Econophysics

Econophysics is a heterodox interdisciplinary research field, applying theories and methods originally developed by physicists in order to solve problems in economics, usually those including uncertainty or stochastic processes and nonlinear dynamics. Some of its application to the study of financial markets has also been termed statistical finance referring to its roots in statistical physics. Econophysics is closely related to social physics.

History

Physicists' interest in the social sciences is not new; Daniel Bernoulli, as an example, was the originator of utility-based preferences. One of the founders of neoclassical economic theory, former Yale University Professor of Economics Irving Fisher, was originally trained under the renowned Yale physicist, Josiah Willard Gibbs.[1] Likewise, Jan Tinbergen, who won the first Nobel Memorial Prize in Economic Sciences in 1969 for having developed and applied dynamic models for the analysis of economic processes, studied physics with Paul Ehrenfest at Leiden University. In particular, Tinbergen developed the gravity model of international trade that has become the workhorse of international economics.

Econophysics was started in the mid-1990s by several physicists working in the subfield of statistical mechanics. Unsatisfied with the traditional explanations and approaches of economists – which usually prioritized simplified approaches for the sake of soluble theoretical models over agreement with empirical data – they applied tools and methods from physics, first to try to match financial data sets, and then to explain more general economic phenomena.

One driving force behind econophysics arising at this time was the sudden availability of large amounts of financial data, starting in the 1980s. It became apparent that traditional methods of analysis were insufficient – standard economic methods dealt with homogeneous agents and equilibrium, while many of the more interesting phenomena in financial markets fundamentally depended on heterogeneous agents and far-from-equilibrium situations.

The term "econophysics" was coined by H. Eugene Stanley, to describe the large number of papers written by physicists in the problems of (stock and other) markets, in a conference on statistical physics in Kolkata (erstwhile Calcutta) in 1995 and first appeared in its proceedings publication in Physica A 1996.[2][3] The inaugural meeting on econophysics was organised in 1998 in Budapest by János Kertész and Imre Kondor. The first book on econophysics was by R. N. Mantegna & H. E. Stanley in 2000.[4]

The almost regular meeting series on the topic include: ECONOPHYS-KOLKATA (held in Kolkata & Delhi),[5] Econophysics Colloquium, ESHIA/ WEHIA.

In recent years network science, heavily reliant on analogies from statistical mechanics, has been applied to the study of productive systems. That is the case with the works done at the Santa Fe Institute in European Funded Research Projects as Forecasting Financial Crises and the Harvard-MIT Observatory of Economic Complexity

If "econophysics" is taken to denote the principle of applying statistical mechanics to economic analysis, as opposed to a particular literature or network, priority of innovation is probably due to Emmanuel Farjoun and Moshé Machover (1983). Their book Laws of Chaos: A Probabilistic Approach to Political Economy proposes dissolving (their words) the transformation problem in Marx's political economy by re-conceptualising the relevant quantities as random variables.[6]

If, on the other hand, "econophysics" is taken to denote the application of physics to economics, one can consider the works of Léon Walras and Vilfredo Pareto as part of it. Indeed, as shown by Bruna Ingrao and Giorgio Israel, general equilibrium theory in economics is based on the physical concept of mechanical equilibrium.

Econophysics has nothing to do with the "physical quantities approach" to economics, advocated by Ian Steedman and others associated with neo-Ricardianism. Notable econophysicists are Jean-Philippe Bouchaud, Bikas K Chakrabarti, J. Doyne Farmer, Diego Garlaschelli, Dirk Helbing, János Kertész, Francis Longstaff, Rosario N. Mantegna, Matteo Marsili, Joseph L. McCauley, Enrico Scalas, Didier Sornette, H. Eugene Stanley, Victor Yakovenko and Yi-Cheng Zhang. Particularly noteworthy among the formal courses on econophysics is the one offered by Diego Garlaschelli at the Physics Department of the Leiden University,[7][8] from where the first Nobel-laureate in economics Jan Tinbergen came. From September 2014 King's College has awarded the first position of Full Professor in Econophysics.

Basic tools

Basic tools of econophysics are probabilistic and statistical methods often taken from statistical physics.

