Financial Distress Forecasting Research Articles

Deep Learning Based on Hierarchical Self-Attention for Finance Distress Prediction Incorporating Text.

To detect comprehensive clues and provide more accurate forecasting in the early stage of financial distress, in addition to financial indicators, digitalization of lengthy but indispensable textual disclosure, such as Management Discussion and Analysis (MD&A), has been emphasized by researchers. However, most studies divide the long text into words and count words to treat the text as word count vectors, bringing massive invalid information but ignoring meaningful contexts. Aiming to efficiently represent the text of large size, an end-to-end neural networks model based on hierarchical self-attention is proposed in this study after the state-of-the-art pretrained model is introduced for text embedding including contexts. The proposed model has two notable characteristics. First, the hierarchical self-attention only affords the essential content with high weights in word-level and sentence-level and automatically neglects lots of information that has no business with risk prediction, which is suitable for extracting effective parts of the large-scale text. Second, after fine-tuning, the word embedding adapts the specific contexts of samples and conveys the original text expression more accurately without excessive manual operations. Experiments confirm that the addition of text improves the accuracy of financial distress forecasting and the proposed model outperforms benchmark models better at AUC and F2-score. For visualization, the elements in the weight matrix of hierarchical self-attention act as scalers to estimate the importance of each word and sentence. In this way, the “red-flag” statement that implies financial risk is figured out and highlighted in the original text, providing effective references for decision-makers.

Is COVID-19 Decaying the Financial Health of the Aviation Industry in India

Forecasting of financial distress helps not only the companies but also their stakeholders, employees, creditors, stock market agents, etc. As COVID-19 has made a humungous impact on the Aviation industry of the country already we have witnessed a decline in the industry but since the country got hit by the co vid-19 the effect got even worse. This has not only caused loss of economy but also has made it very tough for people to earn their livelihood as they have lost their jobs. Economy of the country has been greatly impacted as the Airline industry is the one of the major sources of revenue. This research paper aims to analyze the current and the future situation of the aviation industry with reference to COVID-19.

Is Covid-19 Decaying the Financial Health of the Aviation Industry in India

Forecasting of financial distress helps not only the companies but also their stakeholders, employees, creditors, stock market agents, etc. As COVID-19 has made a humungous impact on the Aviation industry of the country already we have witnessed a decline in the industry but since the country got hit by the co vid-19 the effect got even worse. This has not only caused loss of economy but also has made it very tough for people to earn their livelihood as they have lost their jobs. Economy of the country has been greatly impacted as the Airline industry is the one of the major sources of revenue. This research paper aims to analyze the current and the future situation of the aviation industry with reference to COVID-19

중국상장기업의 부실예측모형에 관한 실증연구

[연구목적] 2018년에 시작된 미중 무역전쟁, 2020년 초의 코로나 19 등은 중국기업의 수출에 큰 타격을 주었다. 이로 인해 기업의 경영리스크와 재무리스크가 커지면서 많은 기업들이 상장폐지 위험에 직면해 있다. 본 연구는 재무지표와 비재무지표를 통해 중국상장기업의 부실예측모형의 정확도를 측정하는데 목적이 있다. [연구방법] 본 연구는 2019년 후선 A증시에 상장한 66개 부실기업과 66개 정상기업을 대상으로 재무지표 14개와 비재무지표 9개를 선정하여 2018년 기업자료를 바탕으로 로지스틱회귀분석으로 실시하여, 부실예측을 실시하였다. 특히, 부실예측은 비재무지표만을 활용한 <모형1>, 재무지표만을 활용한 <모형2>, 재무와 비재무지표를 결합한 <모형3>으로 구성하였다. [연구결과] 분석결과, <모형1>에서는 지분집중도, 감사의견적정이 유의한 것으로 나타났으며, <모형2>는 유동비율, 이자보상배율, 자기자본이익률, 경영자본회전율 등 변수들이 유의한 것으로 나타났다. <모형3>에서는 지분집중도, 감사의견적정과 자기자본이익률이 유의한 것으로 나타났다. 특히 감사의견적정과 자기자본이익률 변수는 모두 모형에서 유의한 것으로 나타나 기업부실예측의 중요한 변수로 판단된다. [연구의 시사점] 재무지표만으로 부실예측모형을 구축하는 것보다 비재무지표를 함께 포함시켜 예측하는 것이 높은 예측력을 보였다. 따라서 기업의 효과적인 리스크관리 및 통제를 위한 리스크조기예측모형으로써의 활용을 기대한다.

