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Preis T.,Center for Polymer Studies | Preis T.,Johannes Gutenberg University Mainz | Preis T.,Artemis
Journal of Physics: Conference Series | Year: 2010

In the beginning of exchange based trading, floor trading was the most widespread form of trading. In the course of the introduction and the progress in information technology, trading processes were adapted to the computational infrastructure at the international financial markets and electronic exchanges were created. These fully electronic exchanges are the starting point for recent agent-based models in econophysics, in which the explicit structure of electronic order books is integrated. The electronic order book structure builds the underlying framework of financial markets which is also contained in the recently introduced realistic Order Book Model [T. Preis et al., Europhys. Lett. 75, 510 (2006), T. Preis et al., Phys. Rev. E 76, 016108 (2007)]. This model provides the possibility to generate the stylized facts of financial markets with a very limited set of rules. This model is described and analyzed in detail. Using this model, it is possible to obtain short-term anti-correlated price time series. Furthermore, simple profitability aspects of the market participants can be reproduced. A nontrivial Hurst exponent can be obtained based on the introduction of a market trend, which leads to an anti-persistent scaling behavior of price changes on short time scales, a persistent scaling behavior on medium time scales, and a diffusive regime on long time scales. A coupling of the order placement depth to the prevailing market trend, which is identified to be a key variable in the Order Book Model, is able to reproduce fat-tailed price change distributions. © 2010 IOP Publishing Ltd.

Preis T.,Center for Polymer Studies | Preis T.,Artemis
European Physical Journal: Special Topics | Year: 2011

This article focuses on the analysis of financial time series and their correlations. A method is used for quantifying pattern based correlations of a time series. With this methodology, evidence is found that typical behavioral patterns of financial market participants manifest over short time scales, i. e., that reactions to given price patterns are not entirely random, but that similar price patterns also cause similar reactions. Based on the investigation of the complex correlations in financial time series, the question arises, which properties change when switching from a positive trend to a negative trend. An empirical quantification by rescaling provides the result that new price extrema coincide with a significant increase in transaction volume and a significant decrease in the length of corresponding time intervals between transactions. These findings are independent of the time scale over 9 orders of magnitude, and they exhibit characteristics which one can also find in other complex systems in nature (and in physical systems in particular). These properties are independent of the markets analyzed. Trends that exist only for a few seconds show the same characteristics as trends on time scales of several months. Thus, it is possible to study financial bubbles and their collapses in more detail, because trend switching processes occur with higher frequency on small time scales. In addition, a Monte Carlo based simulation of financial markets is analyzed and extended in order to reproduce empirical features and to gain insight into their causes. These causes include both financial market microstructure and the risk aversion of market participants. © 2011 EDP Sciences and Springer.

Preis T.,Center for Polymer Studies | Preis T.,Artemis
European Physical Journal: Special Topics | Year: 2011

A recent trend in computer science and related fields is general purpose computing on graphics processing units (GPUs), which can yield impressive performance. With multiple cores connected by high memory bandwidth, today's GPUs offer resources for non-graphics parallel processing. This article provides a brief introduction into the field of GPU computing and includes examples. In particular computationally expensive analyses employed in financial market context are coded on a graphics card architecture which leads to a significant reduction of computing time. In order to demonstrate the wide range of possible applications, a standard model in statistical physics - the Ising model - is ported to a graphics card architecture as well, resulting in large speedup values. © 2011 EDP Sciences and Springer.

Kenett D.Y.,Tel Aviv University | Preis T.,Center for Polymer Studies | Preis T.,ETH Zurich | Preis T.,Artemis | And 2 more authors.
International Journal of Bifurcation and Chaos | Year: 2012

Much effort has been devoted to assess the importance of nodes in complex networks. Examples of commonly used measures of node importance include node degree, node centrality and node vulnerability score (the effect of the node deletion on the network efficiency). Here we present a new approach to compute and investigate the mutual dependencies between network nodes from the matrices of node-node correlations. The dependency network approach provides a new system level analysis of the activity and topology of directed networks. The approach extracts topological relations between the networks nodes (when the network structure is analyzed), and provides an important step towards inference of causal activity relations between the network nodes (when analyzing the network activity). The resulting dependency networks are a new class of correlation-based networks, and are capable of uncovering hidden information on the structure of the network. Here, we present a review of the new approach, and an example of its application to financial markets. We apply the methodology to the daily closing prices of all Dow Jones Industrial Average (DJIA) index components for the period 19392010. Investigating the structure and dynamics of the dependency network across time, we find fingerprints of past financial crises, illustrating the importance of this methodology. © 2012 World Scientific Publishing Company.

Preis T.,Center for Polymer Studies | Preis T.,Artemis | Preis T.,Johannes Gutenberg University Mainz | Reith D.,Johannes Gutenberg University Mainz | Stanley H.E.,Center for Polymer Studies
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences | Year: 2010

Search engine query data deliver insight into the behaviour of individuals who are the smallest possible scale of our economic life. Individuals are submitting several hundred million search engine queries around the world each day. We study weekly search volume data for various search terms from 2004 to 2010 that are offered by the search engine Google for scientific use, providing information about our economic life on an aggregated collective level. We ask the question whether there is a link between search volume data and financial market fluctuations on a weekly time scale. Both collective 'swarm intelligence' of Internet users and the group of financial market participants can be regarded as a complex system of many interacting subunits that react quickly to external changes. We find clear evidence that weekly transaction volumes of S & P 500 companies are correlated with weekly search volume of corresponding company names. Furthermore, we apply a recently introduced method for quantifying complex correlations in time series with which we find a clear tendency that search volume time series and transaction volume time series show recurring patterns. © 2010 The Royal Society.

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