3,503 research outputs found
Bankruptcy Prediction of Small and Medium Enterprises Using a Flexible Binary Generalized Extreme Value Model
We introduce a binary regression accounting-based model for bankruptcy
prediction of small and medium enterprises (SMEs). The main advantage of the
model lies in its predictive performance in identifying defaulted SMEs. Another
advantage, which is especially relevant for banks, is that the relationship
between the accounting characteristics of SMEs and response is not assumed a
priori (e.g., linear, quadratic or cubic) and can be determined from the data.
The proposed approach uses the quantile function of the generalized extreme
value distribution as link function as well as smooth functions of accounting
characteristics to flexibly model covariate effects. Therefore, the usual
assumptions in scoring models of symmetric link function and linear or
pre-specied covariate-response relationships are relaxed. Out-of-sample and
out-of-time validation on Italian data shows that our proposal outperforms the
commonly used (logistic) scoring model for different default horizons
The Prediction of Corporate Bankruptcy and Czech Economy’s Financial Stability through Logit Analysis
This article presents a financial scoring model estimated on Czech corporate accounting data. Seven financial indicators capable of explaining business failure at a 1-year prediction horizon are identified. Using the model estimated in this way, an aggregate indicator of the creditworthiness of the Czech corporate sector (named as JT index) is then constructed and its evolution over time is shown. This indicator aids the estimation of the risks of this sector going forward and broadens the existing analytical set-up used by the Czech National Bank for its financial stability analyses. The results suggest that the creditworthiness of the Czech corporate sector steadily improved between 2004 and 2006, but slightly deteriorated in 2007 what could be explained through global market turbulences.bankruptcy prediction, financial stability, logit analysis, corporate sector risk, JT index
Hybrid model using logit and nonparametric methods for predicting micro-entity failure
Following the calls from literature on bankruptcy, a parsimonious hybrid bankruptcy model is developed in this paper
by combining parametric and non-parametric approaches.To this end, the variables with the highest predictive power to
detect bankruptcy are selected using logistic regression (LR). Subsequently, alternative non-parametric methods
(Multilayer Perceptron, Rough Set, and Classification-Regression Trees) are applied, in turn, to firms classified as
either “bankrupt” or “not bankrupt”. Our findings show that hybrid models, particularly those combining LR and
Multilayer Perceptron, offer better accuracy performance and interpretability and converge faster than each method
implemented in isolation. Moreover, the authors demonstrate that the introduction of non-financial and macroeconomic
variables complement financial ratios for bankruptcy prediction
Improving bankruptcy prediction in micro-entities by using nonlinear effects and non-financial variables
The use of non-parametric methodologies, the introduction of non-financial variables,
and the development of models geared towards the homogeneous characteristics of
corporate sub-populations have recently experienced a surge of interest in the bankruptcy
literature. However, no research on default prediction has yet focused on micro-entities
(MEs), despite such firms’ importance in the global economy. This paper builds the first
bankruptcy model especially designed for MEs by using a wide set of accounts from 1999
to 2008 and applying artificial neural networks (ANNs). Our findings show that ANNs
outperform the traditional logistic regression (LR) models. In addition, we also report
that, thanks to the introduction of non-financial predictors related to age, the delay
in filing accounts, legal action by creditors to recover unpaid debts, and the ownership
features of the company, the improvement with respect to the use of solely financial
information is 3.6%, which is even higher than the improvement that involves the use
of the best ANN (2.6%)
Generalized Extreme Value Regression for Binary Rare Events Data: an Application to Credit Defaults
The most used regression model with binary dependent variable is the logistic regression model. When the dependent variable represents a rare event, the logistic regression model shows relevant drawbacks. In order to overcome these drawbacks we propose the Generalized Extreme Value (GEV) regression model. In particular, in a Generalized Linear Model (GLM) with binary dependent variable we suggest the quantile function of the GEV distribution as link function, so our attention is focused on the tail of the response curve for values close to one. The estimation procedure is the maximum likelihood method. This model accommodates skewness and it presents a generalization of GLMs with log-log link function. In credit risk analysis a pivotal topic is the default probability estimation. Since defaults are rare events, we apply the GEV regression to empirical data on Italian Small and Medium Enterprises (SMEs) to model their default probabilities.
Banking’s implications on Romanian’s business environment in context of global financial crisis
The Romanian banking system is on the edge of becoming a mature one. Even if the last year represented a decrease of its profitability, the chance to be closer to the Western banking systems is still on the horizon. The main problem for the Romanian banks in 2010 is the government’s and central bank’s strategic vision on economic development. In our opinion, the main problems facing companies in our country are: late payments from customers or partners, declining demand, the depreciation of our currency, company liquidity, cash and inflation. For 2011, the resumption of economic growth is influenced by central bank monetary policy together with other components of overall economic policy. The paper addresses the impact of global financial crisis on business in our country and presents some considerations lines of action aimed at companies to adjust their costs.global financial crisis, trend, business environment
Modeling SMEs Credit Default Risk: The Case of Saudi Arabia
This study assesses the credit risk of small and medium-sized enterprises (SMEs) to minimize unexpected risk events. We construct a hybrid statistical model based on factor analysis and logistic regression to predict enterprise default on loans and determine the factors predicting SMEs default. We assess the credit risk of SMEs listed on the Saudi stock market. The results indicate that the SMEs acid-test ratios are the most influential factors in predicting SMEs credit risk. Therefore, the designed logistic model can be used by financial institutions during the decision-making process of granting loans to SMEs. This study sheds light on challenging access to bank credits due to the lack of financial transparency of most Saudi SMEs
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