scholarly journals The Business Failure Prediction Using Statistical Approach

2021 ◽  
pp. 161-168
Author(s):  
Handrie Noprisson
Keyword(s):  
Statistical Approach ◽  
Failure Prediction ◽  
Business Failure ◽  
Support Vector ◽  
Data Envelopment ◽  
Financial Indicators ◽  
Failure Predictions ◽  
Score Model ◽  
Meta Analyses ◽  
A Company

The topic of business failure is important because it can be used as a basis for policy making by stakeholders in a company or government. The results of business failure predictions can be used as company managers to take preventive measures for business failure. This study aim is to study the literature regarding the methods and results of predicting business failure from various sectors and regions. We used PRISMA (preferred reporting items for systematic reviews and meta-analyses) for conducting this research. As the result, we found twelve statistical methods for business failure prediction, including Hybrid Failure Prediction (HFP), Altman’s Z-score Model, Data Envelopment Analysis (DEA), Logistic Regression (LR), Neural Networks (NN), Support Vector Machine (SVM), Kernel Fuzzy C-Means (KFCM), IN01, IN05, Ohlson Model, Cart-Based Model and Cash-Flow-Based Measures. The highest result obtained by using cart-based model for dataset of financial indicators of Slovak companies with 92,00% accuracy.

Investigation of Credit Risk based on Indian Firm Performance

2017 ◽  
Vol 6 (2) ◽  
pp. 35-46
Author(s):  
Manoj Kumar
Keyword(s):  
Regression Models ◽  
Explanatory Power ◽  
Business Failure ◽  
Data Envelopment ◽  
Financial Indicators ◽  
Logit Regression ◽  
Parametric Data ◽  
Efficiency Measures ◽  
Ex Ante ◽  
Indian Firm

In this paper the author investigates whether technical efficiency is an important ex-ante predictor of business failure. The author uses samples of Indian textiles, wood, computers and related activities and R&D companies to obtain efficiency estimates for individual firms in each industry. These efficiency measures are derived from a directional technology distance function constructed empirically using non-parametric Data Envelopment Analysis (DEA) methods. Estimating standard probit and logit regression models the author finds that efficiency has significant explanatory power in predicting the likelihood of default over and above the effect of standard financial indicators.

Evaluating Business Performance Using Data Envelopment Analysis and Grey Relational Analysis

2021 ◽  
pp. 115-148
Author(s):  
Tihana Škrinjarić ◽  
Boško Šego
Keyword(s):  
Data Envelopment Analysis ◽  
Grey Relational Analysis ◽  
Business Performance ◽  
Business Failure ◽  
Special Focus ◽  
Data Envelopment ◽  
Relational Analysis ◽  
Using Data ◽  
Grey Relational ◽  
A Company

Financial ratios are used in a variety of ways today. Empirical research is getting bigger, with a special focus on predicting business failure, the strength of a company, investment decision making, etc. This chapter focuses on two methodologies suitable to deal with many data to evaluate business performance. They are data envelopment analysis and grey relational analysis. The empirical part of the chapter conducts an empirical analysis with the aforementioned two approaches. Firms are ranked based on their performances and detailed interpretations are obtained so that managers within businesses can get useful information on how to utilize such an approach to modelling. This study implicates that using the two mentioned approaches can be useful when making investment decisions based on many data available for the decision maker. This is due to the methodology being suitable to handle big data and correctly quantifying the overall financial performance of a company.

BUSINESS FAILURE PREDICTION USING ROUGH SETS: A COMPARISON WITH MULTIVARIATE ANALYSIS TECHNIQUES

1999 ◽  
Vol 04 (01) ◽  
Author(s):  
C. Zopounidis ◽  
M. Doumpos ◽  
R. Slowinski ◽  
R. Susmaga ◽  
A. I. Dimitras
Keyword(s):  
Multivariate Analysis ◽  
Rough Sets ◽  
Failure Prediction ◽  
Business Failure ◽  
Analysis Techniques

Comparison of SVM, RF and SGD Methods for Determination of Programmer's Performance Classification Model in Social Media Activities

2020 ◽  
Vol 4 (2) ◽  
pp. 329-335
Author(s):  
Rusydi Umar ◽  
Imam Riadi ◽  
Purwono
Keyword(s):  
Social Media ◽  
Gradient Descent ◽  
Classification Model ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Svm Algorithm ◽  
Vector Machines ◽  
Performance Patterns ◽  
A Company

The failure of most startups in Indonesia is caused by team performance that is not solid and competent. Programmers are an integral profession in a startup team. The development of social media can be used as a strategic tool for recruiting the best programmer candidates in a company. This strategic tool is in the form of an automatic classification system of social media posting from prospective programmers. The classification results are expected to be able to predict the performance patterns of each candidate with a predicate of good or bad performance. The classification method with the best accuracy needs to be chosen in order to get an effective strategic tool so that a comparison of several methods is needed. This study compares classification methods including the Support Vector Machines (SVM) algorithm, Random Forest (RF) and Stochastic Gradient Descent (SGD). The classification results show the percentage of accuracy with k = 10 cross validation for the SVM algorithm reaches 81.3%, RF at 74.4%, and SGD at 80.1% so that the SVM method is chosen as a model of programmer performance classification on social media activities.

Financial ratio selection for business failure prediction using soft set theory

2014 ◽  
Vol 63 ◽  
pp. 59-67 ◽  
Author(s):  
Wei Xu ◽  
Zhi Xiao ◽  
Xin Dang ◽  
Daoli Yang ◽  
Xianglei Yang
Keyword(s):  
Set Theory ◽  
Failure Prediction ◽  
Soft Set ◽  
Business Failure ◽  
Financial Ratio ◽  
Selection For
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