Econophysics Approaches to Large-Scale Business Data and by Tobias Preis, H. Eugene Stanley (auth.), Misako Takayasu,

By Tobias Preis, H. Eugene Stanley (auth.), Misako Takayasu, Tsutomu Watanabe, Hideki Takayasu (eds.)

In contemporary years, as a part of the expanding “informationization” of and the financial system, organisations were collecting titanic quantities of particular information akin to high-frequency transaction info in nancial markets and point-of-sale details onindividualitems in theretail zone. Similarly,vast amountsof information arenow ava- capable on company networks in response to inter rm transactions and shareholdings. some time past, a majority of these info have been studied purely by way of economists and administration students. extra lately, even though, researchers from different elds, resembling physics, arithmetic, and knowledge sciences, became attracted to this sort of info and, according to novel empirical methods to looking for regularities and “laws” corresponding to these within the average sciences, have produced exciting effects. This ebook is the court cases of the foreign convention THICCAPFA7 that was once titled “New methods to the research of Large-Scale enterprise and E- nomic Data,” held in Tokyo, March 1–5, 2009. The letters THIC denote the Tokyo Tech (Tokyo Institute of Technology)–Hitotsubashi Interdisciplinary convention. The convention sequence, titled APFA (Applications of Physics in monetary Analysis), specializes in the research of large-scale financial facts. It has regularly introduced physicists and economists jointly to switch viewpoints and adventure (APFA1 in Dublin 1999, APFA2 in Liege ` 2000, APFA3 in London 2001, APFA4 in Warsaw 2003, APFA5 in Torino 2006, and APFA6 in Lisbon 2007). the purpose of the conf- ence is to set up basic analytical ideas and knowledge assortment tools, making an allowance for the consequences from a number of educational disciplines.

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To avoid overlapping, symbols were shifted downwards by a factor of 10 (triangles), and 100 (squares). rj / for RQ D 10 and 30 (symbols correspond to those in (b)). r0 / in units of RQ versus r0 =RQ for the same RQ values as in (c). The curve for RQ D 30 was raised by a factor of 10 to avoid overlapping symbols behaves qualitatively the same as the model data (see Fig. 7c) but due to less statistics finite-size effects are more pronounced. The conditional return periods shown in Fig. 8d agree very well with the model data, but due to less statistics very large values of r0 =RQ cannot be tested.

1007/s10955-009-9914-y 50. Preis T, Stanley HE (2009) APCTP Bulletin 23–24:18 Nonlinear Memory and Risk Estimation in Financial Records Armin Bunde and Mikhail I. Bogachev Abstract It is well known that financial data sets are multifractal and governed by nonlinear correlations. Here we are interested in the daily returns of a financial asset and in the way the occurrence of large gains or losses is triggered by the nonlinear memory. To this end, we study the statistics of the return intervals between gains (or losses) above a certain threshold Q.

2/ depend only on the non-linear correlations and therefore can be used to characterize them. s/=s 1=2 for q D 1; 2 and 5. s/ follows the anticipated power-law scaling, with different exponents for the different values of q. 2 102 103 s 104 105 Fig. 5 Analysis of the multifractal cascade model: MF-DFA fluctuation function for q D 1 (circles), 2 (squares), and 5 (diamonds). s/ of the data is shown in the inset Nonlinear Memory and Risk Estimation in Financial Records 33 of linear correlations in the data.

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