Financial Intelligence in Prediction of Firm's Creditworthiness Risk: Evidence from Support Vector Machine Approach

This paper seeks to explore the usefulness of financial indicators in measuring the financial health of companies and how to use these relevant attributes to design intelligent financial solution provided to investors and financial institutions to predict financial crashes. Such indicators are of the utmost importance since financial risks have significantly increased in the context of the current global financial crisis.Given that a company forms a micro financial system, its good financial health contributes to building a powerful economic engine; therefore, we have selected a sample of 20 firms over 3 years (2009-2011) whose 39 financial indicators are provided, and we have used Principal Factor Analysis (PFA) approach to reduce the dimension of the input matrix to 8 factors that impact on the firms’ financial health, in order to set up our Support Vector Machine (SVM) Model through which we can predict firms’ creditworthiness risk.