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

Background:The suitability and performance of the bankruptcy prediction models is an empirical question.The 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 firms.Out of 208 companies,130 are used for estimation sample,and 78 are holdout for model validation.The 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 model.The paper compares original and re-estimated models to explore the sensitivity of these models towards the change in time periods and financial conditions.Methods: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 models.The 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)tests.Results: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 re-estimated.Amongst the contesting models,the new bankruptcy prediction model outperforms other models.Conclusions:The industry specific model should be developed with the new combinations of financial ratios to predict bankruptcy of the firms in a particular country.The study further suggests the coefficients of the models are sensitive to time periods and financial condition.Hence,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.

The Prediction of Bankruptcy in a Construction Industry of Russian Federation

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.

Prediction Ability of Cash Flows, Net Income （NI）, and Auditors

The accounting information should help investors and creditors evaluate the amounts, timing, and uncertainty of firms＇ future cash receipts and disbursements. The Financial Accounting Standards Board （FASB） contends that accrual-based historical earnings are superior to cash flows in predicting future cash flows. But, Bowen, Burgstahler, and Daley （1986） showed that traditional measures of cash flows （net income （NI） plus depreciation and working capital from operations） appear to be better predictors of future cash flows than accrual accounting earnings. Since then, many researchers have articulated the importance of accounting data, especially cash flows and NI, in the predictive and forecasting processes. In this study, we empirically re-examined the ability of cash flows from operating activities （CFO） and accrual-based NI in predicting firms＇ bankruptcy. In the past, the results of this type of research were mixed. Differently from previous research, we focus on the timing of predictive ability, i.e., which indicator, cash flows or NI, is faster in predicting a firm＇s bankruptcy. We also investigate the timing of auditors＇ issuance of a going-concern opinion. The preliminary results show that the accrual-based NI is more accurate and faster than either CFO or audit opinion in predicting firms＇ failures. On average, NI signals a firm＇s bankruptcy 2.41 years before the bankruptcy filing, while CFO signals 1.48 years before filing. Auditors issued a going-concern opinion, another signal for firms＇ failure, to only 16 out of 41 bankrupt firms one year before bankruptcy, and no auditor issued the going-concern opinion two years before bankruptcy.

Static Model Classification Status： Taking Into Account Emerging External Factors

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 are realized with the use of algorithms linear regression, artificial neural network, and genetic algorithm. In the 2-m model, data set includes both internal and external factors. The results showed that the inclusion of only the microeconomic indicators, excluding external factors, impedes models about two times.

Understanding the indicative factors of university/college closings

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 default.Stakeholders 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.