The Markets Hypothesis

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THE MARKETS HYPOTHESIS

by

Fred Viole

OVVO Financial Systems

[email protected]

And

David Nawrocki

Villanova University

Villanova School of Business

800 Lancaster Avenue

Villanova, PA 19085 USA610-519-4323

[email protected]

Preliminary and confidentialDo not quote without permission of the authors.
mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]


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THE MARKETS HYPOTHESIS

Abstract

Our modest research goal has been to define a durable investor utility framework, dismissing thedecades old expected-utility maximizing economic agent assumption. This has enabled us toproperly re-define and re-quantify risk, and part from a reliance upon explanatory ex post analysis forex ante portfolio generation. Finally, we have expanded that analysis into our market structures fromwhich we observe said agents in context of their utility and risk profiles. We propose newclassification and weighting metrics of the participants. This market analysis focuses on the ensuingdistribution of the participants corresponding with the prices for the security.


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The Markets Hypothesis

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I am omniverbivorous by nature and training. Passing by such words as are poisonous, I can swallow most others, and

chew such as I cannot swallow.Oliver Wendell Holmes, The Autocrat of the Breakfast Table, 1858

Placing a conspicuous adjective in front of a market hypothesis seems a prerequisite to

postulating a theory. Perhaps the market in its nonlinear, dynamic, chaotic form cannot be qualified by

an all-encompassing adjective, such as efficient. At times the market appears to be efficient, while

other times inefficiency is evident. At times the market appears to be chaotic, while other times quite

orderly. At times the market appears to be coherent, while other times simply incoherent. At times the

market appears to be rational, while other times it has been called irrational. At times the market

appears to be adaptive, while appearing stagnant in periods between perceived adaptation. If the

market is all of this (and more), the only descriptive word that seems to fit is omniverbivorous.

Holmes could well have been describing the security markets in The Autocrat of the Breakfast

Table in 1858. The zero-sum nature of the transaction process does not swallow per se, it merely

displaces wealth among buyers and sellers (net of fees and transaction costs). It is this union of buyer

and seller, each with their own utility and risk profile that constitutes the markets we observe. Our

hypothesis will focus primarily on the distribution of the participants. Simply put, without people and

capital, there is no market. People have to want to participate in the market because they will be

treated fairly. However, the market only has to be more effective than any competitive system. If not,

people will not participate in the market. Probably the best descriptor for a market theory would be

effective.

We will review historical linear random walk models and other central assumptions in the

Efficient Markets Hypothesis, as well as the evolution into more behavioral models such as the

Adaptive Markets Hypothesis while explaining the consistencies of entropy and bifurcations.


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EFFICIENT MARKETS HYPOTHESIS

Central to the Efficient Markets Hypothesis (EMH) is the belief that prices are unforeseeable,

that the future direction of the market is random based on the actions of always rational, expected

utility theory maximizing agents. Samuelson [1965] supports his proof that properly anticipated prices

fluctuate randomly with idealized stationary and non-jumping assumptions. The geometric random

walk model is most commonly used to quantify this notion for stock market data. Mathematically the

linear random walk model can be written as:

() ( 1) (1)

Then applying it to the logged series yields the geometric random walk model,

log(()) l o g(( 1)) (2)

This is the so-called "random walk" model - it assumes that, from one period to the next, the original

time series merely takes a random "step" away from its last recorded position. (Think of an inebriated

person who steps randomly to the left or right at the same time as he steps forward: the path he traces

will be a random walk.) whereX(t) is the current price of the security and is the average change one

period to the next. These are autoregressive models. Autoregressive models represent a stochastic

process that can be described by a weighted sum of its previous values and a white noise error.

Figure 1 illustrates the generic random walk model with a stationary forecast.


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If the time series being fitted by a random walk model has an average upward (or downward) trend that

is expected to continue in the future, a non-zero constant term is included in the model--i.e., assume

that the random walk undergoes "drift." This has led to the construction of AutoRegressive Moving

Average (ARMA) models. The inherent problem with ARMA models are their inability to predict

inflection points in the drift. In fact, the drift term should be more dynamic and non-stationary then

currently assumed from ex post observation.

