March  2017, 10(1): 239-261. doi: 10.3934/krm.2017010

A kinetic equation for economic value estimation with irrationality and herding

1. 

Department of Mathematics, University of Sussex, Pevensey Ⅱ, Brighton BN1 9QH, United Kingdom

2. 

Institute for Analysis and Scientific Computing, Vienna University of Technology, Wiedner Hauptstraße 8-10,1040 Wien, Austria

Received  January 2016 Revised  May 2016 Published  November 2016

Fund Project: The first author is supported by the Leverhulme Trust research project grant "Novel discretisations for higher-order nonlinear PDE" (RPG-2015-69). The second and third authors acknowledge partial support from the European Union in the FP7-PEOPLE-2012-ITN Program under Grant Agreement Number 304617 and from the Austrian Science Fund (FWF), grants P22108, P24304, and W1245

A kinetic inhomogeneous Boltzmann-type equation is proposed to model the dynamics of the number of agents in a large market depending on the estimated value of an asset and the rationality of the agents. The interaction rules take into account the interplay of the agents with sources of public information, herding phenomena, and irrationality of the individuals. In the formal grazing collision limit, a nonlinear nonlocal Fokker-Planck equation with anisotropic (or incomplete) diffusion is derived. The existence of global-in-time weak solutions to the Fokker-Planck initial-boundary-value problem is proved. Numerical experiments for the Boltzmann equation highlight the importance of the reliability of public information in the formation of bubbles and crashes. The use of Bollinger bands in the simulations shows how herding may lead to strong trends with low volatility of the asset prices, but eventually also to abrupt corrections.

Citation: Bertram Düring, Ansgar Jüngel, Lara Trussardi. A kinetic equation for economic value estimation with irrationality and herding. Kinetic & Related Models, 2017, 10 (1) : 239-261. doi: 10.3934/krm.2017010
References:
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G. Dimarco and L. Pareschi, High order asymptotic-preserving schemes for the Boltzmann equation, Comptes Rendus Methématique, 350 (2012), 481-486.  doi: 10.1016/j.crma.2012.05.010.  Google Scholar

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[18]

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M. EscobedoJ.-L. Vázquez and E. Zuazua, Entropy solutions for diffusion-convection equations with partial diffusivity, Trans. Amer. Math. Soc., 343 (1994), 829-842.  doi: 10.1090/S0002-9947-1994-1225573-2.  Google Scholar

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S. FongJ. Tai and Y.W. Si, Trend following algorithms for technical trading in stock markets, J. Emerging Tech. Web Intell., 3 (2011), 136-145.  doi: 10.4304/jetwi.3.2.136-145.  Google Scholar

[21]

W. Hamilton, Geometry for the selfish herd, J. Theor. Biol., 31 (1971), 295-311.  doi: 10.1016/0022-5193(71)90189-5.  Google Scholar

[22]

T. HillenK. Painter and M. Winkler, Anisotropic diffusion in oriented environments can lead to singularity formation, Europ. J. Appl. Math., 24 (2013), 371-413.  doi: 10.1017/S0956792512000447.  Google Scholar

[23]

D. Hirshleifer, Investor psychology and asset pricing, J. Finance, 56 (2001), 1533-1597.   Google Scholar

[24]

P. JahangiriA. NejatJ. Samadi and A. Aboutalebi, A high-order Monte Carlo algorithm for the direct simulation of Boltzmann equation, J. Comput. Phys., 231 (2012), 4578-4596.  doi: 10.1016/j.jcp.2012.02.029.  Google Scholar

[25]

T. Lux and M. Marchesi, Scaling and criticality in a stochastic multi-agent model of a financial market, Nature, 397 (1999), 498-500.   Google Scholar

[26]

D. Maldarella and L. Pareschi, Kinetic models for socio-economic dynamics of speculative markets, Physica A, 391 (2012), 715-730.  doi: 10.1016/j.physa.2011.08.013.  Google Scholar

[27]

L. Pareschi and G. Toscani, Interacting Multiagent Systems, Kinetic Equations and Monte Carlo methods, Oxford University Press, Oxford, 2014. Google Scholar

[28]

R. RaafatN. Chater and C. Frith, Herding in humans, Trends Cognitive Sci., 13 (2009), 420-428.  doi: 10.1016/j.tics.2009.08.002.  Google Scholar

[29]

L. Rook, An economic psychological approach to herd behavior, J. Econ., 40 (2006), 75-95.  doi: 10.1080/00213624.2006.11506883.  Google Scholar

[30]

R. Shiller, Irrational Exuberance Princeton University Press, Princeton, 2015. doi: 10.1515/9781400865536.  Google Scholar

[31]

G. Toscani, Kinetic models of opinion formation, Commun. Math. Sci., 4 (2006), 481-496.  doi: 10.4310/CMS.2006.v4.n3.a1.  Google Scholar

