Neural networks and the mathematics of chaos-an investigation of these methodologies as accurate predictors of corporate bankruptcy

Cadden, David

doi:10.1109/aiaws.1991.236574

Cited by 10 publications

(6 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Predominantly, papers in this area have compared an Artificial Neural Network (ANN) with a published MDA model of failure prediction. [Odom and Sharda, 1990;Koster, Sandak and Bourbia, 1990;Cadden, 1991;Coats and Fant, 1992;Lee, Han and Kwon, 1996]. Exceptions are Fletcher and Goss [1993], and Udo [1993], who compare a neural network with a logit model of bankruptcy.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Description, explanation, prediction – the evolution of bankruptcy studies?

Cybinski

2001

Managerial Finance

View full text Add to dashboard Cite

This paper describes a number of models used in bankruptcy studies to date. They arise from two basic model designs used in studies of financial distress: cross-sectional studies that compare healthy and distressed firms, and time-series formulations that study the path to failure of (usually) distressed firms only. These two designs inherently foster different research objectives. Different instances of the most recent work taken from each of the above research groups, broadly categorized by design, are described here including new work by this author. It is argued that those that investigate the distress continuum with predominantly explanatory objectives are superior on a number of criteria to the studies that follow what is essentially a case-control structure and espouse prediction as their objective.

show abstract

Section: Artificial Neural Networkmentioning

confidence: 99%

Description, explanation, prediction – the evolution of bankruptcy studies?

Cybinski

2001

Managerial Finance

View full text Add to dashboard Cite

show abstract

“…Mensah, 1983) developed models combining logistic regression and multidimensional discriminant analysis. At the beginning of the 1990s, Odom and Sharda (1990), Bell et al (1990), Koster, Sondak and Bourbia (1990), Cadden, 1991) and many others proposed the concept of artificial neural networks (ANN) based bankruptcy prediction models for various industries.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Bankruptcy of Slovak Companies Using Neural Networks with SMOTE

Tumpach¹,

Surovičová²,

Juhászová³

et al. 2020

ekoncas

View full text Add to dashboard Cite

Although the bankruptcy prediction models can be a stabilizing element on both macro and microeconomic levels, they are rather a domain of academic research than an instrument, widely applied in a business practice. It is especially true if the models are reflecting the conditions of countries of their origin, rather than countries of their intended uses. Besides, few of the models contain inherent flaws, including the absence of a methodical approach addressing this problem of the severely imbalanced representation of bankrupt companies in financial datasets. The article is focused on the use of oversampling with SMOTE (Synthetic Minority Oversampling Technique) algorithm under the condition of extremely imbalanced data sets of Slovak companies. While the model does not provide a single answer in many (if not most) of the situations, it still could be used for the selection of companies for which the more detailed (and expensive) analysis is not required.

show abstract

“…Existing models in this field are not generally comprehensive in some respects and focus only on the information and statistical results as well as common ratios in the business management (Klofsten 2010), such as the failure model and bankruptcy that uses limited parameters (Altman 1968(Altman , 1977Cadden 1991;Ain 1997). The complexity of the existing models makes it difficult for SME managers to use them (Klofsten 2010).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of trade and production policy in Iranian SME (a system dynamics model)

2019

View full text Add to dashboard Cite

It is undeniable that SMEs face many management-related problems. These problems are often deeply rooted in the strategic decision making by managers. One of these decisions is associated with the production section. Many of these companies provide production infrastructure at high costs; however, they are unsuccessful in acquiring their market share. In these circumstances, providing a solution to the decision-making problem of managers in these companies can prevent the loss of capital. In this research, the system dynamics approach is used to test the hypothesis of a company's participation in the production field after securing a success in business activities of the market. In the present study, the SME performance model, proposed by Schmid's, is considered as the base model. After analyzing its construction steps according to managers' viewpoints, the model has been developed based on the conditions in Iran. Then, the research scenario is presented as follows. If an entrepreneur, before entering the production sector, tries to establish a customer network by conducting business activities, the organization's chance for success will increase. By defining a variable called "production switch" and adding it as a function of a loyal customer network, the model has been tested. After simulating the model for a 5-year period, it is concluded that the rate of return on investment of an organization that has conducted the commercial activities at the outset of production is four times higher than that at the outset of production without the commercial activity.

show abstract

Neural networks and the mathematics of chaos-an investigation of these methodologies as accurate predictors of corporate bankruptcy

Cited by 10 publications

References 0 publications

Description, explanation, prediction – the evolution of bankruptcy studies?

Description, explanation, prediction – the evolution of bankruptcy studies?

Prediction of the Bankruptcy of Slovak Companies Using Neural Networks with SMOTE

Evaluation of trade and production policy in Iranian SME (a system dynamics model)

Contact Info

Product

Resources

About