Factores determinantes de la insolvencia empresarial: caso aplicado a la Bolsa Mexicana de Valores

El objetivo de la presente investigación es contribuir al conocimiento sobre el impacto que tienen los factores financieros y económicos en la insolvencia empresarial de las empresas públicas que cotizan en la Bolsa Mexicana de Valores (BMV) y se realiza un análisis comparativo entre empresas y sectores que han incurrido en insolvencia y las que no. Para ello se utiliza la metodología de probit con datos panel. La información del presente estudio proviene de empresas públicas que han cotizado en la Bolsa Mexicana de Valores en los últimos 26 años. Los resultados indican que los factores financieros, no financieros y macroeconómicos son las determinantes en la insolvencia empresarial y por otra parte en el modelo multisectorial los sectores que tienen más posibilidad de caer en la insolvencia empresarial son el sector de productos de consumo frecuente, el sector industrial, seguido por el sector de servicios y bienes de consumo no básico ya que el que menor riesgo tiene es el sector de materiales

Financial risk management in shipping investment, a machine learning approach

There has been a plethora of research into company credit risk and financial default prediction from both academics and financial professionals alike. However, only a limited volume of the literature has focused on international shipping company financial distress prediction, with previous research concentrating largely on classic linear based modelling techniques. The gaps, identified in this research, demonstrate the need for increased effort to address the inherent nonlinear nature of shipping operations, as well as the noisy and incomplete composition of shipping company financial statement data. Furthermore, the gaps illustrate the need for a workable definition of financial distress, which to date has too often been classed only by the ultimate state of bankruptcy/insolvency. This definition prohibits the practical application of methodologies which should be aimed at the timely identification of financial distress, thereby allowing for remedial measures to be implemented to avoid ultimate financial collapse. This research contributes to the field by addressing these gaps through i) the creation of a machine learning based financial distress forecasting methodology and ii) utilising this as the foundation for the development of a software toolkit for financial distress prediction. This toolkit enables the practical application of the financial risk principles, embedded within the methodology, to be readily integrated into an enterprise/corporate risk management system. The methodology and software were tested through the application of a bulk shipping company case study utilising 5000 bulk shipping company-year accounting observations for the period 2000-2018, in combination with market and macroeconomic data. The results demonstrate that the methodology improves the capture of distress correlations, that traditional financial distress models have struggled to achieve. The methodology's capacity to adequately treat the problem of missing data in company financial statements was also validated. Finally, the results also highlight the successful application of the software toolkit for the development of a multi-model, real time system which can enhance the financial monitoring of shipping companies by acting as a practical "early warning system" for financial distress.There has been a plethora of research into company credit risk and financial default prediction from both academics and financial professionals alike. However, only a limited volume of the literature has focused on international shipping company financial distress prediction, with previous research concentrating largely on classic linear based modelling techniques. The gaps, identified in this research, demonstrate the need for increased effort to address the inherent nonlinear nature of shipping operations, as well as the noisy and incomplete composition of shipping company financial statement data. Furthermore, the gaps illustrate the need for a workable definition of financial distress, which to date has too often been classed only by the ultimate state of bankruptcy/insolvency. This definition prohibits the practical application of methodologies which should be aimed at the timely identification of financial distress, thereby allowing for remedial measures to be implemented to avoid ultimate financial collapse. This research contributes to the field by addressing these gaps through i) the creation of a machine learning based financial distress forecasting methodology and ii) utilising this as the foundation for the development of a software toolkit for financial distress prediction. This toolkit enables the practical application of the financial risk principles, embedded within the methodology, to be readily integrated into an enterprise/corporate risk management system. The methodology and software were tested through the application of a bulk shipping company case study utilising 5000 bulk shipping company-year accounting observations for the period 2000-2018, in combination with market and macroeconomic data. The results demonstrate that the methodology improves the capture of distress correlations, that traditional financial distress models have struggled to achieve. The methodology's capacity to adequately treat the problem of missing data in company financial statements was also validated. Finally, the results also highlight the successful application of the software toolkit for the development of a multi-model, real time system which can enhance the financial monitoring of shipping companies by acting as a practical "early warning system" for financial distress

Corporate Bankruptcy Prediction

Bankruptcy prediction is one of the most important research areas in corporate finance. Bankruptcies are an indispensable element of the functioning of the market economy, and at the same time generate significant losses for stakeholders. Hence, this book was established to collect the results of research on the latest trends in predicting the bankruptcy of enterprises. It suggests models developed for different countries using both traditional and more advanced methods. Problems connected with predicting bankruptcy during periods of prosperity and recession, the selection of appropriate explanatory variables, as well as the dynamization of models are presented. The reliability of financial data and the validity of the audit are also referenced. Thus, I hope that this book will inspire you to undertake new research in the field of forecasting the risk of bankruptcy

Factores determinantes de la insolvencia empresarial: caso aplicado a la bolsa mexicana de valores

El objetivo de la presente investigación es contribuir al conocimiento sobre el impacto que tienen los factores financieros y económicos en la insolvencia empresarial de las empresas públicas que cotizan en la Bolsa Mexicana de Valores (BMV) y se realiza un análisis comparativo entre empresas y sectores que han incurrido en insolvencia y las que no. Para ello se utiliza la metodología de probit con datos panel. La información del presente estudio proviene de empresas públicas que han cotizado en la Bolsa Mexicana de Valores en los últimos 26 años. Los resultados indican que los factores financieros, no financieros y macroeconómicos son las determinantes en la insolvencia empresarial y por otra parte en el modelo multisectorial los sectores que tienen más posibilidad de caer en la insolvencia empresarial son el sector de productos de consumo frecuente, el sector industrial, seguido por el sector de servicios y bienes de consumo no básico ya que el que menor riesgo tiene es el sector de materiales