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This research examines stock traders' disposition effects and contrarian/momentum behavior in the Taiwan Stock Exchange (TWSE). Specifically, we first investigate disposition effects across all trader types and then examine the relationships between disposition effects, trader types, and order characteristics. Next, we explore contrarian and/or momentum behavior and analyze the relationships among the contrarian/momentum behavior, investor type, and order characteristics. Finally, the links among trader types, order characteristics, and investment performance are detected. This chapter yields the following findings. (1) Individual investors exhibit the strongest disposition effects compared to other investors. (2) Foreign investors, investment trusts, and individual investors tend to use large orders to sell loser stocks. (3) Investment trusts are inclined to be momentum traders, while individual investors tend to perform contrarian strategies. (4) Institutional aggressive and large orders perform better than individuals' orders. (5) The performance of foreign investors' selling decisions is better than that of retail investors.

The purpose of this paper is to design an innovative performance modeling system by jointly using data envelopment analysis (DEA) and artificial neural network (ANN). The hybrid DEA-ANN model integrates performance measurement and prediction frameworks and serves as an adaptive decision support tool in pursuit of best performance benchmarking and stepwise improvement.

Advantages of combining DEA and ANN methods into an optimal performance prediction model are explored. DEA is used as a preprocessor to measure relative performance of decision-making units (DMUs) and to generate test inputs for subsequent ANN prediction modules. For this sequential process, Charnes, Cooper, and Rhodes and Banker, Chames and Cooper DEA models and back propagation neural network (BPNN) are used. The proposed methodology is empirically supported using longitudinal data of Japanese electronics manufacturing firms.

The combined modeling approach proves effective through sequential processes by streamlining DEA analysis and BPNN predictions. The DEA model captures notable characteristics and efficiency trends of the Japanese electronics manufacturing industry and extends its utility as a preprocessor to neural network prediction modules. BPNN, in conjunction with DEA, demonstrates promising estimation capability in predicting efficiency scores and best performance benchmarks for DMUs under evaluation.

Integration of adaptive prediction capacity into the measurement model is a practical necessity in the benchmarking arena. The proposed framework has the potential to recalibrate benchmarks for firms through longitudinal data analysis.

This research paper proposes an innovative approach of performance measurement and prediction in line with superiority-driven best performance modeling. Adaptive prediction capabilities embedded in the proposed model enhances managerial flexibilities in setting performance goals and monitoring progress during pursuit of improvement initiatives. This paper fills the research void through methodological breakthrough and the resulting model can serve as an adaptive decision support system.

The purpose of this paper is to develop an artificial neural network (ANN) based prediction model via integration with data envelopment analysis (DEA) to provide the means of predicting incremental performance goals. The findings confirm the usefulness of the herein developed prediction approach, based on the results of analyses of time series data from the smartphone industry.

A two-stage hybrid model was developed, incorporating sequential measurement and prediction capability. In the first stage, a Chames, Cooper, and Rhodes DEA model is the preprocessor, generating efficiency scores (ES) of decision-making units (DMUs). In the second or follow-on stage, the ANN prediction module utilizes knowledge variables and ES to predict the change in performance needed for a desired level of improvement.

This combined approach effectively captured the information contained in the industry’s turbulent characteristics, and subsequently demonstrated an adaptive prediction capability. The back propagating neural network successfully predicted the incremental performance targets of DMUs, which translated the desired improvement levels into actionable performance goals, e.g., revenue and operating income.

This paper presents an incremental prediction approach that supports better practice benchmarking. This study differentiates itself from previous research by introducing an adaptive prediction method which generates relevant quantity outputs based upon desired improvement levels. The proposed modeling approach integrates performance measurement with a prediction framework and advances benchmarking practices to enable better performance prediction.

The measurement capabilities of the data envelopment analysis (DEA) models are used to train the artificial neural network (ANN) models for the best performance modeling of the sawmills in Ontario. The bootstrap DEA models measure robust technical efficiency scores and have benchmarking abilities, whereas the ANN models use abstract learning from a limited set of information and provide the predictive power.

The complementary modeling approaches of the DEA and the ANN provide an adaptive decision support tool for each sawmill.

The trained ANN models demonstrate promising results in predicting the relative efficiency scores and the optimal combination of the inputs and the outputs for three categories (large, medium and small) of sawmills in Ontario. The average absolute error in predicting the relative efficiency scores varies from 0.01 to 0.04, and the predicted optimal combination of the inputs (roundwood and employees) and the output (lumber) demonstrate that a large percentage of the sawmills shows less than 10% error in the prediction results.

The purpose of this study is to develop an integrated DEA-ANN model that can help in the continuous improvement and performance evaluations of the forest industry working under uncertain business environment.

The performance analysis of top 50 management institutions of India is conducted to understand their efficiency in utilizing available resources. The importance of different indicators is investigated to identify most preferred strategies of top management institutions in the country in order to meet the expectations of all stakeholders. Artificial neural networks models are applied for pattern recognition and classification purpose using self-organized map algorithms. A huge reservoir of young generation is being trained every year to meet the demand of business in different sectors of economies. It becomes a matter of concern to know the performance of the management institutes to ensure the overall national progress, which can be done by enabling organizations to improve their efficiency and effectiveness, provided the right information and skills are served. Data envelopment analysis (DEA) and self-organizing maps are utilized together to take advantages of optimization and prediction capabilities inherent in each method, and they may be beneficial to assess institution’s competitive position and design their own strategies in order to improve. The paper aims to discuss these issues.

The DEA is used to understand the utilization of resources by institutions on the bases of efficiency scores. Due to a greater flexibility and adaptability, neural technique, i.e. self-organized map, which is an artificial intelligence-based technique, a popular unsupervised learning model with a capability to capture patterns from data sets, is used. In this study, various parameters like qualification of faculty, research output of faculty members, expenditure made for functioning of the institution, etc., are considered. These academic and operational indicators are investigated in relation to the rank score and the efficiency score of top management institutions, and different strategies as a combination of input as well as output indicators are identified.

In the analysis, three types of strategies are identified. At present, the focus on salary packages of graduates seems the most utilized strategy. It is also observed that the strategy of having good performance, in terms of consultancy, peer and employer perception, has the highest success rate (in terms of score used for ranking). Results obtained using both techniques shows that due to high deviation and less explored research publications and sponsored research project is an opportunity that institutions can work upon to have maximum output. But to maintain consistency in terms of the high rank score and efficiency score, management institutions need to focus on consultancy, peer and employer perception.

This research identifies the different parameters categorized into various inputs and outputs for the management institutions in India for the benchmarking. It studies the importance of identified parameters in terms of success (rank score and efficiency score). Further investigation of relationship between parameters and success is conducted. Different strategies as a combination of parameters are identified. The current choice of top management institutions is revealed in terms of their preference and effectiveness of strategy. This research also provides some insight about long-term and short-term strategies, which may be beneficial to education managers or decision makers.

It is one of the rare papers in terms of performance measurement through data envelopment method and identification of strategy using artificial intelligence. This paper utilized a hybrid methodology that integrates these two data analytic methods to capture an innovative performance and strategies prediction in education system.