CSONNcost: metaheuristic optimization-based artificial neural networks that utilize a particle swarm optimizer and a competitive swarm optimizer and five cost sensitivity fitness functions as the base learners in a majority voting ensemble learning paradigm. Three extremely imbalanced datasets from Spanish, Taiwanese, and Polish companies were considered to avoid dataset bias. The results showed significant improvements in the g-mean (the geometric mean of sensitivity and specificity) metric and the F1 score (the harmonic mean of precision and sensitivity) while maintaining adequately high accuracy.

Textile and Garment Sector Financial Distress and Its Prediction: A Systematic Indonesian Literature Review

Aims: The purpose of this study is to examine the conceptualization of financial distress research in the textile and apparel industries, particularly in terms of research scope and methodology. Furthermore, this article attempts to systematically analyze the network formed by these literatures.
 Methodology: In this study, a qualitative approach was used through the literature review method, with 41 specific articles about financial distress in the textile and garment sector serving as the research corpus and drawn from the Litmaps database. To interpret and describe the frequency patterns and relationships visualized using RStudio and Gephi devices, text mining, network analysis, and content analysis were used.
 Results: This study discovers that a frequently discussed issue is the influence of financial variables, both dependent and independent, on the prediction of financial distress or vice versa, using various quantitative approaches and models of financial distress. This claim is supported by the findings of a systematic analysis, which reveals a positive correlation between global cloud output and network analysis.
 Implication/Applications: The corpus aspect of this research is limited, and the research scope is limited to the Indonesian context. Future research with broader literature sources and different types of company sectors is highly anticipated. This literature review can provide a comprehensive framework for researchers and practitioners who are interested in cases of financial distress.
 The Originality of the Study: Furthermore, this is a recent study that conducts a systematic review of the literature on financial distress in Indonesian textile and garment companies.

An explainable artificial intelligence approach for financial distress prediction

External stakeholders require accurate and explainable financial distress prediction (FDP) models. Complex machine learning algorithms offer high accuracy, but most of them lack explanatory power, resulting in external stakeholders being cautious in adopting them. Therefore, an explainable artificial intelligence approach including a whole process ensemble method and an explainable frame for FDP is here proposed. The ensemble algorithm from feature selection to predictor construction can achieve high accuracy according to the actual case, and the interpretation framework can meet the needs of external users by generating local explanations and global explanations. First, a two-stage scheme integrated with a filter and wrapper technique is designed for feature selection. Second, multiple ensemble models are explored and they are evaluated according to the actual case. Finally, Shapley additive explanations, counterfactual explanations and partial dependence plots are employed to enhance model interpretability. Taking financial data of Chinese listed companies from 2007 to 2020 as a dataset, the highest AUC is ensured by LightGBM with a value of 0.92. Local explanations help individual enterprises identify the key features which lead to their financial distress, and counterfactual explanations are produced to provide improvement strategies. By analyzing the features importance and the impact of feature interaction on the results, global explanations can improve the transparency and credibility of ‘black box’ models.

Adequateness of Applying Altman's Z Score Model for Prediction of Potential Financial Distress in NBFIs of India: A Case Study of Reliance Capital Ltd.

Non-banking Financial Institutions (NBFIs) form an important part in the Indian Financial system. It has been intermediating a growing share of the resource flows to the commercial sector. NBFIs add-on the role of the banking sector in meeting the increasing financial needs of the corporate sector. NBFIs mainly belongs to private sector institutions that are focus in meeting the credit needs and a variety of financial services like financing of physical assets, infrastructure loans and commercial vehicles. The Reserve Bank of India's regulatory parameters are applicable to companies conducting non-banking financial activity, such as investment, lending or deposit acceptance as their principal business.

