Tests on Business Failure Processes in Morocco: Organizational and Managerial Aspect

In Morocco,business failures will grow by 4%in 2019,according to the latest Euler Hermes report.The international credit insurer has placed Morocco in the category of countries that will suffer a deterioration.After Turkey,Morocco posted the second most important degradation of the category.Already high in 2017(12%),the aggravation of the failures of Moroccan companies exceeds by 10%the level of the period 2003-2007 which places the kingdom among the most exposed countries.A situation that is the exact opposite of the global trend since business failures will have to decline according to Euler Hermes,notably thanks to the improvement in the global economic situation.This research is a response to the calls of many authors who urge testing conceptual frameworks,even the most elaborated,in an international context.It is also a continuation of the pioneering work on the business failures,with an original perspective on examining the determinants of the companies based in a developing country.The determinants of the failures have been the subject of many conceptual and empirical developments without a consensus emerging from this research.To do so,four of the most recurrent mechanisms in Western investigations that lead to a business failure have been put to test:business failure,managerial behavior,business sector,and age of the firm and its legal form.The empirical study was conducted through a questionnaire sent to companies and/or managers of the defaulting companies already identified with the Casablanca Commercial Court.Results show that the age of manager,personal skills,and the managerial skills are the main determinants of the business failures.

Artificial Intelligence Based Optimal Functional Link Neural Network for Financial Data Science

In present digital era,data science techniques exploit artificial intelligence(AI)techniques who start and run small and medium-sized enterprises(SMEs)to have an impact and develop their businesses.Data science integrates the conventions of econometrics with the technological elements of data science.It make use of machine learning(ML),predictive and prescriptive analytics to effectively understand financial data and solve related problems.Smart technologies for SMEs enable allows the firm to get smarter with their processes and offers efficient operations.At the same time,it is needed to develop an effective tool which can assist small to medium sized enterprises to forecast business failure as well as financial crisis.AI becomes a familiar tool for several businesses due to the fact that it concentrates on the design of intelligent decision making tools to solve particular real time problems.With this motivation,this paper presents a new AI based optimal functional link neural network(FLNN)based financial crisis prediction(FCP)model forSMEs.The proposed model involves preprocessing,feature selection,classification,and parameter tuning.At the initial stage,the financial data of the enterprises are collected and are preprocessed to enhance the quality of the data.Besides,a novel chaotic grasshopper optimization algorithm(CGOA)based feature selection technique is applied for the optimal selection of features.Moreover,functional link neural network(FLNN)model is employed for the classification of the feature reduced data.Finally,the efficiency of theFLNNmodel can be improvised by the use of cat swarm optimizer(CSO)algorithm.A detailed experimental validation process takes place on Polish dataset to ensure the performance of the presented model.The experimental studies demonstrated that the CGOA-FLNN-CSO model has accomplished maximum prediction accuracy of 98.830%,92.100%,and 95.220%on the applied Polish dataset Year I-III respectively.