Financial Distress Prediction Model with the Incorporation of Soft Information

2010 ◽  
Author(s):  
Yang-Cheng Lu ◽  
Yu-Chen Wei
Keyword(s):  
Prediction Model ◽  
Financial Distress ◽  
Soft Information ◽  
Financial Distress Prediction ◽  
Distress Prediction

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%.

scholarly journals Ensemble Model of the Financial Distress Prediction in Visegrad Group Countries

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1886
Author(s):  
Michal Pavlicko ◽  
Marek Durica ◽  
Jaroslav Mazanec
Keyword(s):  
Prediction Model ◽  
Central Europe ◽  
Financial Distress ◽  
Prediction Models ◽  
Value Added ◽  
Ensemble Model ◽  
Financial Distress Prediction ◽  
Proposed Model ◽  
Distress Prediction ◽  
Visegrad Group

The issue of prediction of financial state, or especially the threat of the financial distress of companies, is very topical not only for the management of the companies to take the appropriate actions but also for all the stakeholders to know the financial health of the company and its possible future development. Therefore, the main aim of the paper is ensemble model creation for financial distress prediction. This model is created using the real data on more than 550,000 companies from Central Europe, which were collected from the Amadeus database. The model was trained and validated using 27 selected financial variables from 2016 to predict the financial distress statement in 2017. Five variables were selected as significant predictors in the model: current ratio, return on equity, return on assets, debt ratio, and net working capital. Then, the proposed model performance was evaluated using the values of the variables and the state of the companies in 2017 to predict financial status in 2018. The results demonstrate that the proposed hybrid model created by combining methods, namely RobustBoost, CART, and k-NN with optimised structure, achieves better prediction results than using one of the methods alone. Moreover, the ensemble model is a new technique in the Visegrad Group (V4) compared with other prediction models. The proposed model serves as a one-year-ahead prediction model and can be directly used in the practice of the companies as the universal tool for estimation of the threat of financial distress not only in Central Europe but also in other countries. The value-added of the prediction model is its interpretability and high-performance accuracy.

Sign in / Sign up

Export Citation Format

Share Document