()

=

=(3)

X(t) is the current value of a time series, is a constant, is white noise, is the order of theautoregressive component, ,..., are the parameters of the autoregressive model, ,..., are the

parameters of the moving average model, , ,... are white noise error terms.

Figure 1. Stationary

random walk model.

SOURCE:

http://www.duke.edu/~rn

au/411rand.htm#growth


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While visually more instinctive then Figure 1, Figure 2 is exactly the problem with modern finance.

Benoit Mandelbrot coined the term Joseph Effect, alluding to the Old Testament story of seven years

feast followed by seven years of famine for Egypt. However, an ARMA model would suggest years

eight, nine and ten should be of equal or greater feast to year seven. The positive drift term that

accounts for the observed trend is explanatory. The confidence intervals are derived from the standard

deviation of the number of observations (explanatory) and make no predictive reference to the

influence of Brownian motion which serves as an attractor, or long term mean reverting ballast to

volatility's stochastic nature. Viole and Nawrocki [2011d] highlight the inherent shortcomings of

optimized explanatory models in part due to the nature of their construction which challenges their

ability to serve a predictive purpose on a nonstationary process.

Figure 2. Random walk

model with drift.

SOURCE:

http://www.duke.edu/~rn

au/411rand.htm#growth


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We simply attempt to be fearful when others are greedy and to be greedy only when others are fearful.Warren Buffett

This reliance on explanatory models and expected-utility maximizing agents is what impedes

the EMH from exhibiting any shred of common sense during increased conditional volatility, which

Whitelaw [1994] notes is dictated by a very simple variable, the overall business cycle. Lo [2004]

notes, Therefore, according to the behavioralists, quantitative models of efficient markets - all of

which are predicated on rational choice - are likely to be wrong as well. Anomalies such as bubbles

and panics are nonexistent and are simply ignored by the EMH as they contend, the market is simply

pricing in all relevant information at time t. Central to their notion is that agents in the market are

rational, expected-utility maximizers. Viole and Nawrocki [2011a,b,c] reconcile Prospect Theory

within Expected Utility Theory, and isolate the individual's subjective wealth level relative to their

personal consumption satiation point. It is demonstrated that expected-utility is clearly notmaximized

for all wealth classifications. Market participants' dueling emotions, fear and greed, which impede

expected-utility maximization simply cannot be ignored. Basic questions the EMH cannot answer

include:

Why are there observable trends in seemingly random data?How does EMH deal with distributions that are clearly not Gaussian?How does EMH explain the crash of 1987, WTI Crude Oil at $147, Nasdaq at 5000 orABX at 2 cents?

Witness the tenacity with which almost all clung to the theory of efficient markets throughout the 1970s and 1980s,

dismissively calling powerful facts that refute it anomalies. (I always love explanations of that kind: The Flat Earth

Society probably views a ships circling of the globe as an annoying, but inconsequential, anomaly.)Warren Buffett

In defense of these anomalies which are not explained we offer this rationale. An effective market that

allows participants to create anomalies is efficient in the sense that there are no obstructions to

prevent these occurrences. The markets themselves are not inefficient in these instances. The instances


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however, are clearly based on non-EUT compliant economic agents who the EMH assumes will always

act according to a generalized utility theory. That is the inherent assumptive flaw. Is it inefficient to

have a market that allows for uniform conclusions? No, we contend the inefficiency would rest in the

products traded. An equity market without short sale restrictions would also be an improvement upon

efficiency like currently in place with the commodity and foreign exchange markets.

The EMH also falls prey to an unavoidable summation error by projecting a heavily assumptive

normative theory onto a group.

Because human behavior is heuristic, adaptive, and not completely predictable-at leastnot nearly to the same extent as physical phenomena-modeling the joint behavior of manyindividuals is far more challenging than modeling just one individual. Indeed, thebehavior of even a single individual can be baffling at times, as each of us has surelyexperienced on occasion. (Lo, [2004]).

