ANALISIS PREDIKSI FINANCIAL DISTRESS DENGAN MENGGUNAKAN MODEL SRINGATE PADA PERUSAHAAN PROPERTI DAN REAL ESTATE YANG TERDAFTAR DI BEI PERIODE 2019-2020

  • Rahman F
N/ACitations
Citations of this article
126Readers
Mendeley users who have this article in their library.

Abstract

This study aims to financial distress predict and the level of accuracy using the Springate model in the property and real estate sector listed on the Indonesia Stock Exchange for the 2019-2020 period. The population of this study is all property and real estate companies listed on the Indonesia Stock Exchange for the 2019-2020 period, so the population of this study managed to find 66 companies. Samples were selected based on predetermined purposive sampling criteria. The sample selected according to the specified criteria is 37 companies. The data analysis technique used the Springate S-Score discriminant analysis technique. The results of the bankruptcy analysis using the Springate method, namely in 2019 before the onset of covid-19 there were 27 property and real estate companies in financial distress and 10 companies in healthy condition (non-financial distress). In 2020, during the COVID-19 pandemic, there were additional companies that were in financial distress, namely 34 companies and only 3 companies that remained in a healthy condition (non-financial distress). Based on the results of the analysis of the Springate method in predicting bankruptcy in property and real estate sector companies, it has an accuracy rate of 62.2%.

Cite

CITATION STYLE

APA

Rahman, F. (2021). ANALISIS PREDIKSI FINANCIAL DISTRESS DENGAN MENGGUNAKAN MODEL SRINGATE PADA PERUSAHAAN PROPERTI DAN REAL ESTATE YANG TERDAFTAR DI BEI PERIODE 2019-2020. GOVERNANCE: Jurnal Ilmiah Kajian Politik Lokal Dan Pembangunan, 8(2). https://doi.org/10.56015/governance.v8i2.42

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free