Penelitian ini membahas kemampuan model Altman Z-score emerging market score (EMS) untuk memprediksi status forced delisting di Bursa Efek Indonesia (BEI). Selanjutnya berdasarkan data forced delisting di Indonesia selama kurun waktu 2004 – 2015, analisis diskriminan dengan menggunakan variabel Altman Z-score EMS dan tambahan variabel rasio arus kas operasi digunakan untuk menilai perbedaan tingkat akurasi prediksi antara kedua pemodelan tersebut. Penelitian ini menggunakan data 52 perusahaan non-keuangan yang terdaftar di Indonesia, 26 di antaranya mengalami forced delisting selama periode pengamatan dan 26 lainnya tidak mengalami forced delisting. Hasil penelitian ini menunjukkan bahwa model model prediksi Altman Z-score EMS dapat digunakan untuk memprediksi pengenaan status forced delisting di BEI hingga tiga tahun sebelum perusahaan tersebut mengalami forced delisting dengan tingkat akurasi 67-69% per tahun pengamatan. Selain itu, dengan menggunakan data forced delisting di BEI, analisis diskriminan dengan menggunakan variabel Altman Z-score EMS dan rasio arus kas operasi/total kewajiban dapat meningkatkan tingkat akurasi prediksi pengenaan status forced delisting.

This study discusses the ability of Altman Z-score emerging market score (EMS) modelling to predict the status of forced delisting in Indonesia Stock Exchange (ISX). Furthermore, based on forced delisting data in Indonesia during the period of 2004 - 2015, discriminant analysis using Altman Z-score EMS variables and additional variable in form of operating cash flow ratio is used to assess the differences in the prediction performance. This study uses data of 52 non-financial companies listed in Indonesia, 26 of whom undergone forced delisting during the observation period and the other 26 did not experience forced delisting. From this study, it was found that the Altman Z-score EMS model can be used to predict the forced delisting status up to three years before the company undergone forced delisting with accuracy rate of 67-69% per year of observation. In addition, by using the forced delisting data, discriminant analysis using variables Altman Z-score EMS and the ratio of operating cash flow / total liabilities could increase the prediction accuracy rate of imposition of forced delisting status.