ECONOMICS: THE NEXT PHYSICAL SCIENCE?
By
J. Doyne Farmer, Martin Shubik and Eric Smith
June 2005
COWLES FOUNDATION DISCUSSION PAPER NO. 1520
COWLES FOUNDATION FOR RESEARCH IN ECONOMICS YALE UNIVERSITY Box 208281 New Haven, Connecticut 06520-8281
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Analogies will be declared with hindsight, from successes in identi- to physics played an important role in the development fying, measuring, modeling and in some cases predicting of economic theory through the nineteenth century, and empirical regularities. some of the founders of neoclassical economic theory2, such as Irving Fisher, a student of Willard Gibbs, were originally trained as physicists. Ettore Majorana in 1938 II. DATA ANALYSIS AND THE SEARCH FOR presciently outlined both the opportunities and pitfalls EMPIRICAL REGULARITIES in applying statistical physics methods to the social sci- ences. Economists are typically better trained in statistical The range of topics that have been addressed spans analysis than physicists, so this might seem to be an area many different areas of economics. Finance is particu- where physicists have little to contribute. However, dif- larly well represented; sample topics include the empiri- ferences in goals and philosophy are important. Physics cal observation of regularities in market data, the dynam- is driven by the quest for universal laws. In part, because ics of price formation, the understanding of bubbles and of the extreme complexity of phenomena in society, in the panics, methods for pricing derivatives such as options, postmodern world where relativist philosophies of science the construction of optimal portfolios, and many other enjoy disturbingly widespread acceptance, this quest has subjects. Broader topics in economics include the distri- been largely abandoned. Modern work in social science bution of income, theories of how money emerges, and is largely focused on documenting differences. Although implications of symmetry and scaling to the functioning this trend is much less obvious in economics, a typical of markets. paper in financial economics, for example, might study Despite their long history of association, we see the the difference between the NYSE and the NASDAQ stock substantial contributions of physics to economics as still exchanges, or the effect of changing the tick size of prices in an early stage, and find it fanciful to forecast what will (the unit of the smallest possible price change). Physi- ultimately be accomplished. Almost certainly, “physi- cists have (perhaps naively) entered with fresh eyes and cal” aspects of theories of social order will not simply new hypotheses, and have looked at economic data with recapitulate existing theories in physics, though already the goal of finding pervasive regularities, emphasizing there appear to be overlaps. The development of so- what might be common to all markets rather than what cieties and economies is potentially contingent on acci- might make them different. This work has been oppor- dents of history, and at every turn hinges on complex tunistically motivated by the the existence of large data aspects of human behavior. Nonetheless, striking empir- sets such as the complete transaction histories of major ical regularities such as those we survey below suggest exchanges over timespans of years, which in some cases that at least some social order is not historically con- contain hundreds of millions of events. tingent, and perhaps is predictable from first principles. Much of the work by physicists in economics concerns The role of markets as mediators of communication and power laws. A power law is an asymptotic relation of the distributed computation, and the emergence of the so- form f(x) ∼ x−α, where x is a variable and α > 0 is a cial institutions that support them, are quintessentially constant. In many important cases f is a probability dis- economic phenomena. Yet the notions of their computa- tribution. Power laws have received considerable atten- tional or communication capacity, and how these account tion in physics because they indicate scale free behavior for their stability and historical succession, may naturally and they are characteristic of critical or nonequilibrium be parts of the physical world as it includes human social phenomena. In fact, the first power law distribution (in dynamics. In the context of human desires, markets and any field) was observed in economics (see Box A), and other economic institutions bring with them notions of the existence of power laws in economics has been a mat- efficiency or optimality in satisfying those desires. While ter of debate ever since. In 1963 Benoit Mandelbrot [1] intuitively appealing, such notions have proven hard to observed that the distribution of cotton price fluctua- formalize, and the examples below show some progress in tions follows a power law. Further observations of power this area. As with most new areas of physical inquiry, we laws in price changes were subsequently noted by Rosario Mantegna and Eugene Stanley [2], who coined the term “econophysics”. Fig. 1 shows the striking fidelity often
