Developing Financial Distress Prediction Model for Companies Going Public: Accounting, Macroeconomic, Market, and Industry Approaches

Abstract

This research is to construct a model for an accurate prediction of financial distress by finding and including other variables outside the data/information derived the accounting reports. The population of this research is composed of all the non-financial companies listed on the Indonesia Stock Exchange. As for the samples, they are the companies experiencing financial distress which is indicated by their negative profits in two consecutive years; and the control group is composed of the companies in the same industry group with the total asset of almost the same as that of the companies experiencing financial distress; only that these companies do not experience financial distress.The model to construct the financial distress prediction is the Binary Logistic Regression. The results show that the variables of the group of financial ratios, namely liquidity, profitability, leverage, activity, and cash flow, can be used as the variables for the financial distress prediction. However, the variables of the group of market and macroeconomic ratios cannot be employed to predict. Meanwhile, the variable of the group of industry treated as a moderating dummy variable does not indicate to have any moderating influence on the variables of financial ratio that previously proved to have significant influence on the possibility of the financial distress of a company.