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  • 1
    Electronic Resource
    Electronic Resource
    Oxford, UK; Malden, USA : Blackwell Publishing Ltd/Inc.
    Journal of business finance & accounting 32 (2005), S. 0 
    ISSN: 1468-5957
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Economics
    Notes: Abstract:  We examine the newly developed international diversification instruments–iShares traded on the American Stock Exchange. Given the fact that iShares can be created and redeemed at will, the daily price of an iShare is expected to be equal to the daily portfolio value of the underlying assets in the home-country market. Therefore, theoretically, iShare pricing should be influenced by the risk from the iShare's home-country market and not the risk from the US market, per se. We evaluate the risk exposure of iShare prices to the US market (non-fundamental effect) as well as the home-country market (the fundamental effect). We find that most iShare returns are significantly influenced by and sensitive to the US market risk. Moreover, the US market appears to be the key permanent driving factor and the home-country market is a pronounced transitory driving force for iShare prices. These findings indicate the presence of limits of international arbitrage for iShares. As a result, the international diversification benefits of iShares become questionable.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    350 Main Street , Malden , MA 02148 , USA , and 9600 Garsington Road , Oxford OX4 2DQ , UK . : Blackwell Publishing, Inc.
    Decision sciences 35 (2004), S. 0 
    ISSN: 1540-5915
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Economics
    Notes: Category-management models serve to assist in the development of plans for pricing and promotions of individual brands. Techniques to solve the models can have problems of accuracy and interpretability because they are susceptible to spurious regression problems due to nonstationary time-series data. Improperly stated nonstationary systems can reduce the accuracy of the forecasts and undermine the interpretation of the results. This is problematic because recent studies indicate that sales are often a nonstationary time-series. Newly developed correction techniques can account for nonstationarity by incorporating error-correction terms into the model when using a Bayesian Vector Error-Correction Model. The benefit of using such a technique is that shocks to control variates can be separated into permanent and temporary effects and allow cointegration of series for analysis purposes. Analysis of a brand data set indicates that this is important even at the brand level. Thus, additional information is generated that allows a decision maker to examine controllable variables in terms of whether they influence sales over a short or long duration. Only products that are nonstationary in sales volume can be manipulated for long-term profit gain, and promotions must be cointegrated with brand sales volume. The brand data set is used to explore the capabilities and interpretation of cointegration.
    Type of Medium: Electronic Resource
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  • 3
    Electronic Resource
    Electronic Resource
    Oxford, UK : Blackwell Publishing Ltd
    Decision sciences 31 (2000), S. 0 
    ISSN: 1540-5915
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Economics
    Notes: Analyzing scanner data in brand management activities presents unique difficulties due to the vast quantity of the data. Time series methods that are able to handle the volume effectively often are inappropriate due to the violation of many statistical assumptions in the data characteristics. We examine scanner data sets for three brand categories and examine properties associated with many time series forecasting methods. Many violations are found with respect to linearity, normality, autocorrelation, and heteroscedasticity. With this in mind we compare the forecasting ability of neural networks that require no assumptions to two of the more robust time series techniques. Neural networks provide similar forecasts to Bayesian vector autoregression (BVAR), and both outperform generalized autoregressive conditional herteroscedasticty (GARCH) models.
    Type of Medium: Electronic Resource
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