Quantified by white noise terms in its autoregressive models, these summation errors can be quite

dynamic versus the implied complacency of the moniker white noise. AutoRegressive Conditional

Heteroscedasticity (ARCH) models were developed to identify a dependence of error terms over a

given time series, but as we know, dependence is explanatory and nonstationary. If not Gaussian,

white noise does not imply boredom, white noise implies very often a very rough ride. (Mandelbrot,

[2001]).


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ADAPTIVE MARKETS HYPOTHESIS

The Adaptive Markets Hypothesis (AMH), postulated by Andrew Lo in 2004 has several

implications that differentiate it from the EMH. Lo observes the dynamic, non-stationary relation

between risk and reward. Viole and Nawrocki [2011a,b,c] normatively, mathematically, and

empirically define a robust utility theory which accounts for the nonstationary (path dependent) nature

of a participant's utility function. They further illustrate that a participant's risk and reward

characteristics can be accounted for by using different intervals (essentially steeping or flattening their

utility function at time t), creating a sum over histories phenomena when intervals are summed.

Chauvet and Potter [2000, 2001] also suggest a nonlinear risk measure that allows for the risk-return

measure to not be constant over Markov states (bull or bear) or over time. Another implication is

survival is the only objective that matters while profit and utility maximization are secondary relevant

aspects. Viole and Nawrocki [2011a,b,c] also highlight how the certainty equivalence works

influencing some participants to accept exceptionally large discounts to expected value, effectively

dismissing the notion of expected utility theory maximization.

Another implication from the AMH is that contrary to the classical EMH, there are arbitrage

opportunities from time to time. Flash trading enabled an ultra-high frequency strategy with co-located

servers to minimize latencies. There was an arbitrage available to certain participants that the

exchanges had to eliminate through regulatory actions.1 These orders flashed for milliseconds to

certain participants prior to being published on the exchange, enabling programmers to recognize the

intention of a participant prior to the rest of the market and transact accordingly.

1 http://www.nytimes.com/2009/09/18/business/18regulate.html


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Investment strategies will also wax and wane, performing well in certain environments and

performing poorly in other environments. This includes quantitatively-, fundamentally- and

technically-based methods. This realistic perspective on strategies highlights the correlation of survival

with a positively skewed platykurtic distribution. Platykurtic infers less concentrated frequencies of

success (conversely, the ability to be wrong), directly supporting Lo's notion. Viole and Nawrocki

[2011d] have provided a framework to test for such distributions through their upper and lower partial

moment analysis. Alternating trending and mean reverting investor sentiment models are also proposed

in Barberis, Shleifer and Vishny [1998].

Innovation is the key to survival because as risk-reward relation varies through time, the better

way of achieving a consistent level of expected returns is to adapt to changing market conditions. The

only edge in the market is the ability to change. Perez-Quiros and Timmermann [2001] also find

support for a Markov switching model with time-varying means and variances. Andrew Lo at the

IMCA 2010 New York Consultants Conference offers further support for this notion:

Alpha, over time, morphs into betait cannot remain alpha, Lo posits, when hugeamounts of assets pile in and everyone starts to invest to replicate that alpha, and alphaevolves into beta. Hedge funds are a case in point for Localling them, the GalapagosIslands of financeyou can actually see evolution taking place. He says the clear trendover time, is that even though the unique skills of managers who generate alpha are

valuable, returns diminish over time, alpha becomes beta.2

Warren Buffett also notes this in his 2010 letter to shareholders, due to the increase in overall size of

his company, ...But huge sums forge their own anchor and our future advantage, if any, will be a

fraction of our historical edge.

2 http://www.wealthmanagerweb.com/News/2010/2/Pages/Beta-Morphs-From-Alpha.aspx


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The AMH's non-random walk autoregressive model:

where

is the price of the stock at time t.

is an arbitrary drift parameter

is a random disturbance

term. Upon inspection, Lo's model is not that different from the auoresgressive portion of the ARMA

model in equation 3 we presented earlier. The only difference in variables between the two equations is

Lo's model is missing the order summing of the autoregressive parameters.