2 found in economic power laws. The existence of power “Neoclassical economics” refers to a representation of individual laws in price changes is interesting from a practical point decision making in terms of scalar “utility” functions, whose gra- dients are imagined to be like forces directing people to trade, and of view because of its implications for the risk of financial from which economic equilibria arise as a kind of “force balance” investments, and from a theoretical point of view because among different people’s trading wishes. From the economist’s it suggests scale independence and possible analogies to point of view neoclassical economics clarified and extended the nonequilibrium behavior in the processes that generate work of the classical economists, Smith, Mill, Ricardo and oth- financial returns. ers by formalizing the notions of competition, marginal utility and rent, as well as producing separate theories of the firm and Since then many other power laws have been discov- consumer.” ered by physicists. These include the variance in the 3
100 tical averages. Physicists have recently discovered that volatility is 10−1 just one of several long-memory-processes in markets. One of the most surprising concerns fluctuations in sup- 10−2 ply and demand [6]. If one assigns +1 to an order to buy and −1 to an order to sell, the resulting series of numbers 10−3 has a positive autocorrelation function that decays as a power law with an exponent γ ≈ 0.6, which persists at 10−4 statistically significant levels across tens of thousands of orders, for periods of time lasting for weeks. This implies 10−5 that changes in supply and demand obey a long-memory process. This has interesting implications. −6 10 One of the most fundamental principles in financial economics is called market efficiency. This principle 10−7 Distribution function P( |return | > x) 100 101 102 takes many forms: A market is informationally efficient x (Units of standard deviation) if prices reflect all available information; it is arbitrage efficient if it is impossible for investors to make “excess profits”, and it is allocationally efficient if prices are set FIG. 1: The distribution of 15 minute price movements for so that they in some sense maximize everyone’s welfare. 1000 stocks from the NYSE and NASDAQ, from ref. [3]. A One of the consequences of informational efficiency is movement in price p is defined as log p(t + τ) − log p(t). The that prices should not be predictable. In reality this is price movements for each stock were normalized by dividing not a bad approximation; even the best trading strategies by the standard deviation for that stock. There is a total of 15 exploit only very weak levels of predictability. million events. The straight line on the right side of the curve indicates a power law, over about two orders of magnitude. The coexistence of the long-memory of supply and de- mand with market efficiency creates an interesting and as yet unresolved puzzle. Long-memory processes are highly predictable using a simple linear algorithm. Since growth rates of companies as a function of their size [4], the entrance of new buyers tends to drive the price up, the distribution function for the number of shares in a and the entrance of new sellers tends to drive it down, transaction, the distribution for the number of trading this naively suggests that price changes should also be orders submitted at a price x away from the best price long-memory, which would violate market efficiency. To offered, the size of the price response to a trade as a prevent this from happening, the agents in the market function of the size C of the company being traded, and must somehow collectively adjust their behavior to off- many other examples. The question of why power laws set this, for example by creating an asymmetric response are so ubiquitous in financial markets has stimulated a of prices, so that when there is an excess of new buyers great deal of theoretical work. the price response to new buy orders is smaller than it One of the most famous and most used models of prices is to new sell orders. How this comes about, and why it is the random walk. The random walk was originally in- comes about, remains a mystery. This may be related to troduced for prices in 1900 by Louis Bachelier, a student the cause of clustered volatility. of Poincar´e, five years before Einstein introduced it to There is a great deal of other empirical work using describe Brownian motion. This forms the basis for the methods and analogies from physics that we do not have Black-Scholes theory of option pricing [5], which won a the space to describe in any detail. For example, ran- Nobel prize in economics in 1997 (see Box B). One of the dom matrix theory [7] (developed in nuclear physics) interesting and surprising properties of the random walk and the use of ultrametric correlations have proved use- of real prices is that its diffusion rate is not constant. The ful