An-Pin Chen

Knowledge management performance evaluation: a decade review from 1995 to 2004

In this paper, the development of knowledge management (KM) was surveyed, using a literature review and classification of articles from 1995 to 2004. With a keyword index and article abstract, we explored how KM performance evaluation has developed during this period. Based on a scope of 108 articles from 80 academic KM journals (retrieved from six online databases), we surveyed and classified methods of KM measurement, using the following eight categories: qualitative analysis, quantitative analysis, financial indicator analysis, non-financial indicator analysis, internal performance analysis, external performance analysis, project-orientated analysis and organizationorientated analysis, together with their measurement matrices for different research and problem domains. Future development directions for KM performance evaluation are presented in our discussion. They include: (1) KM performance measurements have tended towards expertise orientation, while evaluation development is a problemorientated domain; (2) different information technology methodologies, such as expert systems, knowledge-based systems and case-based reasoning may be able to evaluate KM as simply another methodology; (3) the ability to continually change and obtain new understanding is the driving power behind KM methodologies, and should be the basis of KM performance evaluations in the future.

Integrating option model and knowledge management performance measures: an empirical study

The knowledge-based economy is coming, and knowledge management (KM) has rapidly disseminated in academic circles as well as in the business world. While an increasing number of companies have launched into knowledge management initiatives, a large proportion of these initiatives are limited to a technical focus. The problem with this type of focus is that it excludes and neglects the true potential benefits that can be derived from knowledge management. This paper develops a new metric, knowledge management performance index (KMPI), for evaluating the performance of a firm in its KM at a point in time. We therefore suggest that a KMPI can be used to determine KM activities from the following perspectives: knowledge creation, knowledge conversion, knowledge circulation and knowledge completion. When KM activities efficiency is increased, KMPI will also be expanded, enabling firms to become knowledge intensive. This paper makes three important contributions: (1) it provides a formal theoretical grounding for the validity of the Black-Scholes model that might be applied to KM; (2) it proposes a measurement framework to enable knowledge assets to be leveraged effectively and efficiently; and (3) it presents the first application of the Black-Scholes model that uses a real-world business situation involving KM as its test bed. The results prove the option pricing model can act as a measurement guideline to the whole range of KM activities.

Integrating extended classifier system and knowledge extraction model for financial investment prediction: An empirical study

Abstract Machine learning methods such as fuzzy logic, neural networks and decision tree induction have been applied to learn rules, however they can get trapped into a local optimal. Based on the principle of natural evolution and global searching, a genetic algorithm is promising for obtaining better results. This article adopts the learning classifier systems (LCS) technique to provide a three-phase knowledge extraction methodology, which makes continues and instant learning while integrates multiple rule sets into a centralized knowledge base. This paper makes three important contributions: (1) it represents various rule sets that are derived from different sources and encoded as a fixed-length bit string in the knowledge encoding phase; (2) it uses three criteria (accuracy, coverage, and fitness) to select an optimal set of rules from a large population in the knowledge extraction phase; (3) it applies genetic operations to generate optimal rule sets in the knowledge integration phase. The experiments prove that the rule sets derived by the proposed approach is more accurate than other machine learning algorithm.

Evaluating the airline service quality by fuzzy OWA operators

The OWA (Ordered Weighted Averaging) aggregation operators have been extensively adopted to assign the relative weights of numerous criteria. However, previous aggregation operators (including OWA) are independent of aggregation situations. To solve the problem, this study proposes a new aggregation model – dynamic fuzzy OWA operators based on situation model, which can modify the associated dynamic weight based on the aggregation situation and can work like a “magnifying lens” to enlarge the most important attribute dependent on minimal information, or can obtain equal attribute weights based on maximal information. We also apply proposed model to evaluate the service quality of airline.

Applying extending classifier system to develop an option-operation suggestion model of intraday trading – an example of taiwan index option

This novel study developed an option-operation suggestion model by applying integrated artificial intelligence technique, extending learning classifier system (XCS), which incorporates reinforcement machine learning method to the dynamical problems to the behavior finance. Due to the history of Behavior Finance, many researches have found that the shape of stock trend is not following random walk model, but the repeated trading patterns exist which are referred to as investors experiences. Furthermore, some classical researches have been merely adopted traditional artificial intelligence to analyze the result. Those methodologies are not sufficiently to resolve the dynamical problem, such as economical trading behaviors. Therefore, the model has been proposed concerning intraday trading but avoiding the system risk in the short-term position to benefit investors. By dynamic learning ability of XCS and general population features, the output operation suggestions could be obtained as a reference strategy for investors to predict the index option trend. As an example of Taiwan Index option, the results of the accuracy and accumulative profit have been exhibited remarkable outcome, and so as the simulations of short term prediction with 10-minute and 20-minute tick data.