Physics models that have been applied in economics include the kinetic theory of gas (called the kinetic exchange models of markets [9]), percolation models, chaotic models developed to study cardiac arrest, and models with self-organizing criticality as well as other models developed for earthquake prediction.[10] Moreover, there have been attempts to use the mathematical theory of complexity and information theory, as developed by many scientists among whom are Murray Gell-Mann and Claude E. Shannon, respectively.

For potential games, it has been shown that an emergence-producing equilibrium based on information via Shannon information entropy produces the same equilibrium measure (Gibbs measure from statistical mechanics) as a stochastic dynamical equation, both of which are based on bounded rationality models used by economists. The fluctuation-dissipation theorem connects the two to establish a concrete correspondence of "temperature", "entropy", "free potential/energy", and other physics notions to an economics system. The statistical mechanics model is not constructed a-priori - it is a result of a bounded rational assumption and modeling on existing neoclassical models. It has been used to prove the "inevitability of collusion" result of Huw Dixon in a case for which the neoclassical version of the model does not predict collusion.[11] Here the demand is increasing, as with Veblen goods or stock buyers with the "hot hand" fallacy preferring to buy more successful stocks and sell those that are less successful. [12]

Quantifiers derived from information theory were used in several papers by econophysicist Aurelio F. Bariviera and coauthors in order to assess the degree in the informational efficiency of stock markets. In a paper published in Physica A[13]

Zunino et al. use an innovative statistical tool in the financial literature: the complexity-entropy causality plane. This Cartesian representation establish an efficiency ranking of different markets and distinguish different bond market dynamics. Moreover, the authors conclude that the classification derived from the complexity-entropy causality plane is consistent with the qualifications assigned by major rating companies to the sovereign instruments. A similar study developed by Bariviera et al.[14] explore the relationship between credit ratings and informational efficiency of a sample of corporate bonds of US oil and energy companies using also the complexity–entropy causality plane. They find that this classification agrees with the credit ratings assigned by Moody's.

Another good example is random matrix theory, which can be used to identify the noise in financial correlation matrices. One paper has argued that this technique can improve the performance of portfolios, e.g., in applied in portfolio optimization.[15]

There are, however, various other tools from physics that have so far been used, such as fluid dynamics, classical mechanics and quantum mechanics (including so-called classical economy, quantum economics and quantum finance),[16] and the path integral formulation of statistical mechanics.[17]

The concept of economic complexity index, introduced by the MIT physicist Cesar A. Hidalgo and the Harvard economist Ricardo Hausmann and made available at MIT's Observatory of Economic Complexity, has been devised as a predictive tool for economic growth. According to the estimates of Hausmann and Hidalgo, the ECI is far more accurate in predicting GDP growth than the traditional governance measures of the World Bank.[18]

There are also analogies between finance theory and diffusion theory. For instance, the Black–Scholes equation for option pricing is a diffusion-advection equation (see however [19][20] for a critique of the Black–Scholes methodology). The Black–Scholes theory can be extended to provide an analytical theory of main factors in economic activities.[17]

Influence

Papers on econophysics have been published primarily in journals devoted to physics and statistical mechanics, rather than in leading economics journals. Mainstream economists have generally been unimpressed by this work.[21] Some economists, including Mauro Gallegati, Steve Keen, Paul Ormerod, and Alan Kirman have shown more interest, but also criticized some trends in econophysics.

In contrast, econophysics is having some impacts on the more applied field of quantitative finance, whose scope and aims significantly differ from those of economic theory. Various econophysicists have introduced models for price fluctuations in physics of financial markets or original points of view on established models.[19][22][23] Also several scaling laws have been found in various economic data.[24][25][26]

