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The past researches emphasize merely the avoidance of over-learning at the system level and ignore the problem of over-learning at the model level, which lead to the poor performance of the evolutionary computation based stock trading decision-making system. This study presents a new evaluation approach to focus on evaluating the generalization capability at the model level. An empirical study was provided and the results reveal four important findings. First, the decision-making system generated at the model design stage outperforms the system generated at the model validation stage, which shows over-learning at the model level. Secondly, for the decision-making system generated either at the model design stage or at the model validation stage, the investment performance in the training period is much better than that in the testing period, exhibiting over-learning at the system level. Third, employing moving timeframe approach is unable to improve the investment performance at the model validation stage. Fourth, reducing the evolution generation and input variables are unable to avoid the over-learning at the model level. The major contribution of this study is to clarify the issue of over-learning at the model and the system level. For future research, this study developed a more reliable evaluation approach in examining the generalization capability of evolutionary computation based decision-making system.

The research objective is to propose a novel association analysis approach using association reasoning neural networks (ARNN) to discover the association rules from cosmetics purchasing. ARNN is modified from multi-layered perceptron and back-propagation algorithm. The number of association rules is controlled by the rule threshold and the number of hidden units. To explore the possibility of producing useful and meaningful association rules using ARNN, our study uses the practical cosmetics transaction data. The results show (1) the predicted output values of ARNN are close to their desired confidence values, (2) reducing the number of hidden units of ARNN can inhibit the generation of association rules with low support, and (3) ARNN has the ability of discovering the cohesion and expansion commodities and this information could be used to make pricing strategy. Therefore, ARNN could be a promising alternative approach for association analysis.

This research aimed at the case of customers' default payments in Taiwan and compares the predictive accuracy of probability of default among six data mining methods. From the perspective of risk management, the result of predictive accuracy of the estimated probability of default will be more valuable than the binary result of classification - credible or not credible clients. Because the real probability of default is unknown, this study presented the novel ''Sorting Smoothing Method'' to estimate the real probability of default. With the real probability of default as the response variable (Y), and the predictive probability of default as the independent variable (X), the simple linear regression result (Y=A+BX) shows that the forecasting model produced by artificial neural network has the highest coefficient of determination; its regression intercept (A) is close to zero, and regression coefficient (B) to one. Therefore, among the six data mining techniques, artificial neural network is the only one that can accurately estimate the real probability of default.

Our study investigates the generalization capability of fuzzy rule-based trading systems and conducted an empirical study on Taiwan's stock market during 1991/1/1–2010/12/31. The empirical conclusions are as follows (1) The investment performance in the training period is much better than that in the testing period, which exhibits over-learning. (2) The symmetry rules approach demonstrates that the pure price trend trading system is better than the pure volume trend trading system and hybrid price-volume trend trading system. The pure volume trend trading system is the poorest system. (3) The asymmetry rules approach demonstrates that the trading system optimized in one period may be performed poorer in another period. The pure price trend buy-rule combined with the pure volume trend sell-rule has good performance in 1991–2000 but acts poorly in 2001–2010.

Information asymmetry is a major problem in the ascending online auction. This research conducted two studies to investigate the impacts of information (positive or negative) and its acquiring cost on information premium and final price. In study 1, 113 undergraduates were randomly separated into two groups. Group 1 was given positive information (i.e., a report showing the strong demand for MP3) and group 2 was given negative information (i.e., a report showing MP3 has a defect in its configuration). The test result indicated that there is a significant difference in the information premium resulting from positive and negative information. In study 2, the test outcome showed that, no matter what information (positive or negative) was given, high acquiring cost will lead to a relatively higher final price, compared to low or zero information acquiring cost.