Investment Strategies Used as Spectroscopy of Financial Markets Reveal New Stylized Facts

Zhou, Wei‐Xing; Mu, Guo-Hua; Sornette, Didier; Chen, Wei

doi:10.2139/ssrn.1908632

Cited by 4 publications

(3 citation statements)

References 48 publications

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“…The possibility of accessing large amounts of historical financial data has spurred the interest of scientists in various fields, including physics. Plenty of results have been obtained with physical concepts, methods and models [1][2][3][4][5][6][7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Agent-Based Model with Asymmetric Trading and Herding for Complex Financial Systems

2013

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BackgroundFor complex financial systems, the negative and positive return-volatility correlations, i.e., the so-called leverage and anti-leverage effects, are particularly important for the understanding of the price dynamics. However, the microscopic origination of the leverage and anti-leverage effects is still not understood, and how to produce these effects in agent-based modeling remains open. On the other hand, in constructing microscopic models, it is a promising conception to determine model parameters from empirical data rather than from statistical fitting of the results.MethodsTo study the microscopic origination of the return-volatility correlation in financial systems, we take into account the individual and collective behaviors of investors in real markets, and construct an agent-based model. The agents are linked with each other and trade in groups, and particularly, two novel microscopic mechanisms, i.e., investors’ asymmetric trading and herding in bull and bear markets, are introduced. Further, we propose effective methods to determine the key parameters in our model from historical market data.ResultsWith the model parameters determined for six representative stock-market indices in the world, respectively, we obtain the corresponding leverage or anti-leverage effect from the simulation, and the effect is in agreement with the empirical one on amplitude and duration. At the same time, our model produces other features of the real markets, such as the fat-tail distribution of returns and the long-term correlation of volatilities.ConclusionsWe reveal that for the leverage and anti-leverage effects, both the investors’ asymmetric trading and herding are essential generation mechanisms. Among the six markets, however, the investors’ trading is approximately symmetric for the five markets which exhibit the leverage effect, thus contributing very little. These two microscopic mechanisms and the methods for the determination of the key parameters can be applied to other complex systems with similar asymmetries.

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Section: Introductionmentioning

confidence: 99%

Agent-Based Model with Asymmetric Trading and Herding for Complex Financial Systems

2013

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“…Time series of financial data exhibit nontrivial statistical properties. Many of these anomalous properties appear to be universal and a variety of the so-called stylized facts has been established [43,11,45,12,44,26,15,7,66]. The probability density function (PDF) of the return and other financial variables are successfully described by the distributions of the nonextensive statistical mechanics [58,17,9,21,23].…”

Section: Introductionmentioning

confidence: 99%

Nonextensive Statistical Mechanics Distributions and Dynamics of Financial Observables From the Nonlinear Stochastic Differential Equations

Ruseckas

Gontis

Kaulakys

2012

Advs. Complex Syst.

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We present nonlinear stochastic differential equations, generating processes with the q-exponential and q-Gaussian distributions of the observables, i.e. with the long-range power-law autocorrelations and 1/f β power spectral density. Similarly, the Tsallis q-distributions may be obtained in the superstatistical framework as a superposition of different local dynamics at different time intervals. In such approach, the average of the stochastic variable is generated by the nonlinear stochastic process, while the local distribution of the signal is exponential or Gaussian one, conditioned by the slow average. Further we analyze relevance of the generalized and adapted equations for modeling the financial processes. We model the inter-trade durations, the trading activity and the normalized return using the superstatistical approaches with the exponential and normal distributions of the local signals driven by the nonlinear stochastic process.Many complex systems show large fluctuations that follow non-Gaussian, heavytailed distributions with the power-law temporal correlations, scaling and the fractal features [42,44,41]. These distributions, scaling, self-similarity and fractality are often related with the Tsallis nonextensive statistical mechanics [60,48,61,62,57] and with 1/f β noise (see, e.g. [41,30,29,14,37,54], and references herein). Often nonextensive statistical mechanics represents a consistent theoretical background for the investigation of complex systems, [61,62,57]. On the other hand, a usual way to describe stochastic evolution and the properties of complex systems is by the stochastic differential equations (SDE) [16,52,13,64,65]. Such nondeterministic equations of motion are used for modeling the financial systems, as well [38,44,27,20,19,21,22].There are empirically established facts that the trading activity, trading volume, and volatility are stochastic variables with the long-range correlation [10,46,15]. However, these aspects are not accounted for in some widely used models of the financial systems. Moreover, the trading volume and the trading activity are positively correlated with the market volatility, while the trading volume and volatility show the same type of the long memory behavior [40], including 1/f β noise [21,22].The purpose of this article is to model the inter-trade durations, the trading activity and the normalized return using the superstatistical approaches with the exponential and normal distributions of the local signals driven by the nonlinear stochastic process. We present a class of nonlinear stochastic differential equations giving the power-law behavior of the probability density function (PDF) of the signal intensity and of the power spectral density (1/f β noise) in any desirably wide range of frequency. Modifications of these equations by introducing an additional parameter yields Tsallis distributions preserving 1/f β behavior of the power spectral density. The superstatistical framework [4,59,1,2,63,24,5] using a fast dynamics with the slowly changing parameter...

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“…Financial market dynamics is complex in part because of the very large variety of timescales at play. Both traders and volatility feedback loops are known to have widely distributed timescales [Lynch and Zumbach, 2003, Lillo, 2007, Zhou et al, 2011, Tumminello et al, 2012, Challet et al, 2016. Accordingly, investigating how timescales interact reveals some of the fundamental dynamical ingredients of price dynamics.…”

Section: Introductionmentioning

confidence: 99%

Large Large-Trader Activity Weakens the Long Memory of Limit Order Markets

Primicerio

Challet

2018

Mark. Microstructure Liq.

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Using more than 6.7 billions of trades, we explore how the tick-by-tick dynamics of limit order books depends on the aggregate actions of large investment funds on a much larger (quarterly) timescale. In particular, we find that the well-established long memory of market order signs is markedly weaker when large investment funds trade either in a directional way and even weaker when their aggregate participation ratio is large. Conversely, we investigate to what respect a weaker memory of market order signs predicts that an asset is being actively traded by large funds. Theoretical arguments suggest two simple mechanisms that contribute to the observed effect: a larger number of active meta-orders and a modification of the distribution of size of meta-orders. Empirical evidence suggests that the number of active meta-orders is the most important contributor to the loss of market order sign memory. arXiv:1803.08390v1 [q-fin.ST]

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Investment Strategies Used as Spectroscopy of Financial Markets Reveal New Stylized Facts

Cited by 4 publications

References 48 publications

Agent-Based Model with Asymmetric Trading and Herding for Complex Financial Systems

Agent-Based Model with Asymmetric Trading and Herding for Complex Financial Systems

Nonextensive Statistical Mechanics Distributions and Dynamics of Financial Observables From the Nonlinear Stochastic Differential Equations

Large Large-Trader Activity Weakens the Long Memory of Limit Order Markets

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