Applying market profile theory to forecast Taiwan Index Futures market

Abstract This research applies a market profile to establish an indicator to classify the correlation between the variation in price and value with the stock trends. The indicator and technical index are neural network architecture parameters that assist to extrapolate the market logic and knowledge rules that influence the TAIEX futures market structure via an integral assessment of physical quantities. To implement the theory of market profile on neural network architecture, this study proposes qualitative and quantitative methods to compute a market profile indicator. In addition, the indicator considers the variation and relevance between long-term and short-term trends by incorporating the long-term and short-term change in market in its calculation. An assessment of forecasting performance on different calculation approaches of market profile indicator and technical analysis is conducted to differentiate their accuracies and profitability. The experimental results show the qualitative market profile indicator outperforms the quantitative approach in a short-term forecast period. In contrast, the quantitative market profile indicator has a better trend-predicting ability, thus it is more effective in the long-term forecast period. The integration of market profile and technical analysis surpasses technical analysis as a neural network architecture parameter by effectively improving forecasting performance and profitability.

An inter-market arbitrage trading system based on extended classifier systems

Traditionally, the most popular arbitrage strategy is derived from the cost of carry model or by using the econometrics approach. However, these approaches have difficulty in dealing with intra-day 1-min trading data and capturing inter-market arbitrage opportunity in the real world. In this research, we propose computational intelligence approaches based on the extended classifier system (XCS). First, in order to reduce the amount of data, the original data streams of intra-day 1-min trading data are filtered by the conditions of variant price spread relation. XCS is then adopted for knowledge rule discovery. After analyzing the property with domain-specific knowledge that the price of index futures will get close to that of spot products at the time the futures mature, four important factors related to bias, price spread, expiry date, and intraday trading timing are considered as the conditions of XCS to build the inter-market arbitrage model. The inter-market spread of the Taiwan Stock Index Futures (TX) traded at the Taiwan Futures Exchange (TAIFEX) and the Morgan Stanley Capital International (MSCI) Taiwan Index Futures traded at the Singapore Exchange Limited (SGX) are chosen for an empirical study to verify the accuracy and profitability of the model.

Using the XCS classifier system for portfolio allocation of MSCI index component stocks

In a recent study, Schulenburg and Ross (2001) proposed the LCS for short-term stock forecast. Studley and Bull (2007) proposed the extended classifier system (XCS) agent to model different traders by supplying different input information. Announcement made by Morgan Stanley Capital Investment (MSCI) regarding the additions, removals, and even the weights of the component stocks in its country indices every quarter generally would cause changes to the prices and/or trade volumes of the associated component stocks. This paper takes an XCS in artificial intelligence to dynamically learn and adapt to the changes to the component stocks in order to optimize portfolio allocation of the component stocks. Since these price trends of MSCI component stocks are influenced by unknown and unpredictable surroundings, using XCS to model the fluctuations on financial market allows for the capability to discover the patterns of future trends. This simulation works on the basis of the changes to 121 component stocks in the MSCI Taiwan index between 1998 and 2009 suggests the XCS can produce the great profit and optimize portfolio allocation.

A unifying ontology modeling for knowledge management

The knowledge-based economy is approaching rapidly, and knowledge management (KM) has disseminated in leaps and bounds in academic circles as well as in the business world. This paper develops a unifying framework to evaluate KM activities for supporting intelligent knowledge-based system (KBS) using web interface, and expert system technology to help inexperienced administrators in insuring the smooth operation of KM performance. This paper makes three important contributions: (1) it proposes an efficient KM Ontology Construction Algorithm to fast conceptualize KM domain concept; (2) it provides a hybrid model used for knowledge acquisition through skeletal concept model and IDEF (Integrated DEFinition function modeling) analysis; and (3) it presents a methodology for using KM ontology in building a unifying framework and evaluation guideline for KM that works well and effective.

Modeling e-Learning System Performance Evaluation with Agent-Based Approach

Rapidly evolving information technology has dramatically changed the knowledge dissemination process. A proper e-learning environment is one of the most important knowledge tools in modern organizations. However, many of them lack a generic evaluation process to verify performance. In an attempt to solve this problem, this study propose an agent-based model which compose learning model, balanced scorecard and the option pricing approach to provide an dynamic, flexible framework for e-learning projects performance evaluations.