Using Glow-worm algorithm to predict companies’ financial distress
DOI:
https://doi.org/10.33975/riuq.vol34nS3.1018Keywords:
Glowworm Algorithm, Financial Distress, Hybrid Models, Neural NetworkAbstract
One important research issue in the risk management area is to predict the financial distress of companies. This case has received great attention from banks, companies, managers, and investors. Although there are many studies on this case, the hybrid models (mixed feature selection and classifier models) have been used by researchers in recent years. The main objective of this study is to propose a high-performance predictive model and compare its results with other models that are commonly used for financial distress prediction. To do this, the Glowworm optimization algorithm-based hybrid neural network model was employed. Moreover, the neural network and logistic regression model, which is one of the statistical classifier models were used. The results indicated that the glowworm optimization algorithm (also known as firefly optimization algorithm)-based hybrid neural network model had higher performance compared to the neural network and logistic regression models.
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