[32]

E. Zeidler, Nonlinear Functional Analysis and Applications Vol. ⅡA. Springer, New York, 1990. doi: 10.1007/978-1-4612-0985-0.  Google Scholar

show all references

References:
[1]

E. AltshulerO. RamosY. NúñezJ. FernándezA. Batista-Leyva and C. Noda, Symmetry breaking in escaping ants, Aner. Natur, 166 (2005), 643-649.  doi: 10.1086/498139.  Google Scholar

[2]

A. Amadori and R. Natalini, Entropy solutions to a strongly degenerate anisotropic convection-diffusion equation with application to utility theory, J. Math. Anal. Appl., 284 (2003), 511-531.  doi: 10.1016/S0022-247X(03)00339-1.  Google Scholar

[3]

C. Avery and P. Zemsky, Multidimensional uncertainty and herd behavior in financial markets, Amer. Econ. Rev., 88 (1998), 724-748.   Google Scholar

[4]

A. Banerjee, A simple model of herd behavior, Quart. J. Econ., 107 (1992), 797-817.  doi: 10.2307/2118364.  Google Scholar

[5]

S. BickhchandaniD. Hirshleifter and I. Welch, A theory of fads, fashion, custim, and cultural change as informational cascades, J. Polit. Econ., 100 (1992), 992-1026.   Google Scholar

[6]

G. Bird, Molecular Gas Dynamics and the Direct Simulation of Gas Flows Oxford University Press, Oxford, 1995.  Google Scholar

[7]

L. Boudin and F. Salvarani, A kinetic approach to the study of opinion formation, ESAIM Math. Mod. Anal. Num., 43 (2009), 507-522.  doi: 10.1051/m2an/2009004.  Google Scholar

[8]

M. Brunnermeier, Asset Pricing Under Asymmetric Information: Bubbles, Crashes, Technical Analysis, and Herding Oxford University Press, Oxford, 2001. doi: 10.1093/0198296983.001.0001.  Google Scholar

[9]

M. BurgerP. Markowich and J.-F. Pietschmann, Continuous limit of a crowd motion and herding model: Analysis and numerical simulations, Kinetic Related Models, 4 (2011), 1025-1047.  doi: 10.3934/krm.2011.4.1025.  Google Scholar

[10]

V. ComincioliL. Della Croce and G. Toscani, A Boltzmann-like equation for choice formation, Kinet. Relat. Models, 2 (2009), 135-149.  doi: 10.3934/krm.2009.2.135.  Google Scholar

[11]

S. CordierL. Pareschi and G. Toscani, On a kinetic model for a simple market economy, J. Stat. Phys., 120 (2005), 253-277.  doi: 10.1007/s10955-005-5456-0.  Google Scholar

[12]

P. DegondC. Appert-RollandM. MoussaidJ. Pettré and G. Theraulaz, A hierarchy of heuristic-based models of crowd dynamics, J. Stat. Phys., 152 (2013), 1033-1068.  doi: 10.1007/s10955-013-0805-x.  Google Scholar

[13]

M. Delitala and T. Lorenzi, A mathematical model for value estimation with public information and herding, Kinet. Relat. Models, 7 (2014), 29-44.  doi: 10.3934/krm.2014.7.29.  Google Scholar

[14]

A. Devenow and I. Welch, Rational herding in financial economics, Europ. Econ. Rev., 40 (1996), 603-615.  doi: 10.1016/0014-2921(95)00073-9.  Google Scholar

[15]

G. Dimarco and L. Pareschi, High order asymptotic-preserving schemes for the Boltzmann equation, Comptes Rendus Methématique, 350 (2012), 481-486.  doi: 10.1016/j.crma.2012.05.010.  Google Scholar

[16]

B. DüringP. MarkowichJ.-F. Pietschmann and M.-T. Wolfram, Boltzmann and Fokker-Planck equations modelling opinion formation in the presence of strong leaders, Proc. Roy. Soc. Lond. Ser. A Math. Phys. Eng. Sci., 465 (2009), 3687-3708.  doi: 10.1098/rspa.2009.0239.  Google Scholar

[17]

B. Düring and M. -T. Wolfram, Opinion dynamics: Inhomogeneous Boltzmann-type equations modelling opinion leadership and political segregation Proc. Roy. Soc. Lond. Ser. A Math. Phys. Eng. Sci. 471 (2015), 20150345, 21pp. doi: 10.1098/rspa.2015.0345.  Google Scholar

[18]

J. DyerA. JohanssonD. HelbingI. Couzin and J. Krause, Leadership, consensus decision making and collective behaviour in humans, Phil. Trans. Roy. Soc. B: Biol. Sci., 364 (2009), 781-789.  doi: 10.1098/rstb.2008.0233.  Google Scholar

