Analisis Akurasi Metode Prediksi Financial Distress sebagai Detektor Perusahaan Delisting

Abstract

The purpose of this study was to find out which of the Altman, Springate, Zmijewski, and Grover methods had the most accurate accuracy in detecting indications of financial distress that could lead to delisting events. This type of research is quantitative with descriptive method. The data collection instrument is using the documentation method. The sample selection used purposive sampling method, with the sample used as many as 11 manufacturing companies from 24 companies that were delisted during the study period. Data is collected from the delisted company's financial statements and then calculated through the formulas of each method. The results of this study indicate that the Springate method has the highest level of accuracy, which is 100% with a type error of 0%. This is because the Springate method has advantages that can include internal factors through the variables Working Capital to Total Assets, Earning Before Interest and Tax to Total Assets, and Net Profit Before Tax to Current Liability, as well as external factors in the Sales to Total Assets variable so that the Springate method very appropriate to be implemented in analyzing financial distress as an indication of a delisting event. 

Abstrak

Tujuan penelitian ini adalah untuk mengetahui manakah diantara metode Altman, Springate, Zmijewski, dan Grover yang memiliki akurasi paling tepat dalam mendeteksi indikasi financial distress yang dapat mengarah pada peristiwa delisting. Jenis penelitian ini adalah kuantitatif dengan metode deskriptif. Instrumen pengumpulan data adalah menggunakan metode dokumentasi. Pemilihan sampel menggunakan metode purposive sampling, dengan sampel yang dipakai sebanyak 11 perusahaan manufaktur dari 24 perusahaan yang delisting selama periode penelitian. Data dikumpulkan dari laporan keuangan perusahaan yang delisting untuk kemudian dihitung melalui formula masing-masing metode. Hasil penelitian ini menunjukkan bahwa metode Springate memiliki tingkat akurasi paling tinggi yaitu sebesar 100% dengan type error sebesar 0%. Hal ini dikarenakan metode Springate memiliki kelebihan yang dapat menyertakan faktor internal melalui variabel Working Capital to Total Asset, Earning Before Interest and Tax to Total Asset, dan Net Profit Before Tax to Current Liability, serta faktor eksternal pada variabel Sales to Total Asset sehingga metode Springate sangat tepat untuk diimplementasikan dalam menganalisis financial distress sebagai indikasi peristiwa delisting.