By Fabrizio Pomponio, Frédéric Abergel (auth.), Frédéric Abergel, Bikas K. Chakrabarti, Anirban Chakraborti, Manipushpak Mitra (eds.)
The basic objective of the booklet is to provide the tips and examine findings of lively researchers from a variety of groups (physicists, economists, mathematicians, monetary engineers) operating within the box of "Econophysics", who've undertaken the duty of modelling and reading order-driven markets. Of fundamental curiosity in those reviews are the mechanisms resulting in the statistical regularities ("stylized facts") of fee information. effects touching on different vital concerns reminiscent of market effect, the profitability of buying and selling recommendations, or mathematical models for microstructure results, also are awarded. a number of top researchers in those fields document on their fresh paintings and likewise evaluate the modern literature. a few ancient views, reviews and debates on fresh matters in Econophysics learn also are included.
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Extra info for Econophysics of Order-driven Markets: Proceedings of Econophys-Kolkata V
As we have seen in the previous paragraph, sub-penny trading is a reality. However, the previous graph showed average ﬁgures on all NASDAQ100 index stocks. We here want to show that the previous distribution is actually strongly correlated with the stock’s value. In order to do so, we present on the next page a graph showing a similar distribution as the one shown before, but calculated on a list of more than 440 stocks quoted on the NASDAQ and priced between 1 $ and 5 $. We can observe that the ﬁrst and last bars of the distribution have a relative weight much more important for those stocks than for the NASDAQ100 stocks.
Doyne Farmer: The Long Memory of the Efﬁcient Market. Studies in Nonlinear Dynamics & Econometrics, vol. 8, issue 3 (2004) 5. -P. Bouchaud, J. Doyne Farmer, F. Lillo: How markets slowly digest changes in supply and demand. Handbook of ﬁnancial markets: dynamics and evolution Are the Trading Volume and the Number of Trades Distributions Universal? Vikram S. Vijayaraghavan and Sitabhra Sinha Abstract. Analysis of dynamical phenomena in ﬁnancial markets have revealed the existence of several features that appear to be invariant with respect to details of the speciﬁc markets being considered.
Vijayaraghavan, S. Sinha Empirical Data Quantiles 10 4 (a) 4 (b) (c) 5 2 2 0 0 0 −5 −2 −2 −10 −5 10 0 −4 −4 −5 −5 0 5 5 Standard Normal Theoretical Quantiles 4 (d) 0 5 (f) 2 2 0 0 0 −5 −2 −2 0 5 4 (e) 5 −10 −5 0 −4 −4 −5 −5 0 5 5 Standard Normal Theoretical Quantiles Fig. 4 Q-Q plots comparing the distributions of normalized and de-trended volume (a-c) and number of trades (d-f) for the entire market to a standard normal distribution at different scales of temporal aggregation. The aggregation is over 1 day for (a,d), 5 days for (b,e) and 10 days for (c,f).