Bankruptcy Prediction Models Applied on Companies Listed on the Indonesian Stock Exchange (IDX) - Harsono Yoewono SE,AK,BBA hons,Mak (Journal of Management and Leadership, 2018)
This study tries to determine the best BPM (bankruptcy prediction model) method in predicting the bankruptcy (delisting) event amongst the delisted companies from the IDX for the period of 2011-2015. To verify the acuracy rate of those 4 BPMs, that is Altman, Springate, Zmijewski, and Grover, we apply these 4 BPM methods in predicting the non-bankruptcy (non-delisting) event of the paired companies used as the sample. This also mean that we need to measure the Error Type-II (ET-II). On average, the acuracy rate of 4 BPMs in predicting 7 companies NOT to be bankrupt (still-listed) was 82.14%, and coupled with the relevant ET-II at 17.86%. By restricting the prediction only on the bankruptcy (delisting) event, Altman is the best BPM method with an acuracy of 71.43%. Altman becomes the best BPM in predicting the bankruptcy (delisting) event as it has an error rate by 14.29%, lower than the Springate. Although Springate has an acuracy of 71.43%, it has an error rate higher than Altman, that is by 28.57%. Grover and Zmijewski took the third and fourth place respectively in the overall acuracy and in predicting the bankruptcy (delisting) event. By companies, the 4 BPM can predict the bankrupty (delisting) event of PWSI (Panca Wiratama Sakti), that is with ET-I = 0, but not with the delisting event of KARK (Dayaindo Resource International) whose acuracy rate was 0%.