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ABSTRACT: Financial crises result from a catastrophic combination of actions. Vast stock market datasets offer us a window into some of the actions that have led to these crises. Here, we investigate whether data generated through Internet usage contain traces of attempts to gather information before trading decisions were taken. We present evidence in line with the intriguing suggestion that data on changes in how often financially related Wikipedia pages were viewed may have contained early signs of stock market moves. Our results suggest that online data may allow us to gain new insight into early information gathering stages of decision making.
Scientific Reports 05/2013; 3:1801.
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ABSTRACT: Crises in financial markets affect humans worldwide. Detailed market data on trading decisions reflect some of the complex human behavior that has led to these crises. We suggest that massive new data sources resulting from human interaction with the Internet may offer a new perspective on the behavior of market participants in periods of large market movements. By analyzing changes in Google query volumes for search terms related to finance, we find patterns that may be interpreted as "early warning signs" of stock market moves. Our results illustrate the potential that combining extensive behavioral data sets offers for a better understanding of collective human behavior.
Scientific Reports 04/2013; 3:1684.
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ABSTRACT: It is well-known that financial asset returns exhibit fat-tailed distributions and long-term memory. These empirical features are the main objectives of modeling efforts using (i) stochastic processes to quantitatively reproduce these features and (ii) agent-based simulations to understand the underlying microscopic interactions. After reviewing selected empirical and theoretical evidence documenting the behavior of traders, we construct an agent-based model to quantitatively demonstrate that "fat" tails in return distributions arise when traders share similar technical trading strategies and decisions. Extending our behavioral model to a stochastic model, we derive and explain a set of quantitative scaling relations of long-term memory from the empirical behavior of individual market participants. Our analysis provides a behavioral interpretation of the long-term memory of absolute and squared price returns: They are directly linked to the way investors evaluate their investments by applying technical strategies at different investment horizons, and this quantitative relationship is in agreement with empirical findings. Our approach provides a possible behavioral explanation for stochastic models for financial systems in general and provides a method to parameterize such models from market data rather than from statistical fitting.
Proceedings of the National Academy of Sciences 05/2012; 109(22):8388-93. · 9.68 Impact Factor
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ABSTRACT: It is widely believed that switching phenomena require switches, but this is actually not true. For an intriguing variety
of switching phenomena in nature, the underlying complex system abruptly changes from one state to another in a highly discontinuous
fashion. For example, financial market fluctuations are characterized by many abrupt switchings creating increasing trends
(“bubble formation”) and decreasing trends (“financial collapse”). Such switching occurs on time scales ranging from macroscopic
bubbles persisting for hundreds of days to microscopic bubbles persisting only for a few seconds. We analyze a database containing
13,991,275 German DAX Future transactions recorded with a time resolution of 10msec. For comparison, a database providing
2,592,531 of all S&P500 daily closing prices is used. We ask whether these ubiquitous switching phenomena have quantifiable
features independent of the time horizon studied. We find striking scale-free behavior of the volatility after each switching
occurs. We interpret our findings as being consistent with time-dependent collective behavior of financial market participants.
We test the possible universality of our result by performing a parallel analysis of fluctuations in transaction volume and
time intervals between trades. We show that these financial market switching processes have properties similar to those of
phase transitions. We suggest that the well-known catastrophic bubbles that occur on large time scales—such as the most recent
financial crisis—are no outliers but single dramatic representatives caused by the switching between upward and downward trends
on time scales varying over nine orders of magnitude from very large (≈102 days) down to very small (≈10ms).
Econophysics
Journal of Statistical Physics 04/2012; 138(1):431-446. · 1.40 Impact Factor
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ABSTRACT: We introduce a future orientation index to quantify the degree to which Internet users worldwide seek more information about years in the future than years in the past. We analyse Google logs and find a striking correlation between the country's GDP and the predisposition of its inhabitants to look forward.
Scientific Reports 01/2012; 2:350.
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ABSTRACT: Understanding correlations in complex systems is crucial in the face of turbulence, such as the ongoing financial crisis. However, in complex systems, such as financial systems, correlations are not constant but instead vary in time. Here we address the question of quantifying state-dependent correlations in stock markets. Reliable estimates of correlations are absolutely necessary to protect a portfolio. We analyze 72 years of daily closing prices of the 30 stocks forming the Dow Jones Industrial Average (DJIA). We find the striking result that the average correlation among these stocks scales linearly with market stress reflected by normalized DJIA index returns on various time scales. Consequently, the diversification effect which should protect a portfolio melts away in times of market losses, just when it would most urgently be needed. Our empirical analysis is consistent with the interesting possibility that one could anticipate diversification breakdowns, guiding the design of protected portfolios.
Scientific Reports 01/2012; 2:752.
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ABSTRACT: For an intriguing variety of switching processes in nature, the underlying complex system abruptly changes from one state to another in a highly discontinuous fashion. Financial market fluctuations are characterized by many abrupt switchings creating upward trends and downward trends, on time scales ranging from macroscopic trends persisting for hundreds of days to microscopic trends persisting for a few minutes. The question arises whether these ubiquitous switching processes have quantifiable features independent of the time horizon studied. We find striking scale-free behavior of the transaction volume after each switching. Our findings can be interpreted as being consistent with time-dependent collective behavior of financial market participants. We test the possible universality of our result by performing a parallel analysis of fluctuations in time intervals between transactions. We suggest that the well known catastrophic bubbles that occur on large time scales--such as the most recent financial crisis--may not be outliers but single dramatic representatives caused by the formation of increasing and decreasing trends on time scales varying over nine orders of magnitude from very large down to very small.
