Showing papers by "An-Pin Chen published in 2008"

Global Asset Allocation Using XCS Experts in Country-Specific ETFs

Abstract: There are several studies extended classification system (XCS) in past years, the model can dynamically learn and adapt to the change of environments for maximizing the desired goals. This paper conducts simulation the experiment to evolve XCS for global asset allocation in the country-specific Exchanged Traded Funds (ETFs). Since international stock price trend is influenced by unknown and unpredictable surrounding, using XCS to model the fluctuations on global financial market allows for the capability to discover the patterns of the future trends. The benefits of international diversification can be achieved with country-specific ETFs at a low cost, with a low transaction cost, tracking error and in a tax-efficient way. These empirical results indicate that XCS is capable of evolving from generation to generation, and in this way can provide the highest profit for future global asset allocation decision-making.

Applying the Extended Classifier System to Trade Interest Rate Futures Based on Technical Analysis

Abstract: In practice, it is difficult to gain profit in the process of trading interest rate derivative commodities. This could be attributed to the complexity of existing pricing models, which are derived from the term structure and yield curve, both of which cannot adapt well to short-term market dynamics. In this study, we use the Extended Classifier System (XCS) to model the market behavior of interest rate futures, the purpose of which is to provide effective trading decision support. Several technical indicators and their first- and second-order derivatives are selected as the market descriptive variables, which are then used for XCS training. Finally, the adaptive rules of the classifiers, which consist of conditions with relative actions considered helpful for constructing the automatic trading system, are generated from the XCS knowledge discovery process. The market data of the 10-year government bond futures traded in Taiwan are chosen for empirical study to verify the accuracy and profitability of the XCS model. These were also used to conduct a comparative evaluation between the random walk and tendency following models and the XCS model.

Clustering Time Series Data by SOM for the Optimal Hedge Ratio Estimation

Abstract: The fat-tailed and leptokurtic properties observed in most financial asset return series would cause the inaccuracy of hedge ratio estimation because most traditional statistics approaches are based on the assumption of normal distribution. In this study, a novel approach is proposed using self-organizing map (SOM, also called Kohonen's self-organizing feature map) for time series data clustering and similar pattern recognition to improve the optimal hedge ratio (OHR) estimation. Five SOM-based models (considering the weight for averaging and the interval for data sampling) and two traditional models (ordinary least square method and naive hedge) were compared in Taiwan stock market hedging. The experiment demonstrates the feasibility of applying SOM, and the empirical results show that SOM approach provides a useful alternative to the OHR estimation.

Application of dynamic financial time-series prediction on the interval Artificial Neural Network approach with Value-at-Risk model

Abstract: Artificial neural networks (ANNs) are promising approaches for financial time-series prediction. This study adopts a hybrid approach, called a fuzzy BPN, consisting of a back-propagation neural network (BPN) and a fuzzy membership function which takes advantage of the ANNspsila nonlinear features and interval values instead of the shortcoming of ANNspsila single-point estimation. To employ the two characteristics mentioned above, a dynamic intelligent time-series forecasting system will be built more efficiently for practical financial predictions. Additionally, with the liberalization and opening of financial markets, the relationships among financial commodities became much closer and complicated. Hence, establishing a perfect measure approach to evaluate investment risk has become a critical issue. The objective of this study is not only to achieve higher efficiency in dynamic financial time-series predictions but also a more effective financial risk control with value-at-risk methodology, which is called fuzzy-VaR BPN model in this study. By extending to the financial market environment, it is expected that wider and more suitable applications in financial time-series and risk management problems would be covered. Moreover, the fuzzy-VaR BPN model would be applied to the Taiwan Top50 Tracker Fund to demonstrate the capability of our study.

Applying Mobile Agent to a Mobile Stock Intermediary Services System Development 1

Abstract: Due to the radical changing of the global economy, a more precise stock valuation helps providing important judgment principles to decision-makers and investors. With the advent of the third-generation (3G) or future forth-generation (4G) Internet, the mobile commerce (MCommerce) will become increasingly important. In addition, the mobile stock investment decision support system attracts great interests for professionals, such as stockholders, bondholders, financial analysts, governmental officials, and even the general public, recently. This study introduces a Mobile Agent-based Stock Intermediary Services System (MASISS) framework based on the mobile agent perspective to provide ubiquitous and seamless transaction activities for financial institutions. It also helps customers to make a more precise decision in the current intense commercial competition environment. For building distributed enterprise systems, The MASISS framework is developed in an integration of J2ME and J2EE environment with cross-platform portability, a huge server-side and client-side deployment base, and coverage for most W3C standards..

Hybrid intelligence approaches for designing a dynamic financial time-series predictive model based on web-architecture home finance learning environment

Abstract: This study proposes design concepts for a comprehensive home financial learning environment that individual investors can use as a reference in establishing web-based learning and investment platforms. This study also introduces a hybrid approach that demonstrates a data mining function of the financial learning environment. Known as Fuzzy BPN, this approach is comprised of backpropagation neural network (BPN) and fuzzy membership function. This membership function takes advantage of the nonlinear features of artificial neural networks (ANNs) and the interval values as a means of overcoming the inadequacy of single-point estimation of ANNs. Based from these characteristics, a dynamic and intelligent time-series forecasting system will be developed for practical financial predictions. In addition to this, the experimental processing can demonstrate the feasibility of applying the hybrid model-Fuzzy BPN. The empirical results of the study show that Fuzzy BPN provides an alternative data mining tool for financial learning environment to investment forecasting.