Bankruptcy Prediction Using Machine Learning Techniques

In this study, we apply several advanced machine learning techniques including extreme gradient boosting (XGBoost), support vector machine (SVM), and a deep neural network to predict bankruptcy using easily obtainable financial data of 3728 Belgian Small and Medium Enterprises (SME’s) during the period 2002–2012. Using the above-mentioned machine learning techniques, we predict bankruptcies with a global accuracy of 82–83% using only three easily obtainable financial ratios: the return on assets, the current ratio, and the solvency ratio. While the prediction accuracy is similar to several previous models in the literature, our model is very simple to implement and represents an accurate and user-friendly tool to discriminate between bankrupt and non-bankrupt firms.

Fuzzy-based non-bankruptcy trend forecast

Many companies face an economic downturn due to the Covid-19 pandemic outbreak, which makes their future uncertain. The practical aim of the paper is to establish a procedure for an effective prediction of a business tendency to bankrupt in the short-term period. The tool is a three-stage fuzzy model formulated in the theoreticalmethodological part and applied on the real data of an examined company. The model input parameters are objective and subjective measured data between 2008-2020 of a nature affecting the output. The output is an interval of subjectively expected values determining the non-Bankruptcy trend (non-B) of a company. The paper shows advantages of the interval fuzzy approach for bankruptcy prediction and identifies the measure of business safety.

Practical application of the CCB model in Czechia

This research aimed to present a new bankruptcy prediction model and apply this original prediction method in practice. The Come Clean Bankruptcy (or CCB) model uses relevant financial indicators and ratios to detect the signs of impending financial distress in time so that the management can take appropriate measures to avoid it. The model was applied to the data reported by 199 entities operating in the textile/clothing industry in the Czech Republic. Analyzing data reported for the previous seven years enabled us to predict which companies are more likely to end in a difficult financial situation. Afterward, comparing these predictions with the actual development of those companies in 2013-2020 serves to verify the efficacy and usability of the model to corporate reality. The research has shown that companies that went bankrupt in the analyzed period represented only a fraction of the data set (roughly 4.5%). Despite the small number of financial failures occurring during the analyzed period, the CCB model could detect impending bankruptcy in one-third of the cases.

Prediksi Kebangkrutan Pada Perusahaan Yang Terdaftar Di Bursa Efek Indonesia

This study aims to examine the potential for bankruptcy of companies with three analytical models, namely Altman Z-Score, Springate S-Score, and Zmijewski X-Score, and assess the level of accuracy of the three models. Each model uses ratio analysis with the elements of assets, debt, capital, and company profits. This study uses a sample of coal mining companies listed on the Indonesia Stock Exchange (IDX) during the 2014-2018 period. The sampling technique in this study used purposive sampling and obtained 24 sample companies. This study uses secondary data, namely the company's financial statements obtained from IDX's official website. This study calculates financial ratios, compares the scores of the three bankruptcy prediction models, and tests the model's accuracy. The results of this study show that of the three models, the Springate S-Score model is the most accurate in predicting bankruptcy, with an accuracy rate of 83.33%, as evidenced by two companies that were delisted from the IDX. This study can be used as a reference and as material for consideration in making investment decisions for companies and investors.

Altman Z-Score Sebagai Pengukur Potensi Kebangkrutan PT. MNC Land Sebelum Dan Selama Pandemi Covid-19

MNC Land, as one of the companies engaged in the hotel accommodation sector, has also experienced a negative impact due to the pandemic. The income statement shows a decrease in the revenue account of PT. MNC Land. If the COVID-19 pandemic lasts for a long time, it will have the potential to cause bankruptcy. Assessment of the potential bankruptcy prediction of the company can be measured through the Altman Z-Score analysis model. This study aims to assess the potential for bankruptcy of PT. MNC Land before and during the COVID-19 pandemic when measured by the Altman Z-Score model. The calculation results show that before COVID-19 spread to Indonesia (2017 – 2019) and during the pandemic (2020), PT. MNC Land Tbk is in a safe discriminant zone. The company is still in a healthy state and does not have financial problems or has no potential for bankruptcy.

