PERBANDINGAN MODEL ALTMAN, SPRINGATE, ZMIJEWSKI DAN GROVER SEBAGAI PREDIKSI FINANCIAL DISTRESS (Studi Pada Perusahaan Sektor Infrastruktur yang Terdaftar Di Bursa Efek Indonesia (BEI) Periode 2020-2022)

The financial performance of a company is the prospect or future, the potential growth and development for the company. The economic situation can change, this affects the activities and performance of companies, both small and large companies. One of the company's responsibilities is to produce good performance to avoid financial distress. This research aims to find out whether there are differences in predictions between the Altman, Springate, Zmijewski and Grover models as predictions of financial distress and to find out whether the Springate model is the most accurate prediction of financial distress. This research is quantitative research. The data source is secondary data from infrastructure sector companies listed on the IDX in 2020-2022. Sampling used a nonprobability method with purposive sampling technique. The sample consisted of 44 companies. The data analysis technique uses the Kruskal-Wallis H Test with the SPSS 25 program and the Accuracy Level Test. The results of the research reveal that there are significant differences between the Altman, Springate, Zmijewski and Grover calculation models for predicting financial distress and Springate is the most accurate financial distress prediction model in predicting financial distress with an accuracy level of 73%.

Authors

Publication Details

Journal
PROMOSI (Jurnal Pendidikan Ekonomi)
Published
2024-11-19
DOI
https://doi.org/10.24127/jp.v12i2.10463
Primary Topic
Financial Analysis and Corporate Governance
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

PERBANDINGAN MODEL ALTMAN, SPRINGATE, ZMIJEWSKI DAN GROVER SEBAGAI PREDIKSI FINANCIAL DISTRESS (Studi Pada Perusahaan Sektor Infrastruktur yang Terdaftar Di Bursa Efek Indonesia (BEI) Periode 2020-2022)

Dwi Gemina, Erny Amriani Asmin, Ismartaya Ismartaya, Istiyanah
PROMOSI (Jurnal Pendidikan Ekonomi)
Financial Analysis and Corporate Governance
article

PERBANDINGAN MODEL ALTMAN, SPRINGATE, ZMIJEWSKI DAN GROVER SEBAGAI PREDIKSI FINANCIAL DISTRESS (Studi Pada Perusahaan Sektor Infrastruktur yang Terdaftar Di Bursa Efek Indonesia (BEI) Periode 2020-2022)

Dwi Gemina, Erny Amriani Asmin, Ismartaya Ismartaya, Istiyanah
article en

Abstract

The financial performance of a company is the prospect or future, the potential growth and development for the company. The economic situation can change, this affects the activities and performance of companies, both small and large companies. One of the company's responsibilities is to produce good performance to avoid financial distress. This research aims to find out whether there are differences in predictions between the Altman, Springate, Zmijewski and Grover models as predictions of financial distress and to find out whether the Springate model is the most accurate prediction of financial distress. This research is quantitative research. The data source is secondary data from infrastructure sector companies listed on the IDX in 2020-2022. Sampling used a nonprobability method with purposive sampling technique. The sample consisted of 44 companies. The data analysis technique uses the Kruskal-Wallis H Test with the SPSS 25 program and the Accuracy Level Test. The results of the research reveal that there are significant differences between the Altman, Springate, Zmijewski and Grover calculation models for predicting financial distress and Springate is the most accurate financial distress prediction model in predicting financial distress with an accuracy level of 73%.

PROMOSI (Jurnal Pendidikan Ekonomi)Vol. 12(2)
Openalex Percentile: Top 51%
Financial Analysis and Corporate Governance
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.