for understanding the correlation between the move- size of price changes is strongly positively autocorrelated ment of prices of different companies. An analogy to in time, a phenomenon called clustered volatility3. The the Omori law for seismic activity after major earth- autocorrelation of the size of price changes decays as a quakes has proved to be useful for understanding the power law of the form τ −γ . Since 0 <γ < 1, this is aftermath of large crashes in stock markets, and other a long-memory process. Long-memory processes display analogies from geophysics has led to a controversial hy- anomalous diffusion and very slow convergence of statis- pothesis about why markets crash [8]. The statistics of price movements have been noted to bear a striking re- semblance to those of turbulent fluids, which has led to what may now be the best empirical models available for 3 The autocorrelation function of a time series x(t) is defined as C(τ)= hx˜(t)˜x(t−τ)i, wherex ˜ = (x−hxi)/σx, h i denotes a time predicting clustered volatility. Such examples speak to average and σx the standard deviation. “Volatility” is the term the universality of mathematics in its applications to the used in finance to refer to the variance or size of price shifts. world. 4
III. MODELING THE BEHAVIOR OF AGENTS tions of human behaviors and focuses on the complexity of economic interactions. Yet another approach assumes The most fundamental difference between a physical that some agents (called noise traders) have extremely system and an economy is that economies are inhab- limited reasoning capabilities while others are perfectly ited by people, who have strategic interactions. Because rational. Physicists have joined with many economists in people think, plan, and make decisions based on their seeking new theories of non-fully-rational choice, bring- plans, they are much more complicated to understand ing new perspectives to bear on the problem. than atoms. This is a problem that physics has never An early effort using both agent based modeling and coped with, and it has caused the mathematical tech- artificial intelligence is called the Santa Fe Stock Market. niques and modeling philosophy in economics to diverge This grew out of a conference in 1986 organized by Ken from those in physics. While this is clearly necessary, Arrow, Phil Anderson and David Pines [9] that brought many physicists would argue that the gap is wider than together physicists and economists. Presaging the mod- it should be. ern move toward behavioral economics, the physicists all The central approach to the problem of strategic inter- expressed disbelief in theories of rational choice and sug- actions in neoclassical economics is the theory of rational gested that the economists should take human psychol- choice. The economists’ stylized version of individual ra- ogy and learning more into account. The Santa Fe Stock tionality is to maximize some measure of one’s personal Market was a collaboration between economists, physi- (usually material) welfare, having perfect knowledge of cists and a computer scientist that grew out of this con- the world and of other agents’ goals and abilities, and ference. It replaced the rational agents in an idealized the ability to perform computations of any complexity. market setting with an artificial intelligence model [10]. When agent A considers any strategy, agent B knows It showed that this leads to qualitative modifications of that A is considering that strategy, and A knows that the statistics of prices, such as fat tailed distributions of B knows that A knows, and so on. This infinite regress price change and clustered volatility, and suggested that appears very complicated. However, a key simplifying non-rational behavior plays an important part in gener- result is that in any game there exists at least one Nash ating these phenomena. equilibrium, which is a set of strategies with the property The problem with this approach is that it is compli- that each is the optimal response to all of the others. cated, and while it captures some qualitative features of The Nash equilibrium is a fixed point in the space of markets, the path to more quantitative theories is not strategies, which circumvents the infinite regress prob- clear. Agent based models tend to require ad hoc as- lem by imposing self-consistency as a defining criterion. sumptions that are difficult to validate. The hypothesis Subject to several caveats, rational players who are not of rational choice, in contrast, has the great virtue that it cooperating with each other will choose a Nash strategy. is parsimonious, making strong predictions from simple This is the operational meaning of rational choice. The hypotheses. From this point of view it is more like theo- assumption that decisions of real human beings can be ries in physics. This perspective has inspired the search approximated in this way dominated economic thinking for other simple parsimonious alternatives. One such ap- about individual choices (called microeconomics) from proach is often