Bankruptcy Prediction for Chinese Firms： Comparing Data Mining Tools With Logit Analysis

China＇s capital market is different from that of the US in economic, political, and socio-cultural ways. China＇s dynamic and fast growing economy for the past decade entails some structural changes and weaknesses and as a consequence, there are some business failures. We propose bankruptcy prediction models using Chinese firm data via several data mining tools and traditional logit analysis. We used Chinese firm data one year prior to bankruptcy and our results suggest that the financial variables developed by Altman （1968） and Ohlson （1980） perform reasonably well in determining business failures of Chinese firms, but the overall prediction rate is low compared with those of the US or other countries＇ studies. The reasons for this low prediction rate may be structural weaknesses resulting from China＇s fast growth and immature capital market.     

Bankruptcy prediction for Korean firms after the 1997 financial crisis: using a multiple criteria linear programming data mining approach

The main purpose of this paper is to evaluate the data mining applications, such as classification, which have been used in previous bankruptcy prediction studies and credit rating studies. Our study proposes a multiple criteria linear programming (MCLP) method to predict bankruptcy using Korean bankruptcy data after the 1997 financial crisis. The results, of the MCLP approach in our Korean bankruptcy prediction study, show that our method performs as well as traditional multiple discriminant analysis or logit analysis using only financial data. In addition, our model??s overall prediction accuracy is comparable to those of decision tree or support vector machine approaches. However, our results are not generalizable because our data are from a special situation in Korea.     

Human Resource Allocation in a CPA Firm: A Fuzzy Set Approach

The review of existing human resource allocation models for a CPA firm shows that there are major shortcomings in the previous mathematical models. First, linear programming models cannot handle multiple objective human resource allocation problems for a CPA firm. Second, goal programming or multiple objective linear programming (MOLP) cannot deal with the organizational differentiation problems. To reduce the complexity in computing the trade-offs among multiple objectives, this paper adopts a fuzzy set approach to solve human resource allocation problems. A solution procedure is proposed to systematically identify a satisfying selection of possible staffing solutions that can reach the best compromise value for the multiple objectives and multiple constraint levels. The fuzzy solution can help the CPA firm make a realistic decision regarding its human resource allocation problems as well as the firm's overall strategic resource management when environmental factors are uncertain.     

Capital budgeting with multiple criteria and multiple decision makers

In this article, we propose a model that incorporates the preferences of multiple decision makers into a decison-making process using (1) The analytical hierarchy process (AHP); and (2) multiple criteria and multiple constraint levels (MC²) linear programming in a capital budgeting context. Our model can foster strategic and nonfinancial factors that are important in the capital budgeting problems of the current business environment. The two-phased solution framework proposed in this article is sufficiently flexible to reach a compromise among decision makers. Our method also facilitates collection of decision makers' preferences to minimize suboptimization of overall company's goals. In addition, application of AHP to derive weights to decision makers' preferences for resource availability decreases the solution complexity. All these characteristics represent a significant improvement compared with previous linear or goal programming approaches to capital budgeting problems.     

Japanese Corporate Groupings (Keiretsu) and the Characteristics of Analysts' Forecasts

This study examines differences in the characteristics of analysts' forecasts of earnings for keiretsu and non-keiretsu firms in Japan. Japanese industrial organization is characterized by enterprise groupings, keiretsu, composed of firms in different industries, but interrelated through cross-holdings of ownership. The strong interrelations of the Japanese keiretsu have been shown to increase the monitoring of managerial performance. An alternative view of keiretsu stewardship suggests the exclusionary environment within a keiretsu creates an information monopoly, resulting in greater information asymmetry between inside and outside constituents. These two views provide opposing predictions on how keiretsu groupings potentially affect the characteristics of earnings forecasts made by analysts: increased monitoring will improve the forecast characteristics of earnings, while the opposite is true under an information monopoly.Our results suggest that forecast accuracy (dispersion) is higher (lower) for keiretsu firms than non-keiretsu firms, supporting a monitoring role by keiretsu. The results also show that keiretsu firms' forecast characteristics are related to the strength of the keiretsu relationship, providing further evidence that it is indeed the keiretsu relationship that increases the monitoring of management, which ultimately improves the accuracy and dispersion of analysts' forecast.