[19]

M. EscobedoJ.-L. Vázquez and E. Zuazua, Entropy solutions for diffusion-convection equations with partial diffusivity, Trans. Amer. Math. Soc., 343 (1994), 829-842.  doi: 10.1090/S0002-9947-1994-1225573-2.  Google Scholar

[20]

S. FongJ. Tai and Y.W. Si, Trend following algorithms for technical trading in stock markets, J. Emerging Tech. Web Intell., 3 (2011), 136-145.  doi: 10.4304/jetwi.3.2.136-145.  Google Scholar

[21]

W. Hamilton, Geometry for the selfish herd, J. Theor. Biol., 31 (1971), 295-311.  doi: 10.1016/0022-5193(71)90189-5.  Google Scholar

[22]

T. HillenK. Painter and M. Winkler, Anisotropic diffusion in oriented environments can lead to singularity formation, Europ. J. Appl. Math., 24 (2013), 371-413.  doi: 10.1017/S0956792512000447.  Google Scholar

[23]

D. Hirshleifer, Investor psychology and asset pricing, J. Finance, 56 (2001), 1533-1597.   Google Scholar

[24]

P. JahangiriA. NejatJ. Samadi and A. Aboutalebi, A high-order Monte Carlo algorithm for the direct simulation of Boltzmann equation, J. Comput. Phys., 231 (2012), 4578-4596.  doi: 10.1016/j.jcp.2012.02.029.  Google Scholar

[25]

T. Lux and M. Marchesi, Scaling and criticality in a stochastic multi-agent model of a financial market, Nature, 397 (1999), 498-500.   Google Scholar

[26]

D. Maldarella and L. Pareschi, Kinetic models for socio-economic dynamics of speculative markets, Physica A, 391 (2012), 715-730.  doi: 10.1016/j.physa.2011.08.013.  Google Scholar

[27]

L. Pareschi and G. Toscani, Interacting Multiagent Systems, Kinetic Equations and Monte Carlo methods, Oxford University Press, Oxford, 2014. Google Scholar

[28]

R. RaafatN. Chater and C. Frith, Herding in humans, Trends Cognitive Sci., 13 (2009), 420-428.  doi: 10.1016/j.tics.2009.08.002.  Google Scholar

[29]

L. Rook, An economic psychological approach to herd behavior, J. Econ., 40 (2006), 75-95.  doi: 10.1080/00213624.2006.11506883.  Google Scholar

[30]

R. Shiller, Irrational Exuberance Princeton University Press, Princeton, 2015. doi: 10.1515/9781400865536.  Google Scholar

[31]

G. Toscani, Kinetic models of opinion formation, Commun. Math. Sci., 4 (2006), 481-496.  doi: 10.4310/CMS.2006.v4.n3.a1.  Google Scholar

[32]

E. Zeidler, Nonlinear Functional Analysis and Applications Vol. ⅡA. Springer, New York, 1990. doi: 10.1007/978-1-4612-0985-0.  Google Scholar

Figure 1.  Left: Percentage of bubbles (left) and crashes (right) depending on the choice of $\alpha$ and $\beta$. The parameters are $R=0.025$, $W=0.5$, $\delta=\kappa=1$
Figure 2.  Left: percentage of bubbles for varying $\alpha$ ($\beta=0.25$) and constant $\alpha$ ($\beta=0.5$ and $\beta=0.05$). Right: probability distribution function for the logarithmic return (in blue) and normal distribution (in magenta) with the same mean and variance as the return
Figure 3.  Mean asset value $m_w(f(t))$ versus time $t$ for $\alpha=0.05$ (left) and $\alpha=0.5$ (right). The function $W(t)$ is represented by the solid line in between the dashed lines which represent the functions $W(t)+R$ and $W(t)-R$. The parameters are $\beta=0.25$, $R=0.025$, $\delta=2$, $\kappa=1$
Figure 4.  Mean asset value $m_w(f(t))$ versus time for $\delta=0.01$ (left) and $\delta=100$ (right) with $\alpha=0.25$, $\beta=0.2$, $R=0.025$, $\kappa=1$
Figure 5.  Mean asset value $m_w(f(t))$ and Bollinger bands $R^\pm(t)$ versus time for $\alpha=0.05$ (left) and $\alpha=0.2$ (right). The parameters are $\beta=0.25$, $W=0.5$, $\delta=1$, $\kappa=1$
Figure 6.  Mean asset value $m_w(f(t))$ (left column) and Bollinger bands $R^\pm(t)$ (right column) versus time. The function $W(t)$ has a jump at $t=0.2$. The parameters are $\alpha=0.05$, $\beta=0.25$, $R=0.025$, $\delta=\kappa=1$. Upper row: $\eta=\pm 0.06$, lower row: $\eta=\pm 0.18$
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