Main results

Presently, one of the main results of econophysics comprises the explanation of the "fat tails" in the distribution of many kinds of financial data as a universal self-similar scaling property (i.e. scale invariant over many orders of magnitude in the data),[27] arising from the tendency of individual market competitors, or of aggregates of them, to exploit systematically and optimally the prevailing "microtrends" (e.g., rising or falling prices). These "fat tails" are not only mathematically important, because they comprise the risks, which may be on the one hand, very small such that one may tend to neglect them, but which - on the other hand - are not negligible at all, i.e. they can never be made exponentially tiny, but instead follow a measurable algebraically decreasing power law, for example with a failure probability of only where x is an increasingly large variable in the tail region of the distribution considered (i.e. a price statistics with much more than 108 data). I.e., the events considered are not simply "outliers" but must really be taken into account and cannot be "insured away".[28]  It appears that it also plays a role that near a change of the tendency (e.g. from falling to rising prices) there are typical "panic reactions" of the selling or buying agents with algebraically increasing bargain rapidities and volumes.[28]  The "fat tails" are also observed in commodity markets.

As in quantum field theory the "fat tails" can be obtained by complicated "nonperturbative" methods, mainly by numerical ones, since they contain the deviations from the usual Gaussian approximations, e.g. the Black–Scholes theory. Fat tails can, however, also be due to other phenomena, such as a random number of terms in the central-limit theorem, or any number of other, non-econophysics models. Due to the difficulty in testing such models, they have received less attention in traditional economic analysis.

gollark: On EFI systems, you do not strictly need GRUB.
gollark: https://knightos.org/, not that this "counts", being a weird OS for certain graphing calculators?
gollark: DOS?
gollark: Microcontroller-type devices will do way better.
gollark: Are you sure it's not using some sort of feature where it stealthily un-hibernates?