Financial distress prediction using integrated Z-score and multilayer perceptron neural networks

The COVID-19 pandemic led to a great deal of financial uncertainty in the stock market. An initial drop in March 2020 was followed by unexpected rapid growth over 2021. Therefore, financial risk forecasting continues to be a central issue in financial planning, dealing with new types of uncertainty. This paper presents a stock market forecasting model combining a multi-layer perceptron artificial neural network (MLP-ANN) with the traditional Altman Z-Score model. The contribution of the paper is presentation of a new hybrid enterprise crisis warning model combining Z-score and MLP-ANN models. The new hybrid default prediction model is demonstrated using Chinese data. The results of empirical analysis show that the average correct classification rate of thew hybrid neural network model (99.40%) is higher than that of the Altman Z-score model (86.54%) and of the pure neural network method (98.26%). Our model can provide early warning signals of a company's deteriorating financial situation to managers and other related personnel, investors and creditors, government regulators, financial institutions and analysts and others so that they can take timely measures to avoid losses.

Deep Learning-Based Model for Financial Distress Prediction.

Predicting bankruptcies and assessing credit risk are two of the most pressing issues in finance. Therefore, financial distress prediction and credit scoring remain hot research topics in the finance sector. Earlier studies have focused on the design of statistical approaches and machine learning models to predict a company's financial distress. In this study, an adaptive whale optimization algorithm with deep learning (AWOA-DL) technique is used to create a new financial distress prediction model. The goal of the AWOA-DL approach is to determine whether a company is experiencing financial distress or not. A deep neural network (DNN) model called multilayer perceptron based predictive and AWOA-based hyperparameter tuning processes are used in the AWOA-DL method. Primarily, the DNN model receives the financial data as input and predicts financial distress. In addition, the AWOA is applied to tune the DNN model's hyperparameters, thereby raising the predictive outcome. The proposed model is applied in three stages: preprocessing, hyperparameter tuning using AWOA, and the prediction phase. A comprehensive simulation took place on four datasets, and the results pointed out the supremacy of the AWOA-DL method over other compared techniques by achieving an average accuracy of 95.8%, where the average accuracy equals 93.8%, 89.6%, 84.5%, and 78.2% for compared models.

Financial Distress Prediction of Cooperative Financial Institutions—Evidence for Taiwan Credit Unions

In response to relatively little evidence on the determinants of the financial distress in cooperative financial institutions (e.g., Credit Unions), this paper proposes a distress indicator of Merton Distance to default (Merton DD), which was constructed with a z-score, possessed improved predictive capability, but reducing equity volatility. This model possesses the advantages of both hazard and modified Merton DD model, which could timely reflect market volatility and predict when distress would occur. As a demonstration, we applied this model to forecast the financial distress of credit unions in Taiwan. The results can provide more information to researchers.

A novel method for financial distress prediction based on sparse neural networks with $$L_{1/2}$$ regularization

Corporate financial distress is related to the interests of the enterprise and stakeholders. Therefore, its accurate prediction is of great significance to avoid huge losses from them. Despite significant effort and progress in this field, the existing prediction methods are either limited by the number of input variables or restricted to those financial predictors. To alleviate those issues, both financial variables and non-financial variables are screened out from the existing accounting and finance theory to use as financial distress predictors. In addition, a novel method for financial distress prediction (FDP) based on sparse neural networks is proposed, namely FDP-SNN, in which the weight of the hidden layer is constrained with L_{1/2} regularization to achieve the sparsity, so as to select relevant and important predictors, improving the predicted accuracy. It also provides support for the interpretability of the model. The results show that non-financial variables, such as investor protection and governance structure, play a key role in financial distress prediction than those financial ones, especially when the forecast period grows longer. By comparing those classic models proposed by predominant researchers in accounting and finance, the proposed model outperforms in terms of accuracy, precision, and AUC performance.

Financial Distress Prediction for Digital Economy Firms: Based on PCA-Logistic

Financial distress prediction is important for risk prevention and control of digital economy firms, as well as going concern guarantee. This paper takes 100 Chinese A-share listed digital economy firms from 2017 and 2021 as samples, obtains financial indicators by combining the characteristics of digital economy firms, the first three periods of financial distress are systematically modeled employs Logistic regression, while we use the Principal Component Analysis method to deal with the problem of multicollinearity. The results show that the profitability factor has the greatest contribution to the predictive role; the closer to the year in which the financial distress occurred, the higher the prediction accuracy rate. Finally this model achieves 86% prediction accuracy. The successful modelling provides a basis for information users to determine the financial distress of firms accurately and prospectively in the digital economy.