These models also fail to account for two very important notions of the market, entropy and

bifurcation. They assume that the information is present in prior prices (entropy) and that prices can

reach points quite varied from equilibrium (bifurcation) without specifically accounting for them.


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ENTROPY

Viole and Nawrocki [2011d] prove the importance of conditional entropy analysis, by using it as

a punitive parameter in their quantification of risk. When the explanatory data set exhibited low

entropy, the securitys metric was penalized more than a corresponding security with a high entropy

explanatory data set. This procedure resulted in significant ex ante efficiency in ranking investments.

Below is a random walk proof from an information theory viewpoint. AssumeIt is the current

information set. We know from perfect capital market theory that:

Xt = f (It)

Xt+1 = f (It+1)

It+1 f (It) is a common expression in information theory. (Weiner, [1948]).

Xt+1 f (Xt)

We will explain in the subsequent section how our participant distribution model supports this notion.

Market participants all fit on an entropic ladder. The top of the entropic ladder is the individual with

immediate knowledge of a particular situation. When a participant enters the market, they represent

their entropic position. A grotesque example would be a CEO who initiated merger talks with another

CEO. At this point, the only two individuals in the world with this knowledge are the two CEOs. If

the CEOs enter the market to buy stock in the target company (if the deal is being considered at a

premium), there exists entropic arbitragecommonly referred to as inside information and transacting

upon it is illegal. From that point of initial conversation, the CEOs would alert their counsel to work on

the documents and the information would disseminate. Information dissemination is clearly not

instantaneous, thus enabling the ability of certain participants to earn excess returns.

Grossman [1976] and Grossman and Stiglitz [1980] argue that perfectly informationally

efficient markets are an impossibility, for if markets are perfectly efficient, there is no profit to


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gathering information, in which case there would be little reason to trade and markets would

eventually collapse (Lo, [2004]).

There were no shares traded today on the New York Stock Exchange. Everybody was happy with what they owned.

Saturday Night Live Skit.

Morse [1980] argues further that the speed of information dissemination, while finite, is not

constant, and varies with the amount of new information. With the arrival of new information, the

greater the disparity between the equilibrium price and the actual price, the more investors want to

trade, and increasing trading volume increases the markets speed of information dissemination.

Because of the aforementioned restrictions affecting the speed of information dissemination, greater

dependence in security returns also occurs during this period. Morses results indicate a positive

relationship between trading volume and serial correlations for daily data for a mixture of NYSE,

AMEX and OTC stocks.

BIFURCATION

Bifurcation analysis has led to ex post identification of long term mean regressive and trend

persistent states (Nawrocki and Vaga, [2009]). These trend persistent states that are represented by

large bifurcation parameters are the reflection of the convergence of expectations of the market

participants. In some instances this can be executed by a small number of participants utilizing

increasing leverage to augment their positions and account for a greater percentage of the market. A

distorted example of this phenomenon was in 1970's and the Hunt brothers' silver market manipulation.

At one point the Hunt brothers had 77% of the world silver supply. One market participant accounts

for the bifurcation.


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Another example of a convergence of expectations creating an elevated bifurcation parameter

was the dot com bubble of the late 1990's. Dubbed irrational exuberance by Alan Greenspan, the

market went parabolic based on retail investors' insatiable new found demand for tech stocks. This

bifurcation was caused by many, many participants, yet had the same effect of the single participant

variety.

Government is the largest market participant to offset a bifurcation when an overwhelming

majority of participants derive similar conclusions. Federal Reserve actions during the latest financial

crisis exhibit this stark realization. They took direct participation - Quantitative easing was enacted to

provide an opposing force to the mortgage backed security market in order to dampen interest rate

increases. And they took indirect participation via loss guarantees to certain participant portfolios.

George Soros explains in his theory of reflexivity that a negative feedback process is self-

correcting.3 It can go on forever and if there are no significant changes in external reality, it may

eventually lead to an equilibrium where the participants' views come to correspond the state of affairs.