A dynamic financial distress forecast model with multiple forecast results under unbalanced data environment

Corporate financial distress forecasts are important for companies, investors and regulatory authorities. However, as most financial distress forecast (FDF) models in previous studies were based on a single time dimension, they have tended to ignore the two key financial distress data characteristics, imbalanced data sets and concept drift of data stream. To overcome these problems, this study proposes a new dynamic financial distress forecasting (DFDF) approach, the Adaptive Neighbor SMOTE-Recursive Ensemble Approach (ANS-REA), that allows for multiple forecast results from unbalanced data streams. An empirical experiment was conducted on 373 financially distressed samples and 1119 matching normal Chinese listed companies from 2007 to 2017. With an overall average AUC, it was found that the Random Forest (RF) classifier outperformed other commonly used classifiers such as Support Vector Machine (SVM), Decision Tree (DT), baggingDT, oblique random forests (obRF), Kernel ridge regression (KRR) and Bayes in the classification of DFDF data. In addition, the proposed ANS-REA algorithm had better performance than SMOTE, ANS, Random Walk Over-Sampling Approach (RWO), Rapidly Converging Gibbs sampling Technique (racog), SMOTEboost, RUSboost, SMOTEbagging, wRACOG and Majority Weighted Minority Oversampling Technique (MWMOTE) methods in dealing with imbalanced data sets classification. Further, we found that the proposed model that combined the multiple forecast results is the effective way to solve the financial distress forecast problem.

Dynamic forecasting of financial distress: the hybrid use of incremental bagging and genetic algorithm—empirical study of Chinese listed corporations

Financial distress prediction plays an important role in affecting the healthy development of the firm. Previous studies on financial distress prediction have chiefly built prediction models based on static data samples within a period of time, which cannot adapt to the changing economic environment or the changing enterprise operational environment. To achieve dynamic financial distress prediction, the incremental bagging based on selective ensemble is proposed. Genetic algorithm is employed to optimize the base classifier combination for further forecasting of financial distress per year. Financial datasets from 2005 to 2014 of Chinese real data were collected and represented in terms of significant six-variable financial ratios. In order to verify the prediction capability of the proposed method, comparative analysis with three commonly used dynamic prediction method is conducted. Empirical results indicate that the proposed method helps model producing more accurate performance on financial data stream. This method of using incremental bagging with selective ensemble and optimizing classifier combination is useful in controlling risk in financial management.

Forecasting the financial distress of mining companies: Tool for testing the key performance indicators

There are numerous studies and research work related to the forecasting of financial distress of companies. Developed theoretical and practical models were used for forecasting such problems. Application of specific model is relatively novel analytical approach and represents an indicator which sometimes could have large importance for decision makers. Indicators for production and business aspects are represented by one of the most suitable synthetic parameters - Altman Financial Distress Ratio, which is sum of weighted individual parameters. The aim of this paper is to present a method for forecasting the financial distress, mainly based on financial parameters of a company. Calculation of financial parameters was based on the public annual financial reports of companies included in the example. Authors applied the Altman Z-score model on sample of two mining companies, to establish accuracy of this model and possibility for application on other mining companies.

Financial credit risk assessment: a recent review

The assessment of financial credit risk is an important and challenging research topic in the area of accounting and finance. Numerous efforts have been devoted into this field since the first attempt last century. Today the study of financial credit risk assessment attracts increasing attentions in the face of one of the most severe financial crisis ever observed in the world. The accurate assessment of financial credit risk and prediction of business failure play an essential role both on economics and society. For this reason, more and more methods and algorithms were proposed in the past years. From this point, it is of crucial importance to review the nowadays methods applied to financial credit risk assessment. In this paper, we summarize the traditional statistical models and state-of-the-art intelligent methods for financial distress forecasting, with the emphasis on the most recent achievements as the promising trend in this area.