called zero intelligence. This amounts 1950 until the mid-1980s, though it is clearly implau- to the assumption that agents behave more or less ran- sible for all but the simplest cognitive tasks. It also domly, subject to constraints such as their budget. Zero leaves unaddressed the problem of aggregation of individ- intelligence models can be used to study the properties of ual choices and the behavior of large populations (called market institutions, and to determine which properties macroeconomics). of a market depend on intentionality and which don’t. This provides a benchmark to avoid getting lost in the large space of realistic human behaviors. Once a zero IV. THE QUEST FOR SIMPLE MODELS OF intelligence model has be made, it can be modified by NON-RATIONAL CHOICE incorporating more realistic assumptions, adding a little intelligence based on empirical observations or models of In the last twenty years economics has begun to chal- learning. Where rational choice enters the wilderness of lenge the assumptions of rational choice and perfect mar- bounded rationality from the top, zero intelligence enters kets by modeling imperfections such as asymmetric infor- it from the bottom. mation, incomplete market structure, and bounded ra- The zero intelligence approach can be traced back to tionality. Several new schools of thought have emerged. the work of Herbert Simon, a Nobel laureate in economics The behavioral economists attempt to take human psy- and pioneer in artificial intelligence. Its main champi- chology into account by studying people’s actual choices ons in recent years have been physicists, who have used in idealized economic settings. Another school uses ide- analogies to statistical mechanics to develop new mod- alizations of problem solving and learning ranging from els of markets. A good example is the work of Per Bak, standard statistical methods to artificial intelligence to Maya Paczuski, and Martin Shubik (two physicists and address the problem of bounded rationality. Agent-based an economist), who studied the impact of random trading modeling makes computer simulations based on idealiza- orders on prices within an idealized model of price for- 5 mation. They assumed that traders simply place orders Stock Exchange and have been shown to be in surpris- to buy or sell at random above or below the prices of the ingly good agreement with it [11]. The model also gives most recent transactions. They then modify their orders insight into the shape of supply and demand curves, as from time to time, moving them toward the middle until shown in Fig 2. they generate a transaction. The result is mathematically analogous to a reaction diffusion model for the reaction A + B → 0 that was developed by the physicist John V. THE EL FAROL BAR PROBLEM AND THE Cardy. While this model is highly unrealistic, with a few MINORITY GAME modifications it produces some qualitative features, such as heavy tailed price distributions, that resemble their Another alternative approach is to develop highly sim- counterparts in real markets. plified models of strategic interaction that do a better job of capturing the essence of the collective behavior in a financial market. Brian Arthur’s El Farol bar problem provides an alternative to conventional game theory. The name El Farol comes from a bar in Santa Fe that is often crowded. Each day agents decide whether or not to go hear music; if there is room in the bar they are happy, and if it is too crowded they are disappointed. By defi- nition only a minority of the people can be happy, which leads to a phenomenon analogous to frustration - some desires are necessarily unsatisfiable and as a result an as- tronomically large number of equilibria can emerge. The El Farol model was simplified and abstracted by Challet and Zhang as the minority game [12], in which an odd number N of agents repeatedly choose between two alter- natives, which can be labeled 0 or 1. Their decisions are made independently and simultaneously. Agents whose choice is the minority value are rewarded (awarded “pay- offs”, in game-theoretic terminology). Typically agents are capable of remembering the outcomes of M prior rounds of play, and maintain an inventory of s strate- gies (random lookup tables) dictating a next move for FIG. 2: Nondimensionalized market impact as a function each history. For example, for M = 2 a possible lookup of trading size for eleven stocks from the London Stock Ex- table would be 00 → 1, 01 → 0, 10 → 1, 11 → 1, meaning change. The market impact is defined as the price shift in- in the first case, “If the previous majority choices were duced immediately upon arrival of a trading order causing a 0 in both previous rounds of the game, choose 1 on the transaction. Price is defined as the mean of the best quoted prices to buy and to sell. Nondimensionalization of price and next round”. The strategy chosen is that with the best size is based on a theory that treats arrival and cancellation cumulative performance. of trading orders as a stochastic queuing process, as described Minority games exhibit phase transitions for s ≥ 2, in ref. [11]. The pool is an average of the nondimensionalized in the ratio z = 2M /N, of the number of resolvable data for all eleven stocks. The collapse seen here indicates pasts to the number of agents, as shown in Fig. 3. For that after appropriate scaling all stocks have a common price z
