edgar peters talks

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Stocks & Commodities V. 20:12 (79-86): Interview: Edgar Peters Talks About Chaos Theory by Jayanthi Gopalakrishnan

Edgar Peters TalksAbout Chaos TheoryEdgar Peters Talks

About Chaos Theory

Butterflies In China, Market Corrections In New York

Edgar Peters TalksAbout Chaos Theory

INTERVIEW

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ow did you get interested inchaos theory?

As a quantitative manager,I had all the traditional

schooling. As a practitioner, you findthat the only way to make quantitativeanalysis work is to make a lot of adjust-ments, because there are a lot of prob-lems with it. And also because I tookclasses with Harry Markowitz when Iwas in business school at Rutgers Uni-versity, I knew a lot of the underlyingassumptions that go into traditionalquantitative management. After a whileyou find that those are fairly significantassumptions, and they can have signifi-cant problems. So in early 1987, when Iread an article that chaos theorist JamesGleick wrote for The New York TimesMagazine, I got interested in the topic.I began to do research on my own.

What happened after that?Over the next few years, I read a

bunch of physics and mathematics pa-pers. I applied some of that knowledgeto do empirical research on the marketsdirectly. In 1988, I wrote one paper forthe Financial Analysts Journal in whichI looked at fractal analysis, which Id

His work Chaos And Order In The Capital Markets ignited the imagi-nation of traders and investors everywhere, and the way we viewed themarkets changed. As PanAgoras chief investment officer and co-chairof its investment committee, Edgar Peters is responsible for ensuringthe integrity of the firms investment strategies and product array. Hisinsights into market dynamics and behavioral finance have not onlyadded value to client portfolios but also stimulated the development ofinnovative investment technologies. An expert in tactical asset alloca-tion strategies, he also manages PanAgoras asset allocation portfoliomanagers.

STOCKS & COMMODITIES Editor Jayanthi Gopalakrishnan spoke toEdgar Peters on October 3, 2002.

come across in a book byJens Feder. Rescaled rangeanalysis, which is what Federwrote about, has the abilityto determine whether a pro-cess had long memory. Whathappens now influences aprocess far into the future; itdoesnt just go away.

But thats not conducive totraditional theories like therandom walk.

When they talk about mar-kets being random walks orhaving short memories, itmeans that what happens to-day doesnt really affect to-morrow that much, and itdefinitely doesnt affectthings that happen a few days from now.Thats critical to traditional quantita-tive techniques, because if you donthave that, a lot of the analysis doesntwork, or at least it becomes unreliable.

What did you do?I conducted a test on market returns,

and found that the market did have along memory. This prompted me to

You dont typically have a bigcapitulation selloff at the bottomsof bear markets ... they just sortof peter out.

H

write a paper, which was published ayear later, as well as a book, Chaos AndOrder In The Capital Markets. I thoughtit was important for people to be awareof the implications of the long memoryof the market, rather than having themslog through all the physics papers. ThenI got a request for a more technicalexplanation, because the first book wassupposed to be a conceptual introduc-



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tion. So I wrote the second book, FractalMarket Analysis. In that one, I describedthe fractal market hypothesis to explaindays of extreme volatility.

How do you use fractal analysis todetermine volatility in the markets?

Chaos theory and fractals allow youto put a framework around how themarkets work, which I think is alwaysgood to have. You use it as a guide inorder to avoid mistakes like, for ex-ample, the mistakes of Long-Term Capi-tal Management. Youre assuming acertain type of statistical process. [Long-Term Capital Management was a hedgefund begun in 1993 by John Meriwetherand a collection of financial geniuses,including two Nobel laureates. The fundenjoyed huge success until 1998, but itsmodels failed when the Russian rublecollapsed at the same time other finan-cial pressures dramatically changed thehistorical fluctuations on which LTCMhad risked $1.21 trillion. To preventwidespread economic damage, LTCMwas bailed out by Wall Street creditorbanks in a deal arranged by the FederalReserve Bank of New York, and sur-vived long enough to pay its debts, butwas liquidated early in 2000. Ed.] Alot of people think of fractals in terms oftechnical analysis, like Elliott wavetheory, but fractals applied to the mar-ket are not like that at all.

