Analysis Comparison Financial Distress Prediction Models for Hotel, Restaurant, and Tourism Subsector Companies on IDX - Mutia Andini; Dian Prihardini Wibawa; Ari Agung Nugroho (UIN Raden Mas Said Surakarta, 2023)
The Covid-19 outbreak has plagued and spread to all corners of the world, one of which is Indonesia. As a result of the prolonged Covid-19, Indonesia's economic growth has decreased as well as publicly listed companies on the IDX. This study aims to: (1) test whether there are differences in the prediction results between Taffler, CA-Score, Grover and Ohlson models, (2) test which predictive model is the most accurate in predicting the financial distress of companies in hotel, restaurant, and tourism sub-sectors, and (3) knowing which companies are predicted to be affected by financial distress. This type of research is quantitative descriptive using secondary data taken from the company's financial statements with purposive sampling technique so that there are 13 companies as samples. The results showed that there were differences in the prediction results of Taffler, CA-Score, Grover, and Ohlson models with the highest level of accuracy owned by the Grover model, which was 93,85%. Based on the calculation of the Grover model, there are three companies that are predicted to experience financial distress. Therefore, to overcome this, the company can improve its performance by increasing working capital and profits. Keywords: Financial Distress; Taffler; CA-Score; Grover; Ohlson