Showing papers by "An-Pin Chen published in 2005"

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

Abstract: 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.

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

Abstract: 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.

Measurement practices for knowledge management: an option perspective

Abstract: This article develops an option pricing model to evaluate knowledge management (KM) activities from the following perspectives: knowledge creation, knowledge conversion, knowledge circulation, and knowledge carry out. This paper makes three important contributions: (1) it provides a formal theoretical grounding for the validity of the Black-Scholes model that might be employed to KM; (2) it proposes a measurement framework to enable leveraging knowledge assets effectively and efficiently; (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 be act as a measurement guideline to the whole KM activities.

A unifying ontology modeling for knowledge management

Abstract: 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.

Applying two-stage XCS model on global overnight effect for local stock prediction

Abstract: This study applied an integrated artificial intelligence method, extend learning classifier system (XCS), to predict the stock trend fluctuation considering the global overnight effect. However, some researchers have already indicated that XCS model that is applied successfully to form a forecast model in local market. Based on those prediction models, we put more effort to focus on the financial phenomenon, overnight effect between each two global markets, and we developed a two-stage XCS model to forecast the local stock market. In the experiments, DJi and Twi are chosen as referent and predicted markets respectively, and the model is trained by their historical data. For its accuracy verified, the model is tested by recently data. Finally, we have concluded that the proposed model successfully simulates the phenomenon, and the high ratio of correctness is definitely figured out.

A three-phase knowledge extraction methodology using learning classifier system

Abstract: Machine learning methods such as fuzzy logic, neural networks and decision tree induction have been applied to learn rules but they may be trapped into local optimal. Based on the principle of natural evolution and global searching, a genetic algorithm is promising in obtaining better results. This article adopts 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 the rule sets derived by the proposed approach is more accurate than other machine learning algorithm.

An extended two-phase architecture for mining time series data

Abstract: Time series data vary with time. In the past, most of the researches focused on the matching of feature points or measuring of the similarities. They could successfully represent the feature patterns in a visualized way. In the mean while, those researches did not sufficiently describe the results in simple and understandable words. In this research, a two-phase architecture for mining time series data is introduced. By combining some different mining techniques, the difficulties mentioned above may be overcome. This architecture mainly consists of Exploratory Data Analysis (EDA) and techniques related to mining association rules. After the phase I analysis, quantitative association rules are obtained by phase II. Meanwhile, the rules of the architecture are able to be verified by accuracy analysis. Finally, a result of comparison with the traditional data mining techniques and this architecture shows that the two-phase architecture is superior to traditional techniques to the time series data.

An implementation of learning classifier systems for rule-based machine learning

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 hybrid knowledge integration strategy, which makes for continuous and instant learning while integrating multiple rule sets into a centralized knowledge base. This paper makes three important contributions: (1) it provides a knowledge encoding methodology to represent various rule sets that are derived from different sources, and that are encoded as a fixed-length bit string; (2) it proposes a knowledge integration methodology to apply genetic operations and credit assignment to generate optimal rule sets; (3) it uses three criteria (accuracy, coverage, and fitness) to apply the knowledge extraction process, which is very effective in selecting an optimal set of rules from a large population. The experiments prove that the rule sets derived by the proposed approach is more accurate than the Fuzzy ID3 algorithm.