Relationship between Google search and the Vietcombank stock

The overall objective of this research is to analyze the financial condition of failing companies prior to bankruptcy, in comparison with non-failing companies, which are matched on the industry, size, and time-period. The sample consists of 168 SMEs from the wholesale and retail industry, whose financial statements were analyzed for the 2011-2015 period. The analysis is primarily based on the ratio analysis and the models developed for bankruptcy prediction and financial statement manipulation. Mann-Whitney U test is used to compare differences between failing and non-failing SMEs for a set of twenty variables. Research findings indicate that there is a significant difference between failing and non-failing SMEs, especially in accruals, asset quality, leverage, profitability, and liquidity. For the very first time in the transition economy of CEE Bosnia and Herzegovina, the pre-bankruptcy behavior of failing SMEs is analyzed, providing insights into potentially manipulated areas, which represent the main contribution of the research.

Financial statement manipulation in failing Small and Medium-Sized Enterprises in Bosnia and Herzegovina

The overall objective of this research is to analyze the financial condition of failing companies prior to bankruptcy, in comparison with non-failing companies, which are matched on the industry, size, and time-period. The sample consists of 168 SMEs from the wholesale and retail industry, whose financial statements were analyzed for the 2011-2015 period. The analysis is primarily based on the ratio analysis and the models developed for bankruptcy prediction and financial statement manipulation. Mann-Whitney U test is used to compare differences between failing and non-failing SMEs for a set of twenty variables. Research findings indicate that there is a significant difference between failing and non-failing SMEs, especially in accruals, asset quality, leverage, profitability, and liquidity. For the very first time in the transition economy of CEE Bosnia and Herzegovina, the pre-bankruptcy behavior of failing SMEs is analyzed, providing insights into potentially manipulated areas, which represent the main contribution of the research.

Bankruptcy Prediction for Innovative Companies

The main purpose of this article is to identify the best neural network model algorithm and relevant set of variables for predicting financial distress/bankruptcy in innovative companies. While previous articles in this area considered neural network analysis for large companies from primary sectors of the economy, we take the novel approach of examining theless-explored area of innovative companies. First, we complete a comprehensive review of the relevant literature in order to define the best configuration of factors which can influence bankruptcy, network architecture and learning methodology. We apply our chosen method to a sample of companies from around the world, from industries which are considered innovative, and identify the dependence of bankruptcy probability on a set of factors which are reflected in the financial data of a company. Our evaluation is based on the financial data of 300 companies – 50 of them are bankrupts, and 250 are ‘healthy’. Our results represent the set of relevant factors for bankruptcy prediction and the appropriate neural network. We have applied a total of 19 factors characterising efficiency, liquidity, profitability, sustainability, and level of innovation. Our proposed analysis is appropriate for all sizes of companies. We provided two models in order to cater for the most confidence in terms of obtained results. The total predictive ability of the model developed in our research is almost 98%, which is extremely efficient, and corresponds to the results of the most modern methods. Both approaches demonstrated almost the same level of influence of factor groups on final bankruptcy probability.

Bankruptcy Prediction with a Doubly Stochastic Poisson Forward Intensity Model and Low-Quality Data

With the record high leverage across all segments of the (global) economy, default prediction has never been more important. The excess cash illusion created in the context of COVID-19 may disappear just as quickly as the pandemic entered our world in 2020. In this paper, instead of using any scoring device to discriminate between healthy companies and potential defaulters, we model default probability using a doubly stochastic Poisson process. Our paper is unique in that it uses a large dataset of non-public companies with low-quality reporting standards and very patchy data. We believe this is the first attempt to apply the Duffie–Duan formulation to emerging markets at such a scale. Our results are comparable, if not more robust, than those obtained for public companies in developed countries. The out-of-sample accuracy ratios range from 85% to 76%, one and three years prior to default, respectively. What we lose in (data) quality, we regain in (data) quantity; the power of our tests benefits from the size of the sample: 15,122 non-financial companies from 2007 to 2017, unique in this research area. Our results are also robust to model specification (with different macro and company-specific covariates used) and statistically significant at the 1% level.