How would you define them, then?Instead, fractals applied to the mar-

kets are mostly statistical functions. Withthose, what happens is that you havestatistical processes with a larger prob-ability of larger events happening thanthe normal distribution suggests. Allthe traditional quantitative managementand what most people think of the mar-kets are based on the normal distribu-tion, or the bell-shaped curve. The ac-tual market distribution is different fromthat. It has a high peak at the mean, andfat tails.

What does that mean?The fat tails mean two things: one is

that average events are more likely tohappen than people think, and the otheris that large events are also more likely

o you have an example inmind?

Again, the people atLong-Term Capital Man-

agement. Say that the events that hap-pened to them were like a 100-yearflood. But if you look at the actualdistribution of returns, the frequencyof those events happening is a lothigher than once in 100 years. Theyactually happen every few years. Ifthey were based on the normal distri-bution, the probability would be onceevery 100 years but its not nor-mally distributed.

Any other examples?Another thing is portfolio insurance,

which was a popular strategy in the 1980s,up until the crash of 1987. It was based onthe idea that if things are normally dis-tributed, theyre also continuous. Thatwould mean that if the price of a stockwas falling from 50 to 40, it would passthrough all the prices in between 50 and40. But in reality, what happens during acrash like the one in 1987 is that themarket actually leaps over prices. It mightgo right from 50 to 40, without goingthrough 49, 48, or any other prices inbetween. Portfolio insurance was basedon the idea that you can rebalance as themarket is going down, but youd havetrigger points. But if the trigger point wasone that you were supposed to sell astock at 45, and the stock didnt hit 45because it went immediately to 40, thenyoud be left in the dark.

One of the characteristics of thesefat-tail distributions is not only that largeevents are more likely to happen thanyoud expect, but also when large eventshappen, they tend to be abrupt and dis-continuous.

When you say large events, do youmean crashes and rallies?

to happen than people think. This is trueacross time scales, so it doesnt matter ifyou look at daily returns, monthly re-turns, or annual returns. They all havethis characteristic. So what happens isthat certain things that are based onnormal distributions do not work.

Yeah, but it doesnt have to be a crashlike the one in 1987. It could just be a bigdownturn. If you get several downturns,the market just doesnt go through theprices you want. It leaps over them inboth directions. It can open or gap up,and then drop immediately. Theres nocontinuous nature to the way the pricesmove. Price continuity was critical toportfolio insurance, because that was inline with normal distribution.

So what happened during the 1987crash?

The trades couldnt be executed fastenough on the way down and traderswerent able to hedge their holdings,which was the way it was supposed towork. To overcome this, what we do ismake sure that when youre setting upyour strategy, you dont fool yourselfinto thinking that youre just preparingfor something with a low probability ofhappening. The probability is actuallymuch higher. When an event that willnegatively affect your holdings doescome through, it can wipe you out.

Youre quantitative in your approach.You use a lot of quantitative models inyour analysis. But you also believe thatsome of the conventional models, suchas the capital asset pricing model(CAPM) and the efficient market hy-pothesis, are not reflective of actualmarket conditions, correct?

Thats right. And as a matter of fact,the people who created the traditionalquantitative models, the originalfounders, know that. Bill Sharpe used tocall them toy worlds. When creatingthese models you make a lot of simpli-fying assumptions to get to the truth.Theres a lot of truth in CAPM and mod-ern portfolio theory, and a lot of usefulthings that we use, too. You just have tobe careful and not think that the worldalways acts like that. It doesnt. It worksmost of the time, but there are timeswhen it doesnt, and not acknowledgingthat can wipe you out. Theres alwaysan element of judgment at certain pointsin execution. If something were to hap-pen that we thought was outside of themodels experience, we would take thatinto account, but it rarely happens.



details are different but the net effectsare usually the same. So during certainsum factors, we react to whatever isgoing on in the economy, no matterwhat the story is. Those are the types ofthings we look for.

Can you give an example?A basic one we look for is the slope of

the yield curve. The yield curve broad-ens and gets steeper. Interest rates go upand down, but they do so for differentreasons. Ive been in the investmentbusiness since 1978, so Ive watched itgo up and down. But whenever theressomething that makes the economy ex-pand or contract, the yield curve steep-ens and drops. It doesnt matter what itis. This kind of information is the sort ofthing I mean by a global structure. Itsnot dependent on the details of whatsgoing on. The mistake a lot of peoplemake, either when theyre creating theirjudgmental or quantitative models, isthat they model the details, and forgetthat those things will never be repeated.

