Financial Distress Early Warning Research Articles

Financial Distress Early Warning for Chinese Enterprises from a Systemic Risk Perspective: Based on the Adaptive Weighted XGBoost-Bagging Model

This paper aims to tackle the problem of low accuracy in predicting financial distress in Chinese industrial enterprises, attributable to data imbalance and insufficient information. It utilizes annual data on systemic risk indicators and financial metrics of Chinese industrial enterprises listed on the China’s A-share market between 2008 and 2022 to construct the adaptive weighted XGBoost-Bagging model for corporate financial distress prediction. Empirical findings demonstrate that systemic risk indicators possess predictive potential independent of traditional financial information, rendering them valuable non-financial early warning indicators for China’s industrial sector; moreover, they help to enhance the predictive accuracy of various comparative models. The adaptive weighted XGBoost-Bagging model incorporating systemic risk indicators effectively addresses challenges arising from data imbalance and information scarcity, significantly improving the accuracy of financial distress prediction in Chinese industrial enterprises under the 2015 Chinese stock market crash, the Sino-US trade friction, and the COVID-19 epidemic; as such, it can be used as an efficient risk early warning tool for China’s industrial sector.

Design and Application of a Financial Distress Early Warning Model Based on Data Reasoning and Pattern Recognition

Since the 1990s, emerging market financial crises have occurred frequently, causing huge damage to the real economy, and if we cannot find effective means of early warning and prevention of financial crises, the entire international economy and society will bear the high costs of crisis management. Difference nonparametric test and Spearman nonparametric correlation analysis were carried out with cash flow financial data, and 14 financial indicators with strong discriminant ability were selected from 28 financial indicators as the input variables of the model. Due to the limitations of traditional statistical methods, a BP neural network financial distress early warning model is established. Finally, a particle swarm optimization BP neural network financial early warning model is established for the shortcomings of the BP network. These 14 indicators can have strong information timeliness. The prediction accuracy rates of the two early warning models for the test samples are 80% and 85%, respectively. The empirical results show that the two models have good prediction effects. The prediction effect of the swarm optimization BP neural network model is better than that of the BP neural network model. Therefore, the particle swarm optimization BP neural network model proposed in this paper is suitable for solving the problem of discrimination and prediction of the financial distress of enterprises. The company’s financial distress early warning has good application prospects and application value. Therefore, it has very important research significance for the early warning of the corporate financial crisis.

A new multivariate approach for assessing corporate financial risk using balance sheets

Several indicators and univariate ratios can be used to measure the soundness of firms as reflected in their balance sheets (leverage, profitability, liquidity ratio, etc.). However, each indicator alone cannot measure a firm's overall financial risk or financial distress level. In this study, we measure the financial strength of the real sector firms listed on the Borsa Istanbul (BIST) by producing a composite index score that combines several different corporate finance ratios. In the first section, we conduct a multiple discriminant analysis of the variables used in Altman's z-score (1968), which is the most prevalent composite index used to measure firms' financial risk in the literature. In the second section, we introduce a new index, called the multivariate firm assessment (MFA) score, which uses the ratios that best explain the characteristics of companies listed on the BIST. The Tailored version of the Altman z-score and our new index have predictive power of around 90 percent. Furthermore, the MFA score reflects the impact of macroeconomic developments on firms' balance sheets and thus serves as an early warning of financial distress for Turkish firms. Our analyses using the MFA score suggest that non-exporting firms and firms with an open foreign exchange position have weaker balance sheets.

Bankaların Finansal Başarısızlıklarının İncelenmesinde Makine Öğrenme Tekniklerinin Karşılaştırılması

Analyzing banks’ financial distress has gained great importance due to their importance in national economy and caused sociological and economic results. Support Vector Machines SVM and Neural Networks NN , known as machine learning methods, are applied for classifying banks as an early warning of financial distress. A case study which is taking thirty private equity commercial banks’ five year data and financial ratios, is carried out. As a result SVM obtains better classification ratio than NNs

Study on Early Warning of Enterprise Financial Distress — Based on Partial Least-squares Logistic Regression

Establishment of an effective early warning system can make the company operators make relevant decisions as soon as possible when finding the crisis, improve the operating results and financial condition of enterprise, and can also make investors avoid or reduce investment losses. This paper applies the partial least-squares logistic regression model for the analysis on early warning of enterprise financial distress in consideration of quite sensitive characteristics of common logistic model for the multicollinearity. The data of real estate industry listed companies in China are used to compare and analyze the early warning of financial distress by using the logistic model and the partial least-squares logistic model, respectively. The study results show that compared with the common logistic regression model, the applicability of partial least-squares logistic model is stronger due to its eliminating multicollinearity problem among various early warning indicators.