Figure 6: s=2 102 The understanding of relaxation to equilibrium, in- cluding when equilibria are possible and whether they are N=11 25 unique, has grown in economics and in physics together. 101 1001 In both fields mechanical models were used first, followed slope=−1 by statistical explanations [13]. Some recent work [14] has shown which subset of economic decision problems 101 have an identical structure to that of classical thermo- dynamics, including the emergence of a phenomenolog- ical principle equivalent to entropy maximization, while the more general equilibration problems usually consid- ered by economists correspond to physical problems with many equilibria, such as granular, glassy, or hysteretic 100
Stdev^2/N relaxation. The idea that equilibria correspond to sta- tistically most-probable sets of configurations has led to attempts to define price formation in statistical terms. A related observation, that income distribution seems consistent with various forms of entropy maximization,
10−1 recasts the problem of understanding income inequality, and interpreting how much it really tells about the social forces affecting incomes (see box A). We expect that maximum-ignorance principles will grow into a conceptual foundation in economics as they have in physics, and that with this change, the roles of 10−2 10−3 10−2 10−1 100 101 102 103 104 z symmetry, conservation laws, and scaling will become in- creasingly important [15]. Efforts to explain which as- pects of market function or regulatory structure converge FIG. 3: The variance of the number of winners in the minority on predictable forms, relatively free of historical contin- M game is plotted against an order parameter z = 2 /N. For gency, are likely to require characterization in these more z 0 as the distribution of wealth or the size of firms. To 10 US shed light on this we need a better understanding of the −1 Japan natural dimensions of economic life, and the use of sys- 10 tematic dimensional analysis is likely to be very useful −2 10 in revealing this. Dimensional and scaling methods were −3 a cornerstone in the understanding of complex phenom- 10 ena like turbulence in fluids, and all the constituents that −4 make fluid flow complex – long time correlations, nonlin- 10 earity, and chaos – are likely to be even greater factors −5 in the economy. 10 Cumulative probability −6 At the other end of the spectrum, ideas from statistical 10 mechanics could make practical contributions to prob- −7 lems in market microstructure. For market design, for 10 example, some physics-style models suggest that chang- −8 10 3 4 5 6 7 8 ing the rules to create incentives for patient trading or- 10 10 10 10 10 10 ders vs. those that demand immediate transactions could Income in 1999 US dollars lower the volatility of prices. A related practical prob- lem concerns the optimal strategy for market makers, i.e. agents that simultaneously buy and sell, and make FIG. 4: Modern examples of the Pareto distribution of in- a profit by taking the difference. Though markets are comes, from Ref. [16]. Data are collected from federal income tax reporting sources, and are more complete for high incomes increasingly electronic, the design of automated market in Japan than in the U. S. A systematic difference in recent makers is still done in a more or less ad hoc manner. The data is that Japanese low incomes are better fit by lognor- opportunity is ripe to create a theory for market making mal distribution, while U. S. low incomes are better fit by based on methods from statistical mechanics. This could exponential. result in lowering transaction costs and generally making markets more efficient. We are reminded that several key ideas in physics are actually of economic origin. A prejudice that the books should balance was likely responsible for Joule’s As striking as the fact that the large-income distribu- accounting for the energy content of heat before it was tion is scale free, is the fact that the overall distribution well-supported by data. The concept of a “currency”, is so featureless, being described by four (or five) param- which we still think of primarily in economic metaphor, eters: mean income, Pareto exponent, transition point guides our understanding of the role of energy in complex between low- and high-income ranges, and the exponen- systems and particularly in biochemistry. Understand- tial constant (or mean and variance of the lognormal) in ing the dynamics and statistical mechanics of agency the low range. Pareto, lognormal, and exponential dis- promises similarly to expand the conceptual scope of tributions are all limiting distributions of simple random physics. processes, and can also be derived as