Executive SummaryThe study of network topologies provides interesting insights into the way the principles on which the construction of connected systems are based influence diffusion dynamics and communication processes in many socio-technical systems.& Empirical research has shown that there are principles for the construction of social networks and their technical derivatives, like e-mail networks, the Internet, publication coauthoring, or business collaboration. & Such real world networks attach new members over time and the mode of attachment prefers existing members that are already well connected. This principle is called "preferential attachment" and leads to the emergence of "scale-free" networks. & Scale-free networks seem to be a better fit for the description of real world networks than the random networks used so far. Their behavior in terms of diffusion and communication processes is fundamentally different from that of random networks. & To illustrate the value of scale-free networks for applications in information systems re-search, examples will be given to illustrate their usefulness for real world network modeling. A communication network of security traders will show what impact network topology has on the dynamics of complex socio-technical systems.
We study the relationship between communication network topologies, namely the small-world networks introduced by Watts and Strogatz, and the simulation results of an artificial stock market, here the Frankfurt Artificial Stock Market. Heterogeneous interacting agents communicate their success and trading strategy to their nearest neighbors. A process of information diffusion arises through the adaptive behavior of agents when encountering more successful strategies in their direct neighborhood. We will show that an increasing rewiring probability of the small-world network will lead to higher volatility and distortion within our simulation model. It seems probable that the spatial position of traders within a communication network affects the price building process.
This study evaluates the interest rate forecast efforts of U.S. banks, insurance companies, other financial service companies, research-and consulting institutes, associations, and industrial companies. Subjects of analysis are 10-year US-Government bond yield forecasts and 3-month US-Treasury bill rate forecasts for the period between October 1989 and December 2004. In total 134 forecasts time series with more than 14,000 forecast data are scrutinized. This makes it the most extensive analysis of interest rate forecasts so far. Forecast error measures are Theil's U 2 , TOTA coefficient, and the forecast quality matrix. All of the 134 forecast time series lag behind reality. Most of them prove to be inferior to the naïve forecast.KEY WORDS Interest rate forecasts, forecast accuracy, topically orientated trend adjustment behavior, quasi-naïve forecast
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