AIRCC's International Journal of Computer Science and Information Technology

Ming-Chang Lee ¹, Li-Er Su ²

Affiliations
1 National Kaohsiung University of Applied Sciences, TW
2 Shih Chien University, Kaohsiung Campus, Kaohsiung, TW

Abstract

Enterprise financial distress or failure includes bankruptcy prediction, financial distress, corporate performance prediction and credit risk estimation. The aim of this paper is that using wavelet networks in non-linear combination prediction to solve ARMA (Auto-Regressive and Moving Average) model problem. ARMA model need estimate the value of all parameters in the model, it has a large amount of computation. Under this aim, the paper provides an extensive review of Wavelet networks and Logistic regression. It discussed the Wavelet neural network structure, Wavelet network model training algorithm, Accuracy rate and error rate (accuracy of classification, Type I error, and Type II error). The main research opportunity exist a proposed of business failure prediction model (wavelet network model and logistic regression model). The empirical research which is comparison of Wavelet Network and Logistic Regression on training and forecasting sample, the result shows that this wavelet network model is high accurate and the overall prediction accuracy, Type I error and Type II error, wavelet networks model is better than logistic regression model.

Keywords

Wavelet Networks, Logistic Regression, Business Failure Prediction, Type I error, Type II error.

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Ming-Chang Lee ¹

Affiliations
1 National Kaohsiung University of Applied Science, TW

Abstract

This study sampled companies listed on Taiwan Stock Exchange that examined financial distress between 2003 and 2009. It uses the survival analysis to find the main indicators which can explain the business bankruptcy in Taiwan. This paper uses the Cox Proportional Hazard Model to assess the usefulness of traditional financial ratios and market variables as predictors of the probability of business failure to a given time. This paper presents empirical results of a study regarding 12 financial ratios as predictors of business failure in Taiwan. It showed that it does not need many ratios to be able to anticipate potential business bankruptcy. The financial distress probability model is constructed using Profitability, Leverage, Efficiency and Valuation ratio variables. In the proposed steps of business failure prediction model, it used detail SAS procedure. The study proves that the accuracies of classification of the mode in overall accuracy of classification are 87.93%.

Keywords

Business Failure prediction, Survival Analysis, Cox Proportional Hazard model, Logistic model.

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Ming-Chang Lee ¹

Affiliations
1 National Kaohsiung University of Applied Science, TW

Abstract

Information security risk analysis becomes an increasingly essential component of organization's operations. Traditional Information security risk analysis is quantitative and qualitative analysis methods. Quantitative and qualitative analysis methods have some advantages for information risk analysis. However, hierarchy process has been widely used in security assessment. A future research direction may be development and application of soft computing such as rough sets, grey sets, fuzzy systems, generic algorithm, support vector machine, and Bayesian network and hybrid model. Hybrid model are developed by integrating two or more existing model. A Practical advice for evaluation information security risk is discussed. This approach is combination with AHP and Fuzzy comprehensive method.

Keywords

Information Security Risk Analysis, Quantitative Risk Assessment Methods, Qualitative Risk Assessment Method, Analytical Hierarchy Process, Soft Computing.

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