Proceedings of the National Academy of Sciences 05/2011; 108(19):7674-8. · 9.68 Impact Factor
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Tobias Preis
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ABSTRACT: Using our recently introduced order book model of financial markets we analyzed two different matching principles for order
allocation — price-time priority and pro rata matching. Price-time priority uses the submission timestamp which prioritizes
orders in the book with the same price. The order which was entered earliest at a given price limit gets executed first. Pro
rata matching is used for products with low intraday volatility of best bid and best ask price. Pro rata matching ensures
constant access for orders of all sizes. We demonstrate how a multiagent-based model of financial market can be used to study
microscopic aspects of order books.
12/2010: pages 65-72;
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ABSTRACT: 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.
Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences 12/2010; 368(1933):5707-19. · 2.77 Impact Factor
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Tobias Preis
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ABSTRACT: 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.
Journal of Physics Conference Series 06/2010; 221(1):012019.
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[show abstract]
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ABSTRACT: For an intriguing variety of switching processes in nature, the underlying complex system abruptly changes at a specific point
from one state to another in a highly discontinuous fashion. Financial market fluctuations are characterized by many abrupt
switchings creating increasing trends (“bubble formation”) and decreasing trends (“bubble collapse”), on time scales ranging
from macroscopic bubbles persisting for hundreds of days to microscopic bubbles persisting only for very short time scales.
Our analysis is based on a German DAX Future data base containing 13,991,275 transactions recorded with a time resolution
of 10− 2 s. For a parallel analysis, we use a data base of all S&P500 stocks providing 2,592,531 daily closing prices. We ask whether
these ubiquitous switching processes have quantifiable features independent of the time horizon studied. We find striking
scale-free behavior of the volatility after each switching occurs. We interpret our findings as being consistent with time-dependent
collective behavior of financial market participants. We test the possible universality of our result by performing a parallel
analysis of fluctuations in transaction volume and time intervals between trades. We show that these financial market switching
processes have features similar to those present in phase transitions. We find that the well-known catastrophic bubbles that
occur on large time scales – such as the most recent financial crisis – are no outliers but in fact single dramatic representatives
caused by the formation of upward and downward trends on time scales varying over nine orders of magnitude from the very large
down to the very small.
12/2009: pages 3-26;
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ABSTRACT: The compute unified device architecture is an almost conventional programming approach for managing computations on a graphics processing unit (GPU) as a data-parallel computing device. With a maximum number of 240 cores in combination with a high memory bandwidth, a recent GPU offers resources for computational physics. We apply this technology to methods of fluctuation analysis, which includes determination of the scaling behavior of a stochastic process and the equilibrium autocorrelation function. Additionally, the recently introduced pattern formation conformity (Preis T et al 2008 Europhys. Lett. 82 68005), which quantifies pattern-based complex short-time correlations of a time series, is calculated on a GPU and analyzed in detail. Results are obtained up to 84 times faster than on a current central processing unit core. When we apply this method to high-frequency time series of the German BUND future, we find significant pattern-based correlations on short time scales. Furthermore, an anti-persistent behavior can be found on short time scales. Additionally, we compare the recent GPU generation, which provides a theoretical peak performance of up to roughly 1012 floating point operations per second with the previous one.
New Journal of Physics 09/2009; 11(9):093024. · 4.18 Impact Factor
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ABSTRACT: We recently introduced a realistic order book model [T. Preis, Europhys. Lett. 75, 510 (2006)] which is able to generate the stylized facts of financial markets. We analyze this model in detail, explain the consequences of the use of different groups of traders, and focus on the foundation of a nontrivial Hurst exponent based on the introduction of a market trend. Our order book model supports the theoretical argument that a nontrivial Hurst exponent implies not necessarily long-term correlations. A coupling of the order placement depth to the market trend can produce fat tails, which can be described by a truncated Lévy distribution.
Physical Review E 08/2007; 76(1 Pt 2):016108. · 2.26 Impact Factor
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ABSTRACT: The compute unified device architecture (CUDA) is a programming approach for performing scientific calculations on a graphics processing unit (GPU) as a data-parallel computing device. The programming interface allows to implement algorithms using extensions to standard C language. With continuously increased number of cores in combination with a high memory bandwidth, a recent GPU offers incredible resources for general purpose computing. First, we apply this new technology to Monte Carlo simulations of the two dimensional ferromagnetic square lattice Ising model. By implementing a variant of the checkerboard algorithm, results are obtained up to 60 times faster on the GPU than on a current CPU core. An implementation of the three dimensional ferromagnetic cubic lattice Ising model on a GPU is able to generate results up to 35 times faster than on a current CPU core. As proof of concept we calculate the critical temperature of the 2D and 3D Ising model using finite size scaling techniques. Theoretical results for the 2D Ising model and previous simulation results for the 3D Ising model can be reproduced.
Journal of Computational Physics.
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ABSTRACT: A Modern Graphics Processing unit (GPU) is able to perform massively parallel scientific computations at low cost. We extend our implementation of the checkerboard algorithm for the two-dimensional Ising model [T. Preis et al., Journal of Chemical Physics 228 (2009) 4468–4477] in order to overcome the memory limitations of a single GPU which enables us to simulate significantly larger systems. Using multi-spin coding techniques, we are able to accelerate simulations on a single GPU by factors up to 35 compared to an optimized single Central Processor Unit (CPU) core implementation which employs multi-spin coding. By combining the Compute Unified Device Architecture (CUDA) with the Message Parsing Interface (MPI) on the CPU level, a single Ising lattice can be updated by a cluster of GPUs in parallel. For large systems, the computation time scales nearly linearly with the number of GPUs used. As proof of concept we reproduce the critical temperature of the 2D Ising model using finite size scaling techniques.
Computer Physics Communications.
