A Cross Model Telco Industry Financial Distress Prediction in Indonesia: Multiple Discriminant Analysis, Logit and Artificial Neural Network

Abstract

The competition between telco industries is getting stronger and expects telco companies to continually strengthen the fundamentals of it’s management, so that they will have the ability to survive in the competition with other companies. Incapability to anticipate the global changing trends would lead to a decrease in company business value, which in turn led to business losses. Bankruptcy is a very essential issue which must be aware by the company, the bankruptcy of a company can be measured and evaluated from financial statement data, by analyzing financial statements, the company management can take immediate action to restructure debt due to the effects of the liquidation of bankruptcy could be detrimental to creditors and investors. Academic research may provide a model to prevent bankruptcy indispensable in Indonesia, some of the bankruptcy prediction model uses financial data prior to predict financial difficulties. The study compared three models in this research, there are: Altman model, Ohlson model and Artificial Neural Network Backpropagation. This research aims to compare financial distress prediction model with the most appropriate application in Indonesia’s Telecommunication sector. Comparisons were made by analyzing the accuracy level of each model. The samples used were three telecommunication company that listed on Indonesia Stock Exchange in the period 2013-2017. In summary, the prediction models used in this research can be used to help investors and company management to predict business failure probability.