An Empirical Analysis of Nonlinear Dynamics Relationship between the United States and Taiwan Stock Markets

This paper investigates the co-integration and causal relationships by threshold model and non-linear adjustments relationship by STAR model between the U.S. and Taiwan stock market. The fi ndings indicate that there exists an asymmetric threshold co-integration relationship between the U.S. and Taiwan stock markets. Moreover, this paper further fi nds that this is signifi cant evidence of non-linearity in the TAIEX return, and the nonlinear dynamic adjustments of the S&P 500 and TAIEX prices follow the logistic transition function. The contribution of this study demonstrates that the LSTECM-GARCH is well suited to describing the short-run and long-run dynamic relationship between the U.S. and Taiwan stock markets

An Empirical Analysis of Nonlinear Dynamics Relationship between the United States and Taiwan Stock Markets

This paper investigates the co-integration and causal relationships by threshold model and non-linear adjustments relationship by STAR model between the U.S. and Taiwan stock market. The fi ndings indicate that there exists an asymmetric threshold co-integration relationship between the U.S. and Taiwan stock markets. Moreover, this paper further fi nds that this is signifi cant evidence of non-linearity in the TAIEX return, and the nonlinear dynamic adjustments of the S&P 500 and TAIEX prices follow the logistic transition function. The contribution of this study demonstrates that the LSTECM-GARCH is well suited to describing the short-run and long-run dynamic relationship between the U.S. and Taiwan stock markets

The Impact of the QFIIs Deregulation on Normal and Abnormal Information Transmission Between the Stock and Exchange rates in Taiwan

This investigation adopts the Correlated Bivariate Poisson GARCH with Jump and Diffusion Volatility Spillover (CBP-GARCH-JDSV) model to determine whether the Qualified Foreign Institutional Investors (QFIIs) deregulation in Taiwanese stock markets influences normal and abnormal information transmission between stock and exchange rates. Empirical results demonstrated that the diffusion and jump process have significantly correlations and interacted with stock and exchange rates markets following the QFIIs deregulation. Finally, normal information transmission changed bi-directionally across markets and abnormal information supports the asset approach to determining exchange rates. Additionally, estimation results suggest that information transmissions are affected by removal of investment restrictions.The Qualified Foreign Institutional Investors Deregulation Jump Intensity Spillovers CBP-GARCH-JDSV Model

Are the global REIT markets efficient by a new approach?

This study uses a panel KSS test by Nuri Ucar and Tolga Omay (2009), with a Fourier function based on the sequential panel selection method (SPSM) procedure proposed by Georgios Chortareas and George Kapetanios (2009) to test the efficiency of REIT markets in 16 countries from 28 March 2008 to 27 June 2011. A Fourier approximation often captures the behavior of an unknown break, and testing for a unit root increases its power to do so. Moreover, SPSM can determine the mix of I(0) and I(1) series in a panel setting to clarify how many and which are random walk processes. Our empirical results demonstrate that REIT markets are efficient in all sampled countries except the UK. Our results imply that investors in countries with efficient REIT markets can adopt more passive portfolio strategies

Abnormal Domestic Information Disseminate on Cross-listed Nikkei 225 Index Futures from Abroad?

This study extends the GARCH with autoregressive conditional jump intensity in Generalized Error Distribution (GARJI-GED) model to identify the fundamental characteristics of Nikkei 225 index and futures. Furthermore, this study applied the Granger causality test to investigate whether an abnormal information lead and lag relationship existed for the Nikkei 225, SIMEX-Nikkei 225 and OSE-Nikkei 225. Empirical results demonstrate that Nikkei 225 index and futures show jump phenomena, implying a jump process is necessary to match statistical features in spot and futures markets. Finally, the empirical results indicated that the abnormal information of the OSE-Nikkei 225 futures contract significantly leads the one of the SIMEX- Nikkei 225 and Nikkei 225 index.

Supporting Document-Category Management: An Ontology-based Document Clustering Approach

Automated document-category management, particularly the document clustering, represents an appealing alternative of supporting a user\u27s search, access, and utilization of the ever-increasing corpora of textual. Traditional document clustering techniques generally emphasize on the analysis of document contents and measure document similarity on the basis of the overlap between or among the feature vectors representing individual document. However, it can be problematic and cannot address word mismatch or ambiguity effectively to cluster document at the lexical level. To address problems inherent to the traditional lexicon-based approach, we propose an Ontology-based Document Clustering (ODC) technique, which employs a domain-specific ontology to support the proceeding of document clustering at the conceptual level. We empirically evaluate the effectiveness of the proposed ODC technique, using the lexicon-based and LSI-based document clustering techniques (i.e., HAC and LSI-based HAC) for evaluation purpose. Our comparative analysis results show ODC to be partially effective than HAC and LSI-based HAC, showing higher cluster precision across all levels of cluster recall and statistically significant in F1 measure. In addition, our preliminary analysis on the effect of granularity of concept hierarchy suggests the usage of fine-grained concept hierarchy can make ODC reach to a better performance. Our findings have interesting implications to research and practice, which are discussed together with our future research directions

IMPROVING RECOMMENDATION PERFORMANCE WITH USER INTEREST EVOLUTION PATTERNS

Effective recommendation is indispensable to customized or personalized services. Collaborative filtering approach is a salient technique to support automated recommendations, which relies on the profiles of customers to make recommendations to a target customer based on the neighbors with similar preferences. However, traditional collaborative recommendation techniques only use static information of customers’ preferences and ignore the evolution of their purchasing behaviours which contain valuable information for making recommendations. Thus, this study proposes an approach to increase the effectiveness of personalized recommendations by mining the sequence patterns from the evolving preferences of a target customer over time. The experimental results have shown that the proposed technique has improved the recommendation precision in comparison with collaborative filtering method based on Top k recommendation