The present investigation sets out to study the financial intelligence, as a combination of different indexes and financial ratios considered in predicting the credit worthiness risk, of the admitted companies in Tehran Stock Market. To this aim, data collected from 115 admitted companies to Tehran Stock Market has been analyzed within the period spanning between 2009 and 2014. This is done via logistic regression models and support vector machine.
The findings of the present study indicated that the financial intelligence is able to predict solvency risk, and profitability risk. However, no evidence was found to indicate the prediction power of productivity risk. In the end, it was concluded that financial intelligence has the capability of predicting credit worthiness risk.
Darabi, R., & Mashayekhi, G. (2016). Financial Intelligence in Prediction of Firm’s Creditworthiness Risk, Evidence from Support Vector Machine Approach. Accounting and Auditing Research, 8(30), 102-125. doi: 10.22034/iaar.2016.99185
MLA
Roya Darabi; Ghazaleh Mashayekhi. "Financial Intelligence in Prediction of Firm’s Creditworthiness Risk, Evidence from Support Vector Machine Approach". Accounting and Auditing Research, 8, 30, 2016, 102-125. doi: 10.22034/iaar.2016.99185
HARVARD
Darabi, R., Mashayekhi, G. (2016). 'Financial Intelligence in Prediction of Firm’s Creditworthiness Risk, Evidence from Support Vector Machine Approach', Accounting and Auditing Research, 8(30), pp. 102-125. doi: 10.22034/iaar.2016.99185
VANCOUVER
Darabi, R., Mashayekhi, G. Financial Intelligence in Prediction of Firm’s Creditworthiness Risk, Evidence from Support Vector Machine Approach. Accounting and Auditing Research, 2016; 8(30): 102-125. doi: 10.22034/iaar.2016.99185
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