Using Accounting Ratios in Predicting Financial Distress: An Empirical Investigation in the Vietnam Stock Market

Financial distress prediction is an important and practical research topic for many stakeholders and has attracted extensive studies over the past decades. This paper investigates the challenging issue of financial distress in Vietnam by distinguishing “healthy” companies from “financially distressed” companies using a data sample of firms listed on the Ho Chi Minh City Stock Exchange. Employing the logistic regression model to predict financial distress with a unique data set, we characterize the determinants of financial distress in terms of firm accounting and financial ratios over the period from 2007 to 2012. The results indicate that financial ratios can be employed as an early warning of financial distress as financial ratios are significantly correlated with the probability of firm financial distress.

Defensive financial decisions support for retailers in Greek pharmaceutical industry

In this paper, we present a decisions support solution designed for Greek pharmacies comprising a cash flow management system for early warning of financial distress and a financial advisor based on a neural network. The cash flow monitoring system integrates accounting elements with real time transactions and a predictive linear regression model while the decision support module is developed with the help of a neural network. For any given business unit the system associates accounting entries with information about credit times to reflect the precise instants of cash flows and using inflows/outflows equations monthly, eventually build its liquidity curve and cash flow balance over time. Alongside, a linear regression module is introduced to estimate future cash reserves based on past profitability ratios. Lastly, combining the power of artificial neural networks with expertise in this sector of pharmaceutical business, the financial decision support tool focuses on the retailers that face financial difficulties and suggests alternative solutions for escaping from distress and insolvency. The model has an ambitious and useful purpose, to inform and consult the owners of the business units and other members of the pharmaceutical chain, thus reduce financial risk for the chain.

Research on Financial Distress Early-warning of Listed Companies Based on GA-SVM

Based on financial management and enterprises of early-warning theory,a financial distress early-warning model is constructed by using GA-SVM.First,taking listed companies appearing in Shanghai Stock Exchange and Shenzhen Stock Exchange in 2007~2009 as sample books,defining ST listed companies which have abnormity of finance status as signature of the listed company′s financial crisis,and the data in the financial statements known to the public is used as the input feature vector,genetic algorithm and support vector machine was combined.Then the financial distress early-warning model is established by an empirical research method.

Aggregating multiple classification results using Choquet integral for financial distress early warning

Financial distress prediction methods based on combination classifier become a rising trend in this field. This paper applies Choquet integral to ensemble single classifiers and proposes a Choquet integral-based combination classifier for financial distress early warning. Also, as the conditions between training and pattern recognition cannot be completely consistent, so this paper proposes an adaptive fuzzy measure by using the dynamic information in the single classifier pattern recognition results which is more reasonable than the static prior fuzzy density. Finally, a comparative analysis based on Chinese listed companies’ real data is conducted to verify prediction accuracy and stability of the combination classifier. The experiment results indicate that financial distress prediction using Choquet integral-based combination classifier has higher average accuracy and stability than single classifiers.

Financial distress early warning based on group decision making

Financial distress early warning is important for business bankruptcy prevention, and various quantitative prediction methods based on financial ratios have been proposed. However, little attention has been paid to the important role of experts’ experiential knowledge and non-financial information. From this point of view, the article puts forward a group decision-making approach based on experts’ knowledge and all kinds of financial or non-financial information to diagnose business financial distress. Based on the risk factors of enterprise financial distress, a qualitative attribute set and its scoring criteria are designed. A method integrating linguistic label and interval value is adopted for decision makers to express their preference on attributes, and a multi-expert negotiation mechanism is designed for weighting attributes. Diagnosis on business financial distress is made through the grey evaluation method, which also tries to find out the potential risks that may cause financial distress. Case study of a real world company is carried out to validate the proposed financial distress early warning method based on group decision making.