Using discriminant analysis to predict financial distress

2000 ◽  
Vol 6 (3) ◽  
pp. 591-591 ◽  
Author(s):  
Cynthia Benzing
Keyword(s):  
Discriminant Analysis ◽  
Financial Distress

scholarly journals MODEL PREDIKSI FINANCIAL DISTRESS PADA PERUSAHAAN MANUFAKTUR SEKTOR INDUSTRI BARANG DAN KONSUMSI

2019 ◽  
Vol 3 (2) ◽  
pp. 77
Author(s):  
Herlina Herlina ◽  
Ahmad Ridho’i ◽  
Anggie Erma Yunita ◽  
Mega Puja Azhari ◽  
Ade Reynaldi Saputra
Keyword(s):  
Logistic Regression ◽  
Particle Swarm Optimization ◽  
Support Vector Machines ◽  
Discriminant Analysis ◽  
Financial Distress ◽  
Particle Swarm ◽  
Support Vector ◽  
Multiple Discriminant Analysis ◽  
Swarm Optimization ◽  
Vector Machines

Kesulitan keuangan (financial distress) adalah sebuah tahapan yang akan dilalui oleh sebuah perusahaan sebelum mengalami kebangkrutan. Dengan alasan tersebut maka kemampuan untuk memprediksi kesulitan keuangan dapat menjadi informasi yang bermanfaat bagi perusahaan maupun investor. Penelitian mengenai financial distress sudah dimulai dari penelitian Altman pada tahun 1968 menggunakan metode Multiple Discriminant Analysis (MDA). Dimulai dari penelitian Altman, muncul penelitian-penelitian lainnya menggunakan pengembangan metode statistik, seperti Logistic Regression. Dari metode statistik kemudian berkembang dengan munculnya penelitian-penelitian menggunakan metode-metode kecerdasan buatan, serta algoritma evolusi untuk berusaha mendapatkan model prediksi financial distress yang akurat. Tujuan dari penelitian ini adalah untuk membandingkan tingkat akurasi dari model prediksi financial distress perusahaan manufaktur terbuka pada sektor industri barang konsumsi yang terdaftar pada Bursa Efek Indonesia menggunakan metode kecerdasan buatan serta algoritma evolusi. Metode yang digunakan untuk metode kecerdasan buatan adalah metode Support Vector Machines dan untuk model algoritma evolusi menggunakan metode Particle Swarm Optimization-Support Vector Machines. Tingkat akurasi dari masing-masing metode akan diukur dari prosentase misklasifikasi terkecil yang dihasilkan. Dari pengujian model menggunakan metode Support Vector Machines, didapatkan tingkat misklasifikasi terkecil sebesar 11,11% dengan menggunakan Kernel Linear dan untuk metode Particle Swarm Optimization-Support Vector Machines, didapatkan tingkat misklasifikasi terkecil sebesar 5,56% dengan menggunakan Kernel RBF, ? = 2.

scholarly journals Predictors of Financial Distress and Bankruptcy Model Construction

2014 ◽  
Vol 2 (6) ◽  
pp. 34-42 ◽  
Author(s):  
Dana Kubíčková Kubíčková ◽  
Vladimír Nulíček
Keyword(s):  
Discriminant Analysis ◽  
Predictive Models ◽  
Financial Distress ◽  
Model Construction ◽  
Multiple Discriminant Analysis ◽  
Financial Situation ◽  
Transition Economics ◽  
New Model ◽  
Special Regard ◽  
Multivariate Discriminant

The aim of this paper is to prepare the bankruptcy model construction. In the first part, multivariate discriminant analysis and its possibilities in deriving predictive models are characterized. The second part defines the possible indicators/predictors of financial distress of companies, which could be included in the new bankruptcy model. The model itself compares different views of factors that affect the company’s financial situation and contrasts the indicators that were constructed in the model in previous works (with special regard to the models in the transition economics). The result is the collection of 39 indicators to be verified in the next stage of the research project employing the multiple discriminant analysis methods to specify which of them to be included in the new model.

