Hilary Cheng

An ontology-based business intelligence application in a financial knowledge management system

Business intelligence (BI) applications within an enterprise range over enterprise reporting, cube and ad hoc query analysis, statistical analysis, data mining, and proactive report delivery and alerting. The most sophisticated applications of BI are statistical analysis and data mining, which involve mathematical and statistical treatment of data for correlation analysis, trend analysis, hypothesis testing, and predictive analysis. They are used by relatively small groups of users consisting of information analysts and power users, for whom data and analysis are their primary jobs. We present an ontology-based approach for BI applications, specifically in statistical analysis and data mining. We implemented our approach in financial knowledge management system (FKMS), which is able to do: (i) data extraction, transformation and loading, (ii) data cubes creation and retrieval, (iii) statistical analysis and data mining, (iv) experiment metadata management, (v) experiment retrieval for new problem solving. The resulting knowledge from each experiment defined as a knowledge set consisting of strings of data, model, parameters, and reports are stored, shared, disseminated, and thus helpful to support decision making. We finally illustrate the above claims with a process of applying data mining techniques to support corporate bonds classification.

Improved slack-based context-dependent DEA - A study of international tourist hotels in Taiwan

In this paper, we improve the slack-based measure (SBM) of efficiency in context-dependent data envelopment analysis (DEA) and apply in measuring the performance of 34 selected Taiwans International Tourist Hotels. Empirical results indicate that (1) the market differentiates five performance levels forming the benchmark structure for 34 hotels; (2) the hotels with higher attractiveness can be served as learning targets for the hotels in the lagging levels so as to establish the best path for performance improvements; (3) the hotels in the leading levels can use lower progress to analyze potential competitors in the lagging levels. The results of this study can provide hotels managers with insights of competitive advantage and help them with strategic decision making.

Assurance region context-dependent DEA with an application to Taiwanese hotel industry

This study proposes assurance region (AR) context-dependent data envelopment analysis (DEA) and applies the restricted weights derived by analytic hierarchy process in measuring the performance of 34 selected Taiwanese international tourist hotels during the years 1997?2006. Empirical results indicate that: AR context-dependent DEA has more of an efficiency segment than context-dependent DEA; the market differentiates 10 performance levels that form the benchmark structure for 34 hotels; hotels with higher attractiveness can serve as learning targets for hotels at lagging levels so as to establish the best path for performance improvement; and hotels at leading levels can use lower progress to analyse potential competitors at lagging levels. The results of this study can provide hotel managers with insights into competitive advantage and can help them with strategic decision-making.

Industrial cluster involvement, organizational learning, and organizational adaptation: an exploratory study in high technology industrial districts

Purpose – The primary purpose of this study is to examine the relationships among a firm’s industrial cluster involvement, organizational learning and its ability to successfully adapt to external environment. Design/methodology/approach – Field survey research method was used, and data were collected from 943 high-technology companies in the USA, China, Taiwan and Sweden. Multiple regression analysis, as well as mediation test, was conducted to analyze the data. Findings – The study finds that being positioned in an industrial cluster enhances a firm’s learning and further leads to a firm’s desired adaptive outcomes. Research limitation – Using self-reported data could be a potential limitation of this study. It would be preferable to have other forms of data for a study. Further, cross-cultural comparisons are needed to enhance our understanding in this multicultural setting. Practical implication – The findings provide business executives, as well as policymakers, a new way of thinking in respect to ho...

A visualized data analysis for bogus business entity detection

We aimed to build up a visualized data analysis process to detect potential tax evasion caused by bogus business entities. Relevant datasets of bogus business entities were extracted from large databases of 8 different systems at the Fiscal Information Agency for tax evasion pattern recognition and behavior analysis. Visualized data analysis not only helped on identifying bogus business entities, but also helped to establish a detection mechanism to discover potential tax evasion for the government.

