Showing papers by "An-Pin Chen published in 2006"

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

Abstract: 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 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.

Refined Group Learning Based on XCS and Neural Network in Intelligent Financial Decision Support System

Abstract: Cooperative learning is widely defined as a process through which a group of individuals interact to achieve a learning goal. In the fluctuating stock market, investors often have various decision-making approaches. This study attempts to exploit computer technology, financial mathematics, and econometrics to make reasonable investment decisions to reduce man-made errors or mistakes and increase profits. This work integrates the eXtended Classifier System (XCS) and neural network modules and incorporates features such as dynamic learning and group decision making. An empirical study is conducted by comparing the profitability of the proposed system with that of investment strategies based on simple rules with single technical indices, individual learning XCS, buy and hold, and six-year term deposit based on the Taiwan Index. The proposed system demonstrates superior performance in terms of accuracy, rate of cumulative return, and variance of return.

Optimally pricing european options with real distributions

Abstract: Most option pricing methods use mathematical distributions to approximate underlying asset behavior. However, it is difficult to approximate the real distribution using pure mathematical distribution approaches. This study first introduces an innovative computational method of pricing European options based on the real distributions of the underlying asset. This computational approach can also be applied to expected value related applications that require real distributions rather than mathematical distributions. The contributions of this study include the following: a) it solves the risk neutral issue related to price options with real distributions, b) it proposes a simple method adjusting the standard deviation according to the practical need to apply short term volatility to real world applications and c) it demonstrates that modern databases are capable of handling large amounts of sample data to provide efficient execution speeds.