Predicting Business Distress Using Neural Network in SME-Arab Region

Domaine:

socioeconomic

Type de record:

paper

Créateur:

AnaMalWor

Éditeur:

Uni

Hôte:

The paper analyzes the financial and operational measures for Small and medium-sized enterprises (SME) business distress for predicting credit worthiness by using panel data of 110 observations from 22 SME companies for a period of 5 years (2009 – 2013). Panel logistic and Neural Network (NN) models are developed as alternative techniques for predicting the business distress. The result suggests that cash cycle, net fixed assets, and leverage ratio are key factors in making credit decisions by lenders. The logistic model overall correctly classified 70 percent while NN framework outperformed the logistic model with 93 percent overall correct classification in training phase, and 83 percent in testing phase. The study opens up potential opportunities for the lending firms to adopt advanced analytical frameworks for predicting distress behavior of business firms. Keywords: SME, Business distress, Arab region, Petrochemical sub-sectors, Logit Model, Neural Network. JEL codes: G29, G32

Visit

doi.org

Similaires

Effect of Multicollinearity in Predicting Diabetes Mellitus Using Statistical Neural NetworkPrediction of Financial Distress Using Dynamic Artificial Neural Network for Early Warning SystemPredicting physico-chemical parameters of Barekese reservoir using feedforward neural networkA Statistical Prediction Of Likely Distress In Nigeria Banking Sector Using A Neural Network ApproachPredicting Earth-Vas2 parameters of coastal plain sand aquifers using artificial neural Network(s), in the Calabar region of NigeriaPREDICTING THE NATURE OF TERRORIST ATTACKS IN NIGERIA USING BAYESIAN NEURAL NETWORK MODEL

Effect of Multicollinearity in Predicting Diabetes Mellitus Using Statistical Neural Network

ABSTRACT Diabetes mellitus (DM) is a diverse group of metabolic disorders that is frequently associ

Prediction of Financial Distress Using Dynamic Artificial Neural Network for Early Warning System

International audience In Kenya's economic landscape, financial hardship is a growing

Predicting physico-chemical parameters of Barekese reservoir using feedforward neural network

A Statistical Prediction Of Likely Distress In Nigeria Banking Sector Using A Neural Network Approach

One of the most significant threats to the economy of a nation is the bankruptcy of its banks. This

Predicting Earth-Vas2 parameters of coastal plain sand aquifers using artificial neural Network(s), in the Calabar region of Nigeria

PREDICTING THE NATURE OF TERRORIST ATTACKS IN NIGERIA USING BAYESIAN NEURAL NETWORK MODEL

PREDICTING THE NATURE OF TERRORIST ATTACKS IN NIGERIA USING BAYESIAN NEURAL NETWORK MODEL

Poster presented at the Deep Learning Indaba 2023 by Tayo Ogundunmade