Performance Benchmarking by Improved Slack-based Context-dependent DEA for the Hotel Industry in Taiwan

Statistics from the Taiwan Tourism Bureau (TTB) state that travelers to Taiwan in 2006 spent an average of 44.74% of their daily spending at hotels. Thus, the quality of services and the level of operational performance at hotels is key to the rise or fall of the tourism industry. Hotels in Taiwan can be divided into two groups: general and international tourist hotel (ITH). Between 1992 and 2006, the number of general tourist hotels decreased by 31%; the number of ITHs increased by 18.72%. This fact shows that large-scale ITHs have become the market mainstream; therefore, performance management of ITHs is an important research issue. A variety of previous studies dealing with this issue either adopt the comparison of financial indicators or apply data envelopment analysis (DEA) to compute relative efficiency values of decisionmaking units (DMU). Improving the performance of any inefficient DMU requires a comparison with the most efficient DMU. However, the lack of identification information concerning market competitors provides no new research perspective. The SBM context-dependent DEA proposed by Morita, Hirokawa and Zhu (2005) offers many options for performance improvement; yet it fails to shed light on how to identify potential competitors. Moreover, X has not yet certified the research method as feasible. The objectives of this research are listed as follows: (1) analyze operational performance of hotels in Taiwan, (2) provide reference paths for hotel practitioners to make gradual improvements, and (3) assist hotel practitioners in identifying potential competitors. The research issues are as follows: (1) improve the shortcoming of the research method proposed by Morita, Hirokawa and Zhu (2005), (2) establish a benchmark structure for all DMUs, and (3) perform empirical analysis of information obtained from the benchmark structure.

Productivity of the banking sector: A case study in Kyrgyz Republic

This paper conducts a performance analysis by measuring productivity and efficiency change in the Kyrgyz Republics banking sector during 2002-2009. The study employs data envelopment analysis to construct Malmquist indices and break them down into technical efficiency, technical change, and pure technical efficiency change, scale efficiency change and total factor productivity change components. Two input factors - capital and deposits - as well as two output variables - interest revenue and credits - were analyzed to explore the performance of eighteen commercial banks, and to understand how bank ownership and size of bank affect productivity change. The results show that productivity in the banking sector fell during the sample period. Medium-sized banks with foreign capital were found to be the best performers in productivity growth, while large banks showed decreasing productivity during the sample period. Consistent with previous findings discussed in the literature, this study suggests that technological improvements can sustain positive productivity growth.

Resource allocation of city and county under government supervision: An effectiveness analysis of the healthcare system in Taiwan

This paper aims to explore the efficiency of health care resource allocation of 23 cities and counties in Taiwan. The data envelopment analysis (DEA) method was used to compare the relative technical efficiency between counties and cities during the period from 2000 to 2009. In addition, the multidimensional scaling (MDS) method was applied to examine the allocation of healthcare resources in the Taiwan health system. The DEA results showed that some counties and cities had a long history of low relative technical efficiency, and the MDS results indicated that healthcare resource allocation was palpably unbalanced and of low efficiency. The results of this study will provide policy-makers with some insight into the efficiency of current healthcare resource allocation, and prioritize improvements for the counties and cities that show long-term low efficiency. The relative distance between the coordinates produced from the MDS analysis can serve as a reference for planning or adjusting the priority of the improvements.

Towards Automated Optimal Equity Portfolios Discovery in a Financial Knowledge Management System

We propose a knowledge discovery and knowledge management process for equity management institutions. We realize the process with a financial knowledge management system, FKMS, that is a system platform being able to convert various sources of data into the data warehouse, to retrieve data cubes based on different power users’ commands for subsequent valuation modeling or data mining applications. We then introduced a data mining solution for equity portfolio construction using the simulated annealing algorithm. Two data sets consist of small stocks ranging from 11/86 to 10/91 and from 6/93 to 5/96 are used. The corresponding rates of return of Russell 2000 index are collected as benchmarks for evaluation based on the Sharpe ratios and the turnover ratios. The result of the simulated annealing algorithm has shown to outperform the market index as well as the gradient maximization method.

Towards Automated Optimal Equity Portfolios Discovery in a Knowledge Sharing Financial Data Warehouse

We propose a knowledge discovery process for multi-factor portfolio management on a financial decision support system. We first construct an OPen Intelligent Computing System (OPICS) to support time series management and knowledge management. A system, Cyclone, which efficiently supports financial applications, is developed under the OPICS. We then introduce a data mining solution for equity portfolio construction using the simulated annealing algorithm. Two data sets consist of small stocks ranging from 11/86 to 10/91 and from 6/93 to 5/96 are used. The corresponding rates of return of Russell 2000 index are collected as benchmarks for evaluation based on the Sharpe ratios and the turnover ratios. The result shows that the simulated annealing algorithm outperforms both the market index and the gradient maximization method.