Model Prediksi Financial Distress Perusahaan Real Estate Dan Property Yang Go Public Dengan Metode Binary Logistic (Financial Distress Prediction Model for Go Public-Real Estate and Property Companies Using Binary Logistic Method)

2015 ◽  
Author(s):  
Puput Waryanto
Keyword(s):  
Real Estate ◽  
Prediction Model ◽  
Financial Distress ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Property Companies ◽  
Logistic Method

scholarly journals The Role of Board Independence and Ownership Structure in Improving the Efficacy of Corporate Financial Distress Prediction Model Evidence from India

2021 ◽  
Vol 14 (7) ◽  
pp. 333
Author(s):  
Shilpa H. Shetty ◽  
Theresa Nithila Vincent
Keyword(s):  
Prediction Model ◽  
Ownership Structure ◽  
Financial Distress ◽  
Prediction Models ◽  
Receiver Operating Curve ◽  
Financial Measures ◽  
Financial Variables ◽  
Financial Distress Prediction ◽  
Distress Prediction

The study aimed to investigate the role of non-financial measures in predicting corporate financial distress in the Indian industrial sector. The proportion of independent directors on the board and the proportion of the promoters’ share in the ownership structure of the business were the non-financial measures that were analysed, along with ten financial measures. For this, sample data consisted of 82 companies that had filed for bankruptcy under the Insolvency and Bankruptcy Code (IBC). An equal number of matching financially sound companies also constituted the sample. Therefore, the total sample size was 164 companies. Data for five years immediately preceding the bankruptcy filing was collected for the sample companies. The data of 120 companies evenly drawn from the two groups of companies were used for developing the model and the remaining data were used for validating the developed model. Two binary logistic regression models were developed, M1 and M2, where M1 was formulated with both financial and non-financial variables, and M2 only had financial variables as predictors. The diagnostic ability of the model was tested with the aid of the receiver operating curve (ROC), area under the curve (AUC), sensitivity, specificity and annual accuracy. The results of the study show that inclusion of the two non-financial variables improved the efficacy of the financial distress prediction model. This study made a unique attempt to provide empirical evidence on the role played by non-financial variables in improving the efficiency of corporate distress prediction models.

scholarly journals PREDIKSI FINANCIAL DISTRESS BERDASARKAN VARIABEL-VARIABEL KEUANGAN PADA INDUSTRI TEKSTIL

2018 ◽  
Vol 4 (1) ◽  
pp. 54-60
Author(s):  
REFNI SUKMADEWI
Keyword(s):  
Prediction Model ◽  
Financial Distress ◽  
Z Score ◽  
Financial Distress Prediction ◽  
Distress Prediction

Penelitian ini bertujuan untuk memberikan bukti empiris mengenai faktor-faktor yang mempengaruhi financial distress perusahaan. Penelitian ini menguji peran rasio keuangan dalam memprediksi terjadinya financial distress pada perusahaan industri tekstil yang tercatat di Bursa Efek Jakarta. Analisis diskriminan digunakan untuk menguji kemampuan rasio keuangan untuk memprediksi financial distress dan membangun model prediksi distress financial dengan menggunakan prosedur stepwise. Variabel indikator adalah rasio keuangan. Hasil penelitian menunjukkan bahwa ada empat rasio yang berbeda dan secara signifikan mempengaruhi model prediksi distress keuangan. Rasio tersebut adalah Rasio Aktiva Lancar / Kewajiban Lancar, Modal Kerja / Jumlah Aktiva, Pendapatan Bersih / Total Aktiva, Kewajiban / Total Aset. Hasil klasifikasi berdasarkan nilai cut-off-Z Score mampu memprediksi kesulitan finansial perusahaan pada industri tekstil dengan tingkat akurasi 0f 100%. Tingkat akurasi model menunjukkan bahwa model diskriminan akurat dalam mengukur tekanan keuangan pada perusahaan industri tekstil. Kata kunci: Financial distress, Prediction Model, Rasio Keuangan

scholarly journals Financial Distress Prediction Using Hybrid Machine Learning Techniques

2020 ◽  
pp. 1-12
Author(s):  
Suduan Chen ◽  
Zong-De Shen
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Financial Distress ◽  
Machine Learning Techniques ◽  
Financial Variables ◽  
Financial Distress Prediction ◽  
Learning Techniques ◽  
Distress Prediction ◽  
Hybrid Machine ◽  
Financially Distressed

The purpose of this study is to establish an effective financial distress prediction model by applying hybrid machine learning techniques. The sample set is 262 financially distressed companies and 786 non-financially distressed companies, listed on the Taiwan Stock Exchange between 2012 and 2018. This study deploys multiple machine learning techniques. The first step is to screen out important variables with stepwise regression (SR) and the least absolute shrinkage and selection operator (LASSO), followed by the construction of prediction models, as based on classification and regression trees (CART) and random forests (RF). Both financial variables and non-financial variables are incorporated. This study finds that the financial distress prediction model built with CART and variables screened by LASSO has the highest accuracy of 89.74%.

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