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The purpose of this paper is to evaluate the profitability of technical trading relative to buy-and-hold (BH) strategy at firm level, controlling for firm size and trading volume.

This paper applies variable-length moving averages (VMAs) thoroughly to each and every stock listed on Taiwan Stock Exchange (TWSE) and computes the excess returns of technical trading relative to BH strategy. The samples are further grouped by firm size and trading volume. Furthermore, possible data snooping bias is investigated by employing Hansen’s (2005) Superior Predictive Ability tests.

The result shows that VMAs outperform the BH strategy. The profitability of VMAs, remarkably, is positively associated with size and trading volume. After correcting for data snooping bias, VMAs with longer moving averages outperform VMAs with shorter moving averages. The evidence suggests that size and volume information is accountable for trend projection.

Unlike past studies simply applying technical trading rules to market indices, portfolios, or selected stocks, this paper evaluates the profitability of technical trading by applying VMAs comprehensively to each and every individual stock listed on TWSE controlling for the effect of firm size and trading volume, providing more practical insights for trading individual stocks.

Among developing countries, the Republic of China in Taiwan (hereinafter Taiwan) has been experiencing economic growth accompanied by improving income distribution. Between 1964 and 1980, the average annual growth rate of the real gross national product was 9.92 per cent (Council for Economic Planning and Development (CEPD), 1982, p. 23). In the same period, the income ratio between the top 20 per cent and the bottom 20 per cent of families dropped from 5.33 to 4.17 and the Gini coefficient decreased from 0.36 to 0.30 (CEPD, 1982, p. 54; Directorate‐General of Budget Accounting and Statistics, 1980, (DGBAS), p. 44). To put it somewhat dif‐ferently, in 1964 the lowest fifth of households received 7.71 per cent of total personal income, and the highest fifth 41.07 per cent. But in 1980, the income share of the lowest fifth increased to 8.82 per cent while that of the highest fifth decreased to 36.80 per cent. The condition of greater equality in income distribution appears more obvious in the capital city of Taipei. In 1981, for instance, its Gini coefficient was estimated to be only 0.28 (Taipei Bureau of Budget, Accounting and Statistics, 1981, (TBBAS), P. 24).

The purpose of this paper is to analyze the causal relationships among categories in the China Quality Award (CQA) model based on the Malcolm Baldrige National Quality Award model.

The paper identifies seven factors from CQA categories: leadership, strategic planning, human resource focus, process management, customer and market focus, information and analysis, and results. Extending the basic Baldrige theory “Leadership drives the system that creates results,” this paper identifies driver (leadership), direction (strategic planning), foundation (information and analysis), system (human resource focus, process management, and customer and market focus), and results(business results). Structural equation model (SEM) is used to analyze the empirical data and estimate the path coefficients among CQA categories.

First, driver has not only a direct influence on results, but also has an indirect influence on results through system. Leadership has a great influence on foundation and direction. Second, direction affects human resource focus and customer and market focus of system while it has no influence on process management. Third, human resource focus and customer and market focus both affect process management, and process management has a significant impact on results. Fourth, foundation affects direction and all of the categories of system.

There are few studies which try to analyze the causal relationships among categories in the CQA model.

This study examines the relative efficiencies of anti-poverty policies implemented in 28 Chinese provinces.

This study uses meta-frontier undesirable dynamic two-stage data envelopment analysis. The authors divide the poverty reduction process into two stages: agricultural production and poverty reduction. Public expenditure is the input for the second stage, and the population below the poverty line is the undesirable output. The authors compute the efficiencies (overall efficiency, efficiency of each stage and the efficiencies of individual inputs and outputs) using meta-frontier analysis for the 28 provinces.

The results show that: (1) a significant imbalance exists between the eastern and western regions in terms of input-output efficiencies; (2) the poverty reduction stage generally fared better than the agricultural production stage did. In particular, most provinces saw increases in poverty reduction efficiencies between 2013 and 2017; (3) the place-based poverty relief policies introduced in recent years are effective at reducing the poverty rate and reaching the government-set goals and (4) while disposable income has increased steadily over the past few years, income inequality has been exacerbated.

The results show that: (1) a significant imbalance exists between the eastern and western regions in terms of input-output efficiencies; (2) the poverty reduction stage generally fared better than the agricultural production stage did. In particular, most provinces saw increases in poverty reduction efficiencies between 2013 and 2017; (3) the place-based poverty relief policies introduced in recent years are effective at reducing the poverty rate and reaching the government-set goals and (4) while disposable income has increased steadily over the past few years, income inequality has exacerbated.

A large amount of attention and public resources are devoted to fighting poverty and associated market failures in China. The extant literature focuses either on the agricultural production itself or the relationship between human capital and productivity levels. Making use of recent developments of the DEA method, the authors propose a new framework for evaluating the efficiencies of the poverty reduction process. Such a framework has the advantage of giving researchers and policymakers a more detailed diagnosis with regard to the components in the endeavor to eliminate poverty and providing useful information for policymakers to optimize public funds use. Methodologically, the framework is flexible enough to be employed for future research in similar appraisals, at different geographic and scale aggregation levels, for public projects including but not limited to poverty reduction.

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.

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 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.