So if you were to create a stock mar-ket process, or a forecasting process,either judgmentally or quantitatively,and depended on the New Economy,you would have failed miserably. But alot of people believed that was going tobe the future. Back in the late 1970s,everybody looked at money supply,because Paul Volcker, who was the Fed-eral Reserve chairman then, was target-ing money supply for Fed policy. Youcould see the market reacting heavily.So everybody sat around waiting formoney supply, and the market reactedheavily to increases and decreases inmoney supply. Now, of course, nobodypays attention to money supply at all.

You also focus on complexity theory.How do you apply that to the markets?

Chaos theory is a subset of complex-ity theory. Complexity theory says thata process will hover on the edge ofchaos and fully ordered areas. Theyreusually fractals, and theyre usuallynetworks, where things go across. Sothe market is generally not actually cha-otic, but complex. It becomes chaoticwhen fluctuations become really huge.Typically, though, its complex. What

What happens when it doesnt work?Whats the critical factor?

Well, the big events happen moreoften than you think, and things tend tobe abrupt, not continuous. One of theproblems with the Long-Term CapitalManagement situation, again, was thattheir basic assumptions were correct,and things reverted the way they ex-pected them to. But they didnt do itsoon enough, and LTCM was so lever-aged they ran out of money. You cantlet that happen to you. You cant as-sume that just because something has asmall probability in a normal distribu-tion that its not going to happen. Andwhen it does, youre not prepared for it.So you just have to be sure to control forthat kind of risk.

When you see things happening out-side the realm of your model, do theytake place during extreme conditions?

No. They usually revolve aroundpolitical events. One example is theGerman reunification, something wethought the model couldnt handle, sowe had to figure out what to do aboutthat. Things like that do happen. Wheneconomic activity changes, the belief isthat the structure will change too. Butstructural change to the economy real structural change is extremelyrare. The actual mechanics of theeconomy change all the time; thats thenature of it, but people think that newtechnology structurally changes theeconomy. Thats not true, because werealways getting new technology.

Do global and local events affect themarkets differently?

If youre familiar with my work, Iusually make a distinction between cha-otic systems and fractals. There are glo-bal characteristics, and there are localdetails. I look at it as global structureand local randomness. What that meansis that in the business cycle, there arecertain things that are common acrossbusiness cycles.

Then there is the story behind thebusiness cycle, which is always differ-ent. Every time we go through the busi-ness cycle, it seems like things are dif-ferent, because they actually are. The

that means is that you have a processthat is self-adapting, and it learns overtime or adjusts over time. Because ofthat, you never completely know whatsgoing on inside of it.

One of the important characteristicsof complex systems is that theyre veryadaptive and resilient, and they evolve.This is because they have, again, certainglobal laws, but theres a lot of localrandomness. Those random elementsallow the process to search for solu-tions. For instance, during the late 1970sand early 1980s, there was a lot of talkabout how the US economy was a has-been. It was behind the times, and Japanwas going to move ahead, and so forth.

And then Japan imploded.But before that, there was an outcry

that we had to do something, whichusually means people look for some sortof organized search for a solution. Whathappened was that the people in theeconomy were actually searching for asolution, and when you have a lot ofpeople working on a solution indepen-dently, they tend to crossbreed withtheir ideas, because information getsaround. Eventually, the solution popsup without any sort of organized search.Usually it pops up in a number of differ-ent places.

So what happened?During the transition of our economy,

unlike Japans where they keep try-ing to organize a recovery the pro-cess jettisoned some industries, a num-ber of companies, and reestablished newtechnology, and revamped itself, all onits own. It didnt do so with any signifi-cant organized effort; it just organizeditself spontaneously. Thats what keepsa free market working. One of the dan-gers is that in times of crisis theresalways a hue and cry that someones gotto do something about it.

Which is happening right now.Yes, its usually what happens. But it

usually turns out that whatever is orga-nized inhibits what needs to be done.You need regulations to make sureabuses dont happen, but you dont wantto impose a structure on the economy,



which will inhibit its creativity. Japan,for instance, follows a quasi-socialiststructure. Everything has to pass throughcertain bureaus and ministers. The prob-lem is that there are only a few of those,and thats why socialism failed, and sodid communism.

