The author systematically examines one of the important issues of information — establishing the market price. He introduces the concept of «search» — where a buyer wanting to get a better price, is forced to question sellers. The article deals with various aspects of finding the necessary information.

Economics of Information

Bankruptcy prediction in banks and firms via statistical and intelligent techniques – A review

Bankruptcy prediction has drawn a lot of research interests in previous literature, and recent studies have shown that machine learning techniques achieved better performance than traditional statistical ones. This paper applies support vector machines (SVMs) to the bankruptcy prediction problem in an attempt to suggest a new model with better explanatory power and stability. To serve this purpose, we use a grid-search technique using 5-fold cross-validation to find out the optimal parameter values of kernel function of SVM. In addition, to evaluate the prediction accuracy of SVM, we compare its performance with those of multiple discriminant analysis (MDA), logistic regression analysis (Logit), and three-layer fully connected back-propagation neural networks (BPNs). The experiment results show that SVM outperforms the other methods.

Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters

Neural networks are being used in areas of prediction and classification, the areas where statistical methods have traditionally been used. Both the traditional statistical methods and neural networks are looked upon as competing model-building techniques in literature. This paper carries out a comprehensive review of articles that involve a comparative study of feed forward neural networks and statistical techniques used for prediction and classification problems in various areas of applications. Tabular presentations highlighting the important features of these articles are also provided. This study aims to give useful insight into the capabilities of neural networks and statistical methods used in different kinds of applications.

Review: Neural networks and statistical techniques: A review of applications

This study investigates the efficacy of applying support vector machines (SVM) to bankruptcy prediction problem. Although it is a well-known fact that the back-propagation neural network (BPN) performs well in pattern recognition tasks, the method has some limitations in that it is an art to find an appropriate model structure and optimal solution. Furthermore, loading as many of the training set as possible into the network is needed to search the weights of the network. On the other hand, since SVM captures geometric characteristics of feature space without deriving weights of networks from the training data, it is capable of extracting the optimal solution with the small training set size. In this study, we show that the proposed classifier of SVM approach outperforms BPN to the problem of corporate bankruptcy prediction. The results demonstrate that the accuracy and generalization performance of SVM is better than that of BPN as the training set size gets smaller. We also examine the effect of the variability in performance with respect to various values of parameters in SVM. In addition, we investigate and summarize the several superior points of the SVM algorithm compared with BPN.

http://svms.org/finance/ShinLeeKim2005.pdf

An application of support vector machines in bankruptcy prediction model

Bankruptcy prediction is one of the major business classification problems. 1n this paper, we use three different techniques: (1) Multivariate discriminant analysis, (2) case-based forecasting, and (3) neural network to predict Korean bankrupt and nonbankrupt firms. The average hit ratios of three methods range from 81.5 to 83.8%. Neural network performs better than discriminant analysis and the case-based forecasting system.

Bankruptcy prediction using case-based reasoning, neural networks, and discriminant analysis

The relationship between retailer-hosted and third-party hosted WOM sources and their influence on retailer sales

The impact of psychological distance on Chinese customers when selecting an international healthcare service country.

As the competition among hospitals become intensified, hospital management is required to carry out more effective marketing and positioning of the hospital. Successful positioning of a hospital requires the knowledge about how the concrete attributes inherent in the medical service associated with the customer values that customers seek eventually in the medical service. Thus, it is required for hospital management to understand which hospital attributes should be emphasized in order to improve the customer values. The hierarchical structure of service attributes can provide valuable information about effective positioning and advertising. To obtain such knowledge, we employed the order analysis technique as an objective means-end chain method. Order analysis is useful for identifying the causal structure among attributes. Thus, we can examine the underlying causal relationship and eventually the vertical structure of hospital selection attributes. For this study, we conducted a survey to obtain 370 responses for the analysis. The result suggests that hospital management had better increase the scale of hospital, improve the services of health care providers, and modernize the hospital facilities and equipments so as to enhance customer values and eventually to lead them to recommend the hospital to others. Our findings would provide the valuable information for hospital management to develop the more effective hospital positioning and marketing strategies.

Hoonyoung Lee

Papers

Bankruptcy prediction using case-based reasoning, neural networks, and discriminant analysis

The relationship between retailer-hosted and third-party hosted WOM sources and their influence on retailer sales

The impact of psychological distance on Chinese customers when selecting an international healthcare service country.

An Hierarchical Structure Analysis of Hospital Selection Attributes