Integrating Neural Networks and Semi-Markov Processes for Automated Knowledge Acquisition: An Application to Real-time Scheduling*

Recently, artificial neural networks (ANN) have gained attention as a promising modeling tool for building intelligent systems. A number of applications have been reported in areas varying from pattern recognition to bankruptcy prediction. In this paper, we present a creative methodology that integrates computer simulation, semi-Markov optimization, and ANN techniques for automated knowledge acquisition in real-time scheduling. The integrated approach focuses on the synergy between operations research and ANN in eliciting human knowledge, filtering inconsistent data, and building competent models capable of performing at the expert level. The new approach includes three main components. First, computer simulation is used to collect expert decisions. This step allows expert knowledge to be obtained in a non-intrusive way and minimizes the difficulties involved in interviewing experts, constructing repertory grids, or using other similar structures required for manual knowledge acquisition. The data collected from computer simulation are then optimized using a semi-Markov decision model to remove data redundancies, inconsistencies, and errors. Finally, the optimized data are used to build ANN-based expert systems. The integrated approach is evaluated by comparing it with the human expert and using ANN alone in the domain of real-time scheduling. The results indicate that ANN-based systems perform worse than human experts from whom the data were collected, but the integrated approach outperforms human experts and ANN models alone.     

Family Control and Stock Market Reactions to Innovation Announcements

Although family firms are common around the world, studies on family‐controlled business are limited. Prior studies mainly focused on the influences of family ownership on overall firm performance, and the results were mixed. In this study we attempted to explore the impacts of family ownership on innovation by examining the association of family control and stock market reactions to innovation announcements. We found that firms with greater family control experienced significantly more negative stock market reactions to innovation announcements. The results further indicated that divergence of cash flow and voting rights was strongly and negatively correlated with announcement‐period abnormal returns. In addition, the findings suggested a significantly positive moderating effect of institutional ownership. The conclusions were robust under various measures of family control, and remained valid after controlling other influential factors for stock market reactions to innovation announcements.     

Ridge regression: A multivariate analysis of criminal activity

The objective of the paper is to apply the statistical procedure of ridge regression to a multivariate model of criminal activity. The explanatory variables are of an economic, apprehension, and seasonal nature. The Time Shared Reactive On Line Laboratory (TROLL) computer package was used in estimating all regression coefficients and other pertinent statistics. The economic variables, which included per capita personal income, hours worked, and plant closing dummy variable, were found to be statistically related to criminal activity. In addition, police expenditures as well as the three seasonal dummy variables were statistically associated with the dependent variable. A comparison is made between the results obtained from the ordinary least squares procedure and the ridge regression approach.     

A two-phase approach to estimating time-varying parameters in the capital asset pricing model

Following the development of the economy and the diversification of investment, mutual funds are a popular investment tool nowadays. Choosing excellent targets from hundreds of mutual funds has become more and more crucial to investors. The capital asset pricing model (CAPM) has been widely used in the capital cost estimation and performance evaluation of mutual funds. In this study, we propose a new two-phase approach to estimating the time-varying parameters of CAPM. We implemented a simulation study to evaluate the efficiency of the proposed method and compared it with the commonly used state space and rolling regression methods. The results showed that the new method is more efficient in most scenarios. Meanwhile, the proposed approach is very practical and it is unnecessary to judge and adjust the estimating process for different situations. Finally, we applied the proposed method to equity mutual funds in the Taiwan stock market and reported the performances of two funds for demonstration.     

The Extensively Corrected Score for Measurement Error Models

In measurement error problems, two major and consistent estimation methods are the conditional score and the corrected score. They are functional methods that require no parametric assumptions on mismeasured covariates. The conditional score requires that a suitable sufficient statistic for the mismeasured covariate can be found, while the corrected score requires that the object score function can be estimated without bias. These assumptions limit their ranges of applications. The extensively corrected score proposed here is an extension of the corrected score. It yields consistent estimations in many cases when neither the conditional score nor the corrected score is feasible. We demonstrate its constructions in generalized linear models and the Cox proportional hazards model, assess its performances by simulation studies and illustrate its implementations by two real examples.