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Attraction and commercial success of web sites depend heavily on the additional values visitors may find. Here, individual, automatically obtained and maintained user profiles are the key for user satisfaction. This contribution shows for the example of a cooking information site how user profiles might be obtained using category information provided by cooking recipes. It is shown that metrical distance functions and standard clustering procedures lead to erroneous results. Instead, we propose a new mutual information based clustering approach and outline its implications for the example of user profiling.
This paper investigates the accuracy and heterogeneity of output growth and inflation forecasts during the current and the four preceding NBER-dated U.S. recessions. We generate forecasts from six different models of the U.S. economy and compare them to professional forecasts from the Federal Reserve’s Greenbook and the Survey of Professional Forecasters (SPF). The model parameters and model forecasts are derived from historical data vintages so as to ensure comparability to historical forecasts by professionals. The mean model forecast comes surprisingly close to the mean SPF and Greenbook forecasts in terms of accuracy even though the models only make use of a small number of data series. Model forecasts compare particularly well to professional forecasts at a horizon of three to four quarters and during recoveries. The extent of forecast heterogeneity is similar for model and professional forecasts but varies substantially over time. Thus, forecast heterogeneity constitutes a potentially important source of economic fluctuations. While the particular reasons for diversity in professional forecasts are not observable, the diversity in model forecasts can be traced to different modeling assumptions, information sets and parameter estimates. JEL Classification: G14, G15, G24
Order channel management
(2007)
INSTITUTIONAL INVESTORS, I.E. HEDGE FUNDS OR TRADITIONAL FUNDS, FACE ON THE ONE HAND NEW TECHNOLOGY-ENABLED TRADING CHOICES AND ON THE OTHER HAND INCREASED PERFORMANCE PRESSURE FROM THEIR CUSTOMERS. TO BALANCE THESE OPPORTUNITIES AND CHALLENGES, NEW APPROACHES TO MANAGE THEIR TRADING DESKS AND ORDER DECISIONS ARE REQUIRED.
THIS PROJECT PROVIDES A PERFORMANCE MEASURE ON THE EFFECT OF LATENCY IN THE CONTEXT OF THE COMPETITIVE ADVANTAGE OF IT. BASED ON A DATASET OF DEUTSCHE BÖRSE’S ELECTRONIC TRADING SYSTEM XETRA, AN EMPIRICAL ANALYSIS IS APPLIED. THAT WAY, WE QUANTIFY THE IMPACT OF LATENCY FROM A CUSTOMER’S POINT OF VIEW.
IT-driven trading innovations offer institutional investors alternative trading channels to broker delegated order handling. Motivated by the impact on intermediation relationships in securities trading and the adoption rate of such trading channels, the new option of self-directed order handling is analyzed. To capture the prerequisites for institutional investors to insource their order handling, an order-channel management (OCM) framework is introduced. It is based on a structural approach to account for the increasing complexity in comparison to traditional intermediary services. Drivers for the adoption of an OCM framework are investigated from the strategic perspective. Operational OCM is based on the business value of IT analysis of distinct trading innovations. It includes smart order router technology, low latency technology as an upgrade for existing IT-driven trading channels as well as negotiation dark pools, representing alternative trading venues. Evidence that all investigated IT-driven trading innovations generate additional business value is provided as one result. However, it is also shown that they exhibit entry barriers tightly related to investor size. Further, Task-Technology Fit is proven to be the major driver for the adoption decision. Consequently, IT-driven trading innovations should increase trading control, satisfy high anonymity and varying urgency demands.