A Hybrid Analysis Approach to Improve Financial Distress Forecasting: Empirical Evidence from Iran

Bankruptcy prediction is an important problem facing financial decision support for stakeholders of firms, including auditors, managers, shareholders, debt-holders, and potential investors, as well as academic researchers. Popular discourse on financial distress forecasting focuses on developing the discrete models to improve the prediction. The aim of this paper is to develop a novel hybrid financial distress model based on combining various statistical and machine learning methods. Then multiple attribute decision making method is exploited to choose the optimized model from the implemented ones. Proposed approaches have also been applied in Iranian companies that performed previous models and it can be consolidated with the help of the hybrid approach.

Predicting financial distress of the South Korean manufacturing industries

The financial distress forecasting has long been of great interest both to scholars and practitioners. The financial distress forecasting is basically a dichotomous decision, either being financial distress or not. Most statistical and artificial intelligence methods estimate the probability of financial distress, and if this probability is greater than the cutoff value, then the prediction is to be financial distress. To improve the accuracy of the financial distress prediction, this paper first analyzed the yearly financial data of 1888 manufacturing corporations collected by the Korea Credit Guarantee Fund (KODIT). Then we developed a financial distress prediction model based on radial basis function support vector machines (RSVM). We compare the classification accuracy performance between our RSVM and artificial intelligence techniques, and suggest a better financial distress predicting model to help a chief finance officer or a board of directors make better decision in a corporate financial distress. The experiments demonstrate that RSVM always outperforms other models in the performance of corporate financial distress predicting, and hence we can predict future financial distress more correctly than any other models. This enhancement in predictability of future financial distress can significantly contribute to the correct valuation of a company, and hence those people from investors to financial managers to any decision makers of a company can make use of RSVM for the better financing and investing decision making which can lead to higher profits and firm values eventually.

An evolution-based approach with modularized evaluations to forecast financial distress

Due to the radical changing of the global economy, a more precise forecasting of corporate financial distress helps provide important judgment principles to decision-makers. Although financial statements reflect a firm's business activities, it is very challenging to discover critical information from these statements. Applying machine learning algorithms can be demonstrated to improve forecasting accuracy in predicting corporate bankruptcy. In this paper, we introduce an evolutionary approach with modularized evaluation functions to forecast financial distress, which allows using any evolutionary algorithm to extract the set of critical financial ratios and integrates more evaluation function modules to achieve a better forecasting accuracy by assigning distinct weights. To achieve a more precise predicting accuracy, the undesirable forecasting results from some modules are weeded out, if their predicting accuracies are out of the allowable tolerance range as learned from our mechanism.

On the Predictability of Kibbutz Financial Distress: A Principal Component Analysis with Bootstrap Confidence Intervals

This paper proposes an econometric model for conditional forecasting of kibbutz financial distress for a range of one to three years. The primary interest in examining the economic performance of the kibbutz is that its driving characteristic is collective rather than individual profit-maximizing. Thus, this study contributes to bridging the gap between literature on labor-managed firms and the theoretical and empirical models aimed at predicting financial distress. This study is also motivated by the kibbutz debt crisis of the mid-1980s. The annual change in productive equity, scaled by either productive assets or annual sales, is assumed to represent kibbutz financial confidence. The attribute vector contains 26 explanatory variables defined over eight different dimensions: liquidity, profitability, cash flow, operating efficiency and growth, capital structure, saving rate, demography, and size. The model as a whole exhibits high statistical significance, efficiency of forecast, and a good fit. It outperforms the naive forecast model in all calculated measures of dispersion. This outcome is reaffirmed by the bootstrap simulation. The findings of this paper suggest that financially distressed kibbutzim are less profitable, tend to be highly leveraged, are characterized by low and even negative saving rates, and have a higher social burden. The paper provides empirical support for the emergency cash reserve, the optimal capital structure, and the heterogeneity hypotheses. The results are also consistent with predictions of organization theory derived from nontransferability of member rights.