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Re-estimation and comparisons of alternative accounting based bankruptcy prediction models for Indian companies

Financial Innovation 2016年第1期2卷 59-86页

作者： Bhanu Pratap Singh Alok Kumar Mishra School of Economics University of HyderabadProf.C.R.Rao RoadGachibowliHyderabad-500046 TelanganaIndia

Background:The suitability and performance of the bankruptcy prediction models is an empirical *** aim of this paper is to develop a bankruptcy prediction model for Indian manufacturing companies on a sample of 208 companies consisting of an equal number of defaulted and non-defaulted *** of 208 companies,130 are used for estimation sample,and 78 are holdout for model *** study reestimates the accounting based models such as Altman EI(Journal of Finance 23:19189-209,1968)Z-Score,Ohlson JA(Journal of Accounting Research 18:109-131,1980)Y-Score and Zmijewski ME(Journal of Accounting Research 22:59-82,1984)X-Score *** paper compares original and re-estimated models to explore the sensitivity of these models towards the change in time periods and financial ***:Multiple Discriminant Analysis(MDA)and Probit techniques are employed in the estimation of Z-Score and X-Score models,whereas Logit technique is employed in the estimation of Y-Score and the newly proposed *** performance of all the original,re-estimated and new proposed models are assessed by predictive accuracy,significance of parameters,long-range accuracy,secondary sample and Receiver Operating Characteristic(ROC)***:The major findings of the study reveal that the overall predictive accuracy of all the three models improves on estimation and holdout sample when the coefficients are *** the contesting models,the new bankruptcy prediction model outperforms other ***:The industry specific model should be developed with the new combinations of financial ratios to predict bankruptcy of the firms in a particular *** study further suggests the coefficients of the models are sensitive to time periods and financial ***,researchers should be cautioned while choosing the models for bankruptcy prediction to recalculate the models by looking at the recent data in order to get higher predictive accuracy.

关键词： bankruptcy prediction Indian manufacturing companies MDA Logit Probit Unstable coefficient Predictive accuracy Receiver operating characteristic Long range accuracy

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bankruptcy prediction of Chinese Listed Corporations Using Real-Valued Negative Selection Algorithm Optimized by Simulated Annealing

Bankruptcy Prediction of Chinese Listed Corporations Using R...

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第七届（2012）中国管理学年会商务智能分会场

作者： Ma Fuyu~1,Yu Lean~2 (1 Academy of Mathematics and Systems Science,Chinese Academy of Sciences,Beijing 100190) (2 Academy of Mathematics and Systems Science,Chinese Academy of Sciences,Beijing 100190)

＜正＞This paper presents a modified negative selection algorithm with simulated annealing and self detector classification that solves some of the drawbacks of V-Detector algorithm[16].Specifically,it can produce a good estimate of the optimal radius of self samples and the number of v-detectors needed to cover the certain non-self *** minimization of v-detectors’ overlapping is done through an optimization algorithm with proven convergence *** are performed on the Chinese A-share and B-share market to validate the assumptions made while designing the algorithm and to evaluate its *** show that our algorithm outperforms V-Detector algorithm and other classical classification algorithms.

关键词： Negative Selection V-Detector Algorithm Simulated Annealing bankruptcy prediction

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The prediction of bankruptcy in a Construction Industry of Russian Federation

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Journal of Modern Accounting and Auditing 2013年第2期9卷 256-271页

作者： Elena Makeeva Ekaterina Neretina National Research University Higher School of Economics Moscow Russia

The problem of the firm bankruptcy prediction was investigated by foreign researchers in the 1930s and it still remains relevant. Since the publishing of Altman＇s （1968） major work, based on multiple discriminant analysis （MDA）, this methodological area has considerably changed. Taking into consideration that new data have appeared in the course of time, companies＇ average size has changed, and the accounting standards have changed （Altman, Haldeman, ＆ Narayanan, 1977）, methods and models should be renewed so as to be appropriate for current situation. The purpose of this paper1 is to reveal factors causing bankruptcy and use models appropriate for prediction bankruptcy in the area of a construction industry during the financial crisis. This investigation has been carried out on the basis of logit and probit analysis. The main reasons of bankruptcy revealed in the course of this investigation are the following：（1） non-optimal capital structure formation; （2） ineffective liquidity management; （3） decrease in assets profitability; and （4） decrease in short-term assets turnover. The most reliable indicators which give warning of bankruptcy ahead of others are financial instability and liquidity ratios.