If you have a free market, you havethousands of people working on prob-lems, independently and interdepen-dently. But if you have a more struc-tured, governmental system, you onlyhave five or six people who are sup-posed to come up with the solution. Aswe know, when people work in groups,the IQ of the group tends to be lowerthan the IQ of the individuals, whichleads to never finding an answer, and asystem that never really adapts. Thereason communism collapsed was be-cause it wasnt able to adapt to changesin the environment. It turned out to beextremely inefficient. People feared thatthe efficiency of communism wouldovertake us, because they were orga-nized and we werent. And we didntwant to be organized like that, becauseyou had to give up freedom in order todo so, which we didnt want to do. Itturns out that freedom, which is largelya function of individual uncertainty, isactually more resilient.

Living in a free society means wehave more individual responsibility.That also means we face more uncer-tainty on an individual level. Ironically,on a societal level, we actually face lessuncertainty than the structured environ-ment. But individually, we face higheruncertainty, because nobody tells uswhat it is were supposed to be doing. Ina structured economy like the old So-viet Russian economy, you were prettymuch told what it was you were sup-posed to do. You didnt have to makeany individual decisions, so there wasvery little uncertainty for you. Therewas very little opportunity, too, but youdidnt have to worry about going outand finding a job, because someonewould do that for you.

So you believe uncertainty is neces-sary for an economy to function effi-ciently?

Thats right. And at times like these,theres actually great opportunity inthe middle of the uncertainty, becausethats what uncertainty generates not only risk, but opportunity as well.So its a matter of making sure thatwhen you impose regulations, whichtheres a lot of talk about now, you setboundaries for what people can do, butyou dont tell people what theyre sup-posed to do.

H ow much of the uncertaintyand randomness of themarkets has to do with hu-man behavior?

A lot has to do with human behaviorand how people interpret information.Thats the biggest flaw in the rationalefor traditional capitalization pricingtheory. They make a lot of simplifyingassumptions, one of which is that thevalue of information is understood, andeverybody agrees on what it means whenit comes into the market. In reality,most information that comes into themarket is not very precise, as we foundout with the accounting practices. Noteverybody can agree on what it means.

So even though you may have a con-sensus, there will always be a group thatdisagrees. Because of that, things willnot necessarily go to fair value. Becauseof the different reactions to it, you get alot of movement in the marketplaceitself, and a lot of opportunity gets gen-erated from that.

In terms of fair value and the valua-tion of the markets, where do you thinkthe markets are right now? Are theystill overvalued?

No, as a matter of fact we think themarket is severely undervalued right now.I can say this because during the marketbubble I was a notorious bear. And just aspeople were overly optimistic during thebubble period, theyre now overly pessi-mistic. What we dont know is when itsactually going to come true. But based onthe relationship between stocks and inter-est rates, and a couple of other things,stocks are pretty severely undervaluedunless a couple of very unlikely scenariosunfold, which is always possible.

Such as?One of the biggest unlikely scenarios

is deflation. In order to justify the cur-rent level of prices, its not so much thatearnings have to fall, its that you haveto say were going to be facing a defla-tion. Not necessarily a massive defla-tion, but just a deflation sometime in thefuture. There are some worries aboutthat, but the chances of actually havinga deflation significant enough to makethe market overvalued currently arepretty slim. Wed have to have worsedeflation than Japan has.

Were quite a long way from that.I think so too. So the chances of that

happening are pretty unlikely. As far aswere concerned, the market is close tothe bottom if it hasnt already hit, and thedownside is pretty limited at this point.Its mostly upside, but when the upsidewill come through, though, is unknown.

You base a lot of your analysis on thebelief that the market has a memory.Do you see a repeat of a situation fromthe past in the current markets?

Its not that kind of memory, but whatwe are seeing now is very similar to theway things have ended for other specu-lative bubbles. Its less severe for theeconomy than it has been in the past,because weve learned a lot about whatto do. But speculative bubbles them-selves have a number of defining char-acteristics. When we were in the middleof the bubble, I used to point thesecharacteristics out to people. I wouldeven read from newspapers from the1920s, and there would be things therethat were just like what was going on inthe present. You could go back evenfurther to the 1870s, when there wasanother speculative bubble, and youdsee a similar scenario. Its very com-mon, but you dont get them often, andthats the trouble. If youre not a studentof the history of the markets, you are notgoing to see any of this.