scholarly journals Analisis Diskriminan Untuk Memprediksi Kebangkrutan Perusahaan: Studi Kasus Pada Perusahaan Sektor Textile Dan Garment Di Bursa Efek Indonesia 2016-2018

2021 ◽  
Vol 5 (1) ◽  
pp. 132-140
Author(s):  
Deny Ismanto ◽  
Dyah Ernawati
Keyword(s):  
Discriminant Analysis ◽  
Data Collection ◽  
Discriminant Function ◽  
Financial Distress ◽  
Sampling Technique ◽  
Quantitative Approach ◽  
Financial Ratios ◽  
Collection Method ◽  
Purposive Sampling ◽  
Financial Difficulties

Failure to make a continuous profit will hamper the company's development and this can lead to bankruptcy. Bankruptcy is usually marked by financial distress. Companies that are not able to overcome financial difficulties and problems are getting protracted, then the company will go bankrupt. One way to predict bankruptcy is by using discriminant analysis. This type of research is descriptive with a quantitative approach. The data collection method used is documentation. The population in this study were Textile & Garment companies listed on the IDX in 2016-2018. The sampling technique was purposive sampling and obtained a sample of 8 companies with a potential bankruptcy and 10 companies for 3 years of observation. The results showed that of the 2 financial ratios used Quick Ratio (QR) and Return On Asset (ROA), only the Quick Ratio (QR) ratio proved significant to be able to distinguish bankrupt and non-bankrupt companies. By using discriminant analysis. The dominant variable informing the discriminant function is the Quick Ratio (QR).

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

2022 ◽  
Author(s):  
GOVERNANCE: JURNAL POLITIK LOKAL DAN PEMBANGUNAN
Keyword(s):  
Data Analysis ◽  
Discriminant Analysis ◽  
Real Estate ◽  
Financial Distress ◽  
Stock Exchange ◽  
Accuracy Rate ◽  
Purposive Sampling ◽  
Data Analysis Technique ◽  
Real Estate Sector ◽  
Analysis Technique

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%.

scholarly journals Analysis of Altman Model and Ohlson Model in Predicting Financial Distress of Manufacturing Companies in the Indonesia Stock Exchange

2017 ◽  
Vol 1 (1) ◽  
pp. 51-63
Author(s):  
Elsa Imelda ◽  
Ignacia Alodia
Keyword(s):  
Discriminant Analysis ◽  
Financial Distress ◽  
Stock Exchange ◽  
Logit Analysis ◽  
Net Income ◽  
Manufacturing Companies ◽  
Multiple Discriminant Analysis ◽  
Retained Earnings ◽  
Ohlson Model ◽  
Altman Model

The purpose of this research is to examine the accuracy of the Altman Model and the Ohlson Model in Bankruptcy Prediction.The research population is all companies who are listed on the Indonesian Stock Exchange. The sample of the research is 40 manufacturing companies listed on the Indonesian Stock Exchange in the period of 2010-2014 that are divided into companies with financial distress and those without financial distress.The data analysis technique is the Multiple Discriminant Analysis and Logit Analysis. The Multiple Discriminant Analysis is derived from the Altman Model while the Logit Analysis is derived from theOhlson Model. The results show that the Ohlson Model and the Logit Analysis are more accurate than the Altman Model and the Multiple Discriminant Analysis in predicting bankruptcy of manufacturing firms in the Indonesian Stock Exchange (BEI) in 2010-2014. Also, the results of the study reveal that the ratio of retained earnings to total assets; earning before interest and taxes to total assets; market value of equity to total liabilities; sales to total assets; and debt ratio, return on assets, working capital to total assets and net income were negative in the last two years. Hence constitutes the benchmark for consideration in determining the financial distress of a company.

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