关键词： bankruptcy prediction construction industry logit and probit analysis

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Understanding the indicative factors of university/college closings

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Journal of Management Analytics 2022年第3期9卷 330-350页

作者： Larissa Adamiec Deborah Cernauskas Andrew Kumiega College of Business Benedictine UniversityLisleILUSA Vision Analytics LisleILUSA Illinois Institute of Technology ChicagoILUSA

Higher education has been in a financially precarious position for many years –facing either a total transformation or elimination. Tuition increases and fewercollege-age students from shifting demographics are primary reasons for thefinancial distress. Alternative financial stability models have assumed linearvariable relationships and improperly calculate the probability of *** have historically relied upon models such as those developed byEdmit and the Department of Education which are inadequate at separatingfinancially sound from unsound universities. We used an Automated MachineLearning approach utilizing multiple models to explain the relationship betweenmetrics and the probability of default/closure allowing for more informedmanagerial decisions. This research, although applied to the homogeneousgroup of small liberal arts universities, can be applied to online and stateuniversities and will allow the opportunity to take preventive steps to mitigatethe likelihood of closing due to financial distress.

关键词： bankruptcy prediction statistics decision analysis machine learning forecasting applications random forest

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Support Vector Machines Modeling for prediction

International Journal of Systems and Control

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International Journal of Systems and Control 2008年第1期 42-47页

作者： ZHONG Ping, XI Bin*, CEN yong School of Information Science and Technology, Xiamen University, Xiamen 361005, .China

One of the problems still gaining a great attention in finance is the bankruptcy forecasts. The problem of efficient bankruptcy prognosis is of great interest both to scientists and practitioners. Recent studies have shown that machine learning techniques achieved better performance than traditional statistical ones. This paper applies support vector machines (SVM) to the bankruptcy prediction problem in an attempt to suggest a new model and classification with better explanatory power and stability. Carried out experiments have shown a very promising results of SVM for bankruptcy prediction in terms of predictive accuracy and adaptability.

关键词： Machine learning bankruptcy prediction SVM

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Static Model Classification Status： Taking Into Account Emerging External Factors

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Journal of Modern Accounting and Auditing 2013年第6期9卷 798-807页

作者： Perminov G. I. National Research University-Higher School of Economics Moscow Russia

Analysis of the problem of predicting bankruptcy shows that foreign and domestic models included only internal factors of enterprises. But the same indicators of internal factors in the rapidly changing external environment can lead to bankruptcy, and not in others. External factors are the most dangerous, because the possible influence on them is minimal and the impact of their implementation can be devastating. This paper focuses on the same factors to assess the impact of the macroeconomic indicators （extemal factors） on the parameters of static models predicting a local approximation of the crisis at the plant. To accomplish the purpose, a Spark set of 100 companies was compiled, including 50 companies which officially declared bankruptcy in the period of 2000-2009 and 50 stable operating companies with a random sample of the same time period. External factors were extracted from the Joint Economic and Social Data Archive1 The author compared two data sets：（1） microeconomic indicators--money to the total liabilities, retained earnings to total assets, net profit to revenue, Earnings Before Interest and Taxes （EBIT） to assets, net income to equity, net profit to total liabilities, current liabilities to total assets, the totality of short-term and long-term loans to total assets, current assets to current liabilities, assets to revenue, equity to total assets, and current assets to revenue; and （2） external factors--index of real gross domestic product （GDP）, industrial production index, the index of real cash incomes, an index of real investments, consumer price index, the refinancing rate, unemployment rate, the price of electricity, gas prices, oil price, gas price, dollar to ruble, ruble euro Standard ＆ Poor （S＆P） index, the Russian Trading System （RTS） index, and region. The aim of the comparison results paging classes ＂insolvent＂ and ＂non-bankrupt＂ is achieved using two methods： classification and discrimination. In both methods, computational procedures a

关键词： bankruptcy prediction external factors methods of classification and discrimination

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