Integrated Methodology of Grey Prediction, Artificial Bee Colony Based Clustering Method and Rough Set: A Prediction of Business Distress

• Main Authors: Lai-lin Chiang, 江來霖
• Other Authors: Jao-hong Cheng
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/06827458240003756944

碩士 === 國立雲林科技大學 === 資訊管理系碩士班 === 99 === Forecast the company''s financial crisis has been an important academic issue, aimed at the prevention of listed companies unexpected bankruptcy caused significant loss of social costs. Traditional statistical methods of forecasting models constructed by the statistical assumptions of the request are limited, and therefore derivative new algorithms by imitation of biological behavior in recent years have been proposal. In this study, first apply gray prediction for historical data and generate predictive value, compared to only consider the traditional of the cross-sectional message financial data, more vertical-section of time trend of dynamic to join consider. Second, the use of a new artificial bee colony based clustering algorithm will replace with previous clustering method to group forecast value by homogeneous. Finally, use rough set theory dealing with data fuzzy regional and deriving decision rules, and then did the classification of the predicted value. In this study, integrated gray prediction, artificial bee colony based clustering algorithms and rough set theory based on past historical data construct an enterprise''s financial crisis early-warning model. Sample of listed companies in Taiwan to extract the period of 66 to 100 years, deduct of the bankruptcy crisis is not consistent out to 57 companies, and select the scale and assets corresponding to the normal company, the final capture of these two types of each company financial data for the time window before the crisis occurred date seven-year. The empirical results show that the proposed model of early warning crisis better than other accuracy of combination model, and the highest accuracy rate of predict on the previous year.