See also

References
  1. Yale Economic Review, Retrieved October-25-09 Archived 2008-05-08 at the Wayback Machine
  2. Interview of H. E. Stanley on Econophysics (Published in "IIM Kozhikode Society & Management Review", Sage publication (USA), Vol. 2 Issue 2 (July), pp. 73-78 (2013))
  3. Econophysics Research in India in the last two Decades (1993-2013) (Published in "IIM Kozhikode Society & Management Review", Sage publication (USA), Vol. 2 Issue 2 (July), pp. 135-146 (2013))
  4. "An Introduction to Econophysics", Cambridge University Press, Cambridge (2000)
  5. "Econophysics of Wealth Distributions", Eds. A. Chatterjee et al., New Economic Windows, Springer, Milan (2005), & the subsequent eight Proc. Volumes published in 2006, 2007, 2010, 2011, 2013, 2014, 2015 & 2019 in the New Economic Windows series of Springer
  6. Farjoun and Machover disclaim complete originality: their book is dedicated to the late Robert H. Langston, who they cite for direct inspiration (page 12), and they also note an independent suggestion in a discussion paper by E.T. Jaynes (page 239)
  7. "Econophysics, 2012-2013 ~ e-Prospectus, Leiden University". studiegids.leidenuniv.nl. Retrieved 2018-09-10.
  8. "Econophysics, 2019-2020 ~ e-Prospectus, Leiden University". studiegids.leidenuniv.nl. Retrieved 2019-11-03.
  9. Bikas K Chakrabarti, Anirban Chakraborti, Satya R Chakravarty, Arnab Chatterjee (2012). Econophysics of Income & Wealth Distributions. Cambridge University Press, Cambridge.CS1 maint: multiple names: authors list (link)
  10. Didier Sornette (2003). Why Stock Markets Crash?. Princeton University Press.
  11. Dixon, Huw (2000). "keeping up with the Joneses: competition and the evolution of collusion". Journal of Economic Behavior and Organization. 43 (2): 223–238. doi:10.1016/s0167-2681(00)00117-7.
  12. Johnson, Joseph; Tellis, G.J.; Macinnis, D.J. (2005). "Losers, Winners, and Biased Trades". Journal of Consumer Research. 2 (32): 324–329. doi:10.1086/432241. S2CID 145211986.
  13. Zunino, L., Bariviera, A.F., Guercio, M.B., Martinez, L.B. and Rosso, O.A. (2012). "On the efficiency of sovereign bond markets" (PDF). Physica A: Statistical Mechanics and Its Applications. 391 (18): 4342–4349. Bibcode:2012PhyA..391.4342Z. doi:10.1016/j.physa.2012.04.009. hdl:11336/59368.CS1 maint: multiple names: authors list (link)
  14. Bariviera, A.F., Zunino, L., Guercio, M.B., Martinez, L.B. and Rosso, O.A. (2013). "Efficiency and credit ratings: a permutation-information-theory analysis" (PDF). Journal of Statistical Mechanics: Theory and Experiment. 2013 (8): P08007. arXiv:1509.01839. Bibcode:2013JSMTE..08..007F. doi:10.1088/1742-5468/2013/08/P08007. hdl:11336/2007.CS1 maint: multiple names: authors list (link)
  15. Vasiliki Plerou; Parameswaran Gopikrishnan; Bernd Rosenow; Luis Amaral; Thomas Guhr; H. Eugene Stanley (2002). "Random matrix approach to cross correlations in financial data". Physical Review E. 65 (6): 066126. arXiv:cond-mat/0108023. Bibcode:2002PhRvE..65f6126P. doi:10.1103/PhysRevE.65.066126. PMID 12188802.
  16. Anatoly V. Kondratenko (2015). Probabilistic Economic Theory. Nauka. ISBN 978-5-02-019121-1.
  17. Chen, Jing (2015). The Unity of Science and Economics: A New Foundation of Economic Theory. https://www.springer.com/us/book/9781493934645: Springer.CS1 maint: location (link)
  18. Ricardo Hausmann; Cesar Hidalgo; et al. "The Atlas of Economic Complexity". The Observatory of Economic Complexity (MIT Media Lab). Retrieved 26 April 2012.
  19. Jean-Philippe Bouchaud; Marc Potters (2003). Theory of Financial Risk and Derivative Pricing. Cambridge University Press.
  20. Bouchaud, J-P.; Potters, M. (2001). "Welcome to a non-Black-Scholes world". Quantitative Finance. 1 (5): 482–483. doi:10.1080/713665871.
  21. Philip Ball (2006). "Econophysics: Culture Crash". Nature. 441 (7094): 686–688. Bibcode:2006Natur.441..686B. CiteSeerX 10.1.1.188.8120. doi:10.1038/441686a. PMID 16760949.
  22. Enrico Scalas (2006). "The application of continuous-time random walks in finance and economics". Physica A. 362 (2): 225–239. Bibcode:2006PhyA..362..225S. doi:10.1016/j.physa.2005.11.024.
  23. Y. Shapira; Y. Berman; E. Ben-Jacob (2014). "Modelling the short term herding behaviour of stock markets". New Journal of Physics. 16 (5): 053040. Bibcode:2014NJPh...16e3040S. doi:10.1088/1367-2630/16/5/053040.
  24. Y. Liu; P. Gopikrishnan; P. Cizeau; M. Meyer; C.-K. Peng; H. E. Stanley (1999). "Statistical properties of the volatility of price fluctuations". Physical Review E. 60 (2): 1390–400. arXiv:cond-mat/9903369. Bibcode:1999PhRvE..60.1390L. CiteSeerX 10.1.1.241.9346. doi:10.1103/PhysRevE.60.1390. PMID 11969899.
  25. M. H. R. Stanley; L. A. N. Amaral; S. V. Buldyrev; S. Havlin; H. Leschhorn; P. Maass; M. A. Salinger; H. E. Stanley (1996). "Scaling behaviour in the growth of companies". Nature. 379 (6568): 804. Bibcode:1996Natur.379..804S. doi:10.1038/379804a0.
  26. K. Yamasaki; L. Muchnik; S. Havlin; A. Bunde; H.E. Stanley (2005). "Scaling and memory in volatility return intervals in financial markets". PNAS. 102 (26): 9424–8. Bibcode:2005PNAS..102.9424Y. doi:10.1073/pnas.0502613102. PMC 1166612. PMID 15980152.
  27. The physicists noted the scaling behaviour of "fat tails" through a letter to the scientific journal Nature by Rosario N. Mantegna and H. Eugene Stanley: Scaling behavior in the dynamics of an economic index, Nature Vol. 376, pages 46-49 (1995)
  28. See for example Preis, Mantegna, 2003.

Further reading

Lectures


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