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From Wikipedia, the free encyclopedia

Econophysics is a non-orthodox (in economics) 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.

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Transcription

So I got pulled into economics in 2007 because of the 2008 economic crisis. Mike Brown who had been the first CFO of Microsoft, Chief Financial Officer of Microsoft and treasurer of Microsoft, he came to Toronto in 2007 and took my wife and I out to dinner and said he was trying to put together a research group to work on economics and he would like me to be involved. And I said, "I don't know anything about economics." And he said, "That's okay because nobody does and the whole system is about to collapse." He said, "The balance sheets of all the big investment banks -- it's like they have cancer. They're full of holes." And I remember being very struck by this because this was before anybody was talking about this. And so I started to meet with a group of people that he was pulling together to understand what was gonna happen and to understand if there was any way to save the situation. It was a very ambitious thing and, of course, we failed. But along the way I was motivated as a kind of public service to get interested in economics. And what I found . . . economics, in a way, is very easy for a physicist to understand because it's very mathematical. And the mathematical models that they use are very clean. They're based on assumptions and hypotheses, and you can study them. And as I studied it I began to understand, some for myself and more from just reading around because the faults with the standard economic models, with the standard models of finance, are well known. They have been in the literature for decades and decades. So let me give some examples. The standard model of economics is called the neoclassical model and it assumes that markets or systems where trading happens between consumers and firms and there's certain simple models of how that goes on. And the ideas that these come to equilibrium. Equilibrium not in the physical sense but in special economic sense in which you reach a point at which the prices are fixed such that market forces fix the prices such that you maximize the happiness of the consumers and maximize the profits of the firms. And in so called equilibrium nobody can become happier or more profitable without somebody else becoming less happy or less profitable. And the ideology behind this -- not behind the mathematics because mathematics doesn't have an ideology -- but behind the arguments that were made and still are made from this model is that markets don't need regulation because they have these natural equilibria where everybody benefits to the maximum possible. And if you're in equilibrium you can't do better. Now there's a fault with this and it's an obvious fault and it's been known since the 1970s from some theorems proved by some economists including some of the founders of this field of mathematical economics, which is that there's not one equilibrium, there are many equilibria. In fact, there's a vast number of equilibria. And so which equilibria, even assuming that this is a decent model of the economy which is not clear, but even assuming it's a good model, which equilibria you're in depends on the past history, it depends on regulation, it depends on politics, it depends on taste, it depends on changing taste, changing preferences. And so history matters and what's called path dependence matters. This takes us outside the neoclassical model of economics but it doesn't take us outside of economics because some wiser economist, for example, Brian Arthur had for years been developing models and theories of path dependent economy where the history does matter. People from the area of complex systems like Stu Kauffman, Prubac in developing models of markets where the history matters, where there's not a single equilibrium, where there are many equilibria. And where change is paramount. Another symptom of this is the idea that arbitrage isn't, I mean, in these neoclassical models when you go to equilibrium, arbitrage is impossible. Arbitrage is making a profit from trading around a circle of goods or a circle of currencies without actually producing anything. And in equilibrium that's supposed to be impossible but lots of firms and investment banks made fortunes off of currency trading, so why is that? It turns out because you're never really at equilibrium. So why is the notion of equilibrium so powerful? I think part of the answer is this idea of physics envy, that economists thought that what they're doing was more scientific, hence more correct, if it looked like physics. And physics had this timeless picture in which what really mattered, as we were saying before, is the whole history of the system. And in physics there's also a big notion of coming to equilibrium which is, although however, it's important to say, a different notion of equilibrium. And somehow people in economics got seduced into this model which again works in the small -- if you have a small little corner of the economy, a small market -- it may work for a while to characterize approximately what's going on. Arbitrage is not always there. It's not always, I mean, arbitrage, arbitrage does get eaten up. There are market forces which do push you towards equilibria. There's some truth in it. But the whole thing is a disaster if I can say that as an outsider. And it led indirectly -- it wasn't the only reason why regulations were lifted on markets and trading through the decades, but when people were making arguments to Congress, to the President's office that the economy would be better off without regulation, this was the "scientific rationale for it" and led to the very unstable situation of the last economic crisis. And indeed there's still a very dangerous and unstable situation in the world economy because -- well, I'm not an economist. I'm not gonna pontificate about the problems in the economy, but one could see how the idea of timelessness gave false comfort to an unsuccessful scientific theory in the realm of economics.

