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.

Organizational Culture Content of the Manufacturing Companies in Austria and the Czech Republic

The main focus of this scientific article is organizational culture that represents very complicated and complex social phenomenon. Organizational culture is understood as the set of basic assumptions, values, standards, and artifacts, shared in the company in long-term horizon. The main objective of empirical research was to map organizational culture content in manufacturing companies in the Czech Republic and Austria. The content of organizational culture in the selected level of analysis was identified by means of qualitative methods--individual interviews and focus group discussions. With respect to specified objective, research was implemented in the sample of 10 companies in the Czech Republic and Austria. Data acquired by qualitative method of focus group were processed by means of content analysis. The main result of this part of empirical research was to describe the organizational culture content in the manufacturing companies operating in Austrian and Czech environment.

A combined machine learning algorithms and DEA method for measuring and predicting the efficiency of Chinese manufacturing listed companies

Data Envelopment Analysis(DEA)is a linear programming methodology for measuring the efficiency of Decision Making Units(DMUs)to improve organizational performance in the private and public sectors.However,if a new DMU needs to be known its efficiency score,the DEA analysis would have to be re-conducted,especially nowadays,datasets from many fields have been growing rapidly in the real world,which will need a huge amount of computation.Following the previous studies,this paper aims to establish a linkage between the DEA method and machine learning(ML)algorithms,and proposes an alternative way that combines DEA with ML(ML-DEA)algorithms to measure and predict the DEA efficiency of DMUs.Four ML-DEA algorithms are discussed,namely DEA-CCR model combined with back-propagation neural network(BPNN-DEA),with genetic algorithm(GA)integrated with back-propagation neural network(GANN-DEA),with support vector machines(SVM-DEA),and with improved support vector machines(ISVM-DEA),respectively.To illustrate the applicability of above models,the performance of Chinese manufacturing listed companies in 2016 is measured,predicted and compared with the DEA efficiency scores obtained by the DEA-CCR model.The empirical results show that the average accuracy of the predicted efficiency of DMUs is about 94%,and the comprehensive performance order of four ML-DEA algorithms ranked from good to poor is GANN-DEA,BPNN-DEA,ISVM-DEA,and SVM-DEA.

The Multiple Effects and Mechanism Study of Digitalization on the Performance of Manufacturing Enterprises

This paper establishes indicators to measure the degree of digitalization of enterprises with the text mining method,based on the 2011‒2018 annual report of China’s listed non-high-tech manufacturing companies,and in the resource-based view to investigate how digital resources affect the performance of enterprises during the digitalization process.The empirical results show that digitalization affects enterprise performance through management and sales activities.The impact of management and sales offset each other so that the total impact of digitalization degree on performance is not significant.The study also finds that the scale can strengthen the impact between digitalization and performance,yet the digitalization subsidies will lessen the impact.Generally,China’s non-high-tech manufacturing enterprises are still in the primary stage of digitalization,and the performance of digitalization in business model innovation is canceled out by management imbalance.