Trends and Patterns in Turnaround Strategies

The paper aims to examine the publications on turnaround strategies and identify scientific gaps. Hence, bibliographic couplings of countries, institutions, journals, publications, authors, and co-occurrences of the author keywords were analyzed. Bibliographic methods were employed to examine and visualize the characteristics of the publications with the aid of VOSViewer software. Using 174 articles from the Scopus database, the results revealed that corporate distress, turnaround, organizational decline, turnaround strategies, corporate strategy, financial distress, retrenchment, turnaround strategy, and turnarounds were among the most studied key concepts in this area, The “Journal of Strategy and Management” and “European Management Journal” were the top journals. United States, United Kingdom, and India were the most influential countries. The Fort Hays University and McMurry University were top research institutions. Notably, Huang, Y., Reddy, K.S., and Xie, E. were the most influential authors in this research area. These results will help academicians and practitioners.

An Analysis of the Predictors of Financial Distress for Zimbabwe Listed Corporates

This study brings novelty to the area of corporate distress modelling in Zimbabwe by exploring company-specific indicators of corporate distress, unlike most of the previous studies, which used financial performance indicators. Using a binary logistic regression on a time series dataset collated between 2010 and 2017, this study establishes book value, book value per share, average debt to equity and equity per share as very significant determinants of corporate distress on the Zimbabwe Stock Exchange (ZSE). Future studies incorporating artificial intelligence and a combination of both the traditional financial ratios and market-based indicators is recommended to expand the scope of the study.

Emerging Markets at Risk

Policymakers would like to predict and mitigate the risks associated with the post-global financial crisis rise in corporate leverage in emerging markets. However, long-standing advanced-economy bankruptcy models fail to capture the idiosyncrasies that impact the solvency of emerging market firms. We study how a machine learning technique for variable selection, LASSO, can improve corporate distress risk models in emerging markets. Exploring the trade-off between model fit and predictive power, we find that larger models forecast distress with more accuracy during periods of economic stress (when global factors gain relevance), while more parsimonious specifications outperform during normal times.

Corporate distress prediction using random forest and tree net for india

The paper deals with the Random Forest, a popular classification machine learning algorithm to predict bankruptcy (distress) for Indian firms. Random Forest orders firms according to their propensity to default or their likelihood to become distressed. This is also useful to explain the association between the tendency of firm failure and its features. The results are analyzed vis-à-vis Tree Net. Both in-sample and out of sample estimations have been performed to compare Random Forest with Tree Net, which is a cutting edge data mining tool known to provide satisfactory estimation results. An exhaustive data set comprising companies from varied sectors have been included in the analysis. It is found that Tree Net procedure provides improved classification and predictive performance vis-à-vis Random Forest methodology consistently that may be utilized further by industry analysts and researchers alike for predictive purposes.