History

Physicists' interest in the social sciences is not new (see e.g.,[1]); 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.[2] 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.[3][4] 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.[5]

The almost regular meeting series on the topic include: ECONOPHYS-KOLKATA (held in Kolkata & Delhi),[6] 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.[7]

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 Emmanuel Bacry, Giulio Bottazzi, Jean-Philippe Bouchaud, Bikas K Chakrabarti, J. Doyne Farmer, Diego Garlaschelli, Dirk Helbing, János Kertész, Fabrizio Lillo, Rosario N. Mantegna, Matteo Marsili, Joseph L. McCauley, Jean-Francois Muzy, Enrico Scalas, Angelo Secchi, Didier Sornette, H. Eugene Stanley, Victor Yakovenko and Yi-Cheng Zhang. Particularly noteworthy among the formal courses on econophysics is the one offered continuously for more than a decade by Diego Garlaschelli at the Physics Department of the Leiden University.[8][9]

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[10]), percolation models, chaotic models developed to study cardiac arrest, and models with self-organizing criticality as well as other models developed for earthquake prediction.[11] 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 which represents noisy decisions, both of which are based on bounded rationality models used by economists.[12] 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 boundedly rational assumption and modeling on existing neoclassical models. It has been used to prove the "inevitability of collusion" result of Huw Dixon[13] in a case for which the neoclassical version of the model does not predict collusion.[14] Here the demand is increasing, as with Veblen goods, stock buyers with the "hot hand" fallacy preferring to buy more successful stocks and sell those that are less successful,[15] or among short traders during a short squeeze as occurred with the WallStreetBets group's collusion to drive up GameStop stock price in 2021.[16]

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.[17] 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. It was found that more developed countries have stock markets with higher entropy and lower complexity, while those markets from emerging countries have lower entropy and higher complexity. 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.[18] 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.[19]

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),[20] and the path integral formulation of statistical mechanics.[21]

In particular, the ideology of econophysics is embodied in a new probabilistic economic theory and, on its basis, a unified theory of stock markets. [20] [22]


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 [23][24] 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.[21]

Influence

Papers on econophysics have been published primarily in journals devoted to physics and statistical mechanics, rather than in leading economics journals. Some Mainstream economists have generally been unimpressed by this work.[25] Other economists, including Mauro Gallegati, Steve Keen, Paul Ormerod, and Alan Kirman have shown more interest, but also criticized some trends in econophysics. On the other hand, Nobel laureate and founder of experimental economics Vernon L. Smith has used econophysics to model sociability via implementation of ideas in Humanomics.[26]

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.[23][27][28]

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),[29] 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".[30] 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.[30]

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.

See also

References

  1. ^ Kishore Chandra Dash, Story of Econophysics, Cambridge Scholars Press (UK, 2019)
  2. ^ Yale Economic Review, Retrieved October-25-09 Archived 2008-05-08 at the Wayback Machine
  3. ^ 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))
  4. ^ 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))
  5. ^ "An Introduction to Econophysics", Cambridge University Press, Cambridge (2000)
  6. ^ "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
  7. ^ 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)
  8. ^ "Econophysics, 2012-2013 ~ e-Prospectus, Leiden University". studiegids.leidenuniv.nl. Retrieved 2018-09-10.
  9. ^ "Econophysics, 2023-2024 ~ e-Prospectus, Leiden University". studiegids.leidenuniv.nl. Retrieved 2023-12-24.
  10. ^ Bikas K Chakrabarti, Anirban Chakraborti, Satya R Chakravarty, Arnab Chatterjee (2012). Econophysics of Income & Wealth Distributions. Cambridge University Press, Cambridge. Bibcode:2013eiwd.book.....C.{{cite book}}: CS1 maint: multiple names: authors list (link)
  11. ^ Didier Sornette (2003). Why Stock Markets Crash?. Princeton University Press.
  12. ^ Kahneman, Daniel; Sibony, Olivier; Sunstein, Cass R. (2021). Noise: A Flaw in Human Judgment. William Collins. ISBN 978-0008308995.
  13. ^ 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.
  14. ^ Campbell, Michael J. (2016). "Inevitability of Collusion in a Coopetitive Bounded Rational Cournot Model with Increasing Demand". Journal of Mathematical Economics and Finance. 2 (1): 7–20.
  15. ^ 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.
  16. ^ Levine, Matt (18 February 2021). "Congress Wants to Talk About GameStop". Bloomberg.
  17. ^ 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. S2CID 122129979.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  18. ^ 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. S2CID 122829948.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  19. ^ 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. S2CID 2753508.
  20. ^ a b Anatoly V. Kondratenko (2015). Probabilistic Economic Theory. Nauka. ISBN 978-5-02-019121-1.
  21. ^ a b Chen, Jing (2015). The Unity of Science and Economics: A New Foundation of Economic Theory. Springer. doi:10.1007/978-1-4939-3466-9. ISBN 978-1-4939-3464-5.
  22. ^ Anatoly V. Kondratenko (2021). Probabilistic Theory of Stock Exchanges. Nauka. ISBN 978-5-02-041486-0.
  23. ^ a b Jean-Philippe Bouchaud; Marc Potters (2003). Theory of Financial Risk and Derivative Pricing. Cambridge University Press. ISBN 9780521819169.
  24. ^ Bouchaud, J-P.; Potters, M. (2001). "Welcome to a non-Black-Scholes world". Quantitative Finance. 1 (5): 482–483. doi:10.1080/713665871. S2CID 154368053.
  25. ^ 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. S2CID 4319192.
  26. ^ Michael J. Campbell and Vernon L. Smith (2021). "An elementary humanomics approach to boundedly rational quadratic models". Physica A. 562: 125309. Bibcode:2021PhyA..56225309C. doi:10.1016/j.physa.2020.125309.
  27. ^ 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.
  28. ^ 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.
  29. ^ 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)
  30. ^ a b See for example Preis, Mantegna, 2003.

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External links

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