maximum-entropy distributions for either income or its logarithm, subject to appropriate boundary conditions on the (arithmetic or geometric) mean income [17]. APPENDIX A: INCOME DISTRIBUTIONS (BOX) Income distribution is a hot topic economically and The first identification of a power law distribution – politically, because it lies at the heart of a society’s in any field – was made by Vilfredo Pareto in 1897 for notions of egalitarianism, opportunity, or social insur- the distribution of income among the highest earning few ance. Not surprisingly, causes of income inequality are percent of inhabitants of the UK, and all income distri- asserted, such as distinctions between capital owner- butions asymptotically of this form are known in eco- ship and wage labor, with major policy implications. nomics as “Pareto distributions”. Subsequent studies by Maximum-entropy interpretations of income distribution Pareto for Prussia, Saxony, Paris, and few Italian cities place conceptual as well as quantitative bounds on these confirmed these results, which continue to hold up very arguments. They suggest that the many detailed features well (see Fig. 4). More recent studies [16] have shown of a society that could in principle affect incomes some- that not only is the income of the wealthy regular, but how average so that their individual characteristic scales so is the income of the majority of wage earners, and the are not imprinted on the aggregate distribution; the ulti- two groups follow different distributions. The low- and mate constraints may be conservation laws or boundary medium-income body of the distribution is either expo- conditions reflected in at most a few parameters. Such nential or lognormal (variable among data sets), with a featureless averaging, like the scaling relations we have transition to the Pareto law for large incomes, at a level noted above, may suggest that a form of universality clas- that varies with time, tax laws, and other factors, as yet sification is fundamental to understanding economics, as unknown. it has been to thermodynamics and field theory. 8 APPENDIX B: OPTION PRICING (BOX) the underlying, and to introduce the phenomenological curve of “implied volatility” to bring the formula in line Bachelier’s random walk was a triumph of quantita- with data. The Borland construction avoids such mixing tive finance, and became the basis of modern portfolio of reductionism and phenomenology; its single additional analysis, and later the Black-Scholes model for option parameter describes the observed fluctuations in the un- pricing [5]. The α and β coefficients published in ev- derlying asset price, separating the problem of explaining ery security analysis are mean and covariance coefficients these from that of pricing their derivatives. from fits to a random walk. However, the heavy tails of real price fluctuations, under-predicted by the Gaussian distribution resulting from accumulation of an uncorre- lated random walk, can lead to disastrous mis-estimates 10 NASDAQ stocks, 1 min, 2001 of risk. This has been an important problem in financial 100 mathematics which has received a great deal of attention. More recent work by physicists extends analytic meth- 10-1 ods for pricing options to take the heavy tails and volatil- -2 ity bursts of real prices into account. Inspired by the 10 work of Constantino Tsallis on non-extensive statistical -3 mechanics [18], (see Fig. 5) Lisa Borland [19] has de- 10 veloped a new pricing formula that corrects the stan- 10-4 dard Black-Scholes model. For three decades option- pricing practitioners have recognized that random-walk Probability Density 10-5 estimates were too conservative, and compensated by al- tering the parameters of the Black-Scholes model depend- 10-6 ing on the strike price of the option. The “implied volatil- q = 1.43 ity” assigned in this way makes a well-known “smile” 10-7 when plotted versus the strike prices (see Fig. 6). The -20 -10 0 10 20 Borland option-pricing formula provides a rational ba- Normalized Return sis for pricing of rare events, and nicely reproduces the volatility smile. While there are a large number of other generalizations of the Black-Scholes theory that address FIG. 5: Distributions of log returns for 10 Nasdaq high- the problem of the smile, Borland’s has the significant volume stocks. Returns are calculated as log p(t+τ)−log p(t), advantage that it gives a closed form solution. where p is the transaction price and τ is one minute, and are The self-consistency condition on which all rational op- normalized by the sample standard deviation. Also shown is tion pricing is based – arbitrage-free hedging of risk – the student distribution (solid line) which provides a good fit is a classically reductionist principle relating derivatives to the data (from ref. [19]). to their underlying assets. It is noteworthy that eco- nomic practice has chosen to adhere to the specific Black- Scholes formula based on an empirically invalid model of [1] B. Mandelbrot. 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