Predicting Financial Distress: Multi Scenarios Modeling Using Neural Network

Mohamed Gameel and Khairy El-Geziry

International Journal of Economics and Finance, 2016, vol. 8, issue 11, 159

Abstract: This paper aims to investigate the best scenario to predict financial distress in the Egyptian stock market using a neural network model. The sample consists of 37 company listed on the EGX100. The sample period is eight years from 2001 to 2008, so we can isolate the effects of global financial Depression in the end of 2008, and the effect of economic instability, which coincided with the Egyptian revolution in 2011 tell now. The results show evidence that the best scenario for predicting distress in Egypt is that the company will be distressed if there is a decreasing in liquidity, decreasing in generating cash from sales with increasing in financial leverage.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:ibn:ijefaa:v:8:y:2016:i:11:p:159

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