I remember a lot of comments aboutthe similarities, but of course, not manypeople paid any attention.

Each story behind each speculativebubble is different. In the 1920s, the



productivity gains were not due to com-puters or the Internet, of course. Theywere due to electricity, something wenow take for granted as a commodity. Inthe 1920s, the high-flying technologycompanies were the electric utilities,which we now think of as commodities,sleepy little companies. What Id pointout was that this would also be true oftechnology and particularly the Internet it will become a commodity, just likeall the others, because thats what endsup happening. If its something reallyimportant, then competition will even-tually make it into a commodity. An-other thing that is similar is that theresalways an element of truth to what causesthe speculative bubble. This usually endsup not coming through, and thats whatcauses so much distress for people.

Anything else?Whenever the speculative bubble

bursts, theres always a bunch of peoplewho go to jail or commit suicide orsomething like that. It always turns outthat a lot of people who were thought tobe geniuses just turn out to be crooks.When everybodys making money, itshuman nature to overlook those flaws.You dont care how the other personsgetting his money, as long as you getyours. But if you lose yours, you want tomake sure that those who kept it, andkept it illegally, get whats coming tothem. Thats what always ends up hap-pening. I thought for a while it wasntgoing to happen this time, until theEnron scandal happened.

Then what follows is usually a periodwhere everybody gets disgusted, andwhat has typically happened is that a lotof people just leave the market. Marketparticipation goes back down to moremodest levels. Even during the crash of1987, the number of people who partici-pated in the market was much lowerthan this time or the 1920s. This hap-pens because during a market bubble,people overreact and decide that every-

thing is just gambling. The truth is theywere just not being prudent.

I think we are seeing people pull moneyout of the market.

Yes, we are. People are pulling moneyout of the market and putting it in realestate. You dont see CNBC playing inbars anymore. These are the types ofthings that are similar during all thebubbles. As far as the market itself goes,you dont typically even thoughpeople have been waiting for this have a big capitulation selloff at thebottoms of bear markets.

What happens?Bear markets always end with whim-

pers; they just sort of peter out. Every-body loses interest, and we are seeingthat now. Im hearing a lot of peoplesay, Well, we havent seen the bigcapitulation selloff. The big capitula-tion selloff is a symptom of bull marketreversals, not bear market reversals,where everybody just gets tired andquits. Were also seeing that becausethis is just a grinding, day-after-daydecline. Its been going on for a numberof years now, another typical factor ofbear markets, which is another reasonwhy I think we are close to the end.

Are you optimistic about the marketsgenerally?

Yeah, overall Im optimistic for thelong run. During the next couple ofyears, its going to be similar to whenwe came out of the last recession. Whenwe go through mild recessions, we alsohave modest recoveries, so it takes awhile for things to gear up again. Plusnow, we have a lot of excess capacity,due to those productivity gains that ev-erybody talked about. In the 19th cen-tury when there was also a big surge inproductivity gains, there was a longperiod almost 20 years of defla-tion and low profits, because there wasmore capacity produced than there was

actually a need to consume. I dontthink well face 20 years of that, but wemay face a few years of it.

What are we going to be seeing?What well end up with is that stocks

will outperform bonds an allocatorthats important to me but not by alot. Absolute returns in both cases willnot be that great. Current yields areabout 3.5%, so if stocks return 4% or6%, then Ive done my job. But its stillgoing to be below the actuarial assump-tions of institutions, or the kind of re-turns that people want to see for theirretirement nest eggs. We are going toend up with a couple of years wherethings are going to be pretty mediocreon the return side.

Thank you for your time, Edgar.

SUGGESTED READINGFeder, Jens [1998]. Fractals (Physics

Of Solids And Liquids), Plenum Press.Gleick, James [1987]. Chaos: Making A

New Science, Penguin.Peters, Edgar E. [1991]. Chaos And Or-

der In The Capital Markets: A NewView Of Cycles, Prices, And MarketVolatility, John Wiley & Sons.

_____ [1994]. Fractal Market Analysis:Applying Chaos Theory To Invest-ment & Economics, John Wiley &Sons.

_____ [1999]. Patterns In The Dark:Understanding Risk And FinancialCrisis With Complexity Theory, JohnWiley & Sons.

_____ [2001]. Complexity, Risk, AndFinancial Markets, John Wiley &Sons.

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