(Soros, [2009]). This quote refers to the bifurcations and can be applied most notably to the state of

affairs in the asset backed market index provided by Markit. Sub-Prime asset backed indices tracked

by Markit for several years of issuance represent a market with relatively few market participants.4

Some of the tranches have traded at 2 cents on the dollar. Why is this? Fewer participants can come to

a shared conclusion more quickly than a larger diversified market. These participants' views have

clearly come to correspond with the state of affairs for sub-prime home equity loans originated in 2007.

3 http://www.ft.com/cms/s/2/0ca06172-bfe9-11de-aed2-00144feab49a.html4 http://www.markit.com/en/products/data/indices/structured-finance-indices/abx/abx-prices.page


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THE PARTICIPANT DISTRIBUTION

Entropy and bifurcation are two important distinct market characteristics. However, they are

bound by the individual participant, either acting as an individual or sharing expectations with the

masses. Our hypothesis captures these dynamics through the individual participant and helps rectify

why the linear random walk model is insufficient to explain persistent trending and market equilibrium

states far from the market's long term average return.

The above random and non-random walk models are cornerstones of market hypotheses. We

differ in this distinction of a reliance upon an autoregressive model. We believe that focusing on prices

rather than participants is and has proven to be a foolish endeavor. Since prior prices were transacted

upon by prior participants, the source of those prior prices is the most compelling to us. These

participants represent their information sets, (It) and this information is completely ignored when the

process is modeled with past price and error terms. In a security market only 2 information sets can be

processed simultaneously, the buyers and the sellers. While information is additive and consistent

with the second law of thermodynamics, it is simply impossible for all available information to be

priced into the market at time t.

First-order autocorrelation analysis on historical market returns provided by Lo [2004]

buttresses this notion as a completely efficient market would not exhibit any serial correlation.

Nawrocki [1996] illustrates through a daily cross-sectional autocorrelation index, support for Ney's

[1974] contention that specialists were manipulating the equity market during the early 1970s; hardly

efficient.


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However, our hypothesis does take a fundamentally alternative approach. All of the identified

models identified utilize time as an interval qualification. The ARMA model uses not one, but two

summation parameters, thereby increasing its probability of summation error. We postulate that

transactions are a better interval. It is akin to quantum physicists at CERN recreating the big bang and

understanding our universe by observing the smallest particles arising from atomic collisions.

Viole and Nawrocki [2013] derive a nonlinear correlation coefficient illustrating how the macro

correlation coefficient is a linear sum of the weighted micro observations. The same principle

methodology holds here, namely to a) classify the participants; and b) weight the participants and the

ensuing transactions. A quick example may help illustrate the ability of this methodology to observe

the macro from the micro.

The Nasdaq composite index is at 3,000 in March, 2012. It is the same price asNovember, 2000 yet the constituent weightings are completely different. It is a market

Figure 3. First-order

autocorrelation coefficients for

monthly returns of the S&P

Composite Index using 5-year

rolling windows from January

1871 to April 2003.

SOURCE: Lo [2004]


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each owned 50% of the company, and a fund were able to short the entire float (subsequently

counterpartied equally by investor A and B), each would still be 50% of the company.However, i (rather than investor wealth) can be extended from an individual security to the

market as a whole.

(7)

Subject to,

=1 (8)

The sum of all investors market coefficients, for each security with market capitalization ofx,equals one and the sum of all investors of all securities equals one. Transactions alter theparticipant distribution. Time series analysis in essence is the summation of the transactions between

participants and substantial pertinent information is lost in that summation process. The error terms

present in prior models reflect these negative effects. In explaining past prices (which is the goal of the

autoregressive models of other market hypotheses) we are explaining the distribution of participants

(Z). Any transaction will shift ownership from one participant to the other, directly altering the

distribution.

Our hypothesis captures these dynamics through the individual participant and helps rectify

why the linear random walk model is insufficient to explain persistent trending and market equilibrium

states far from the market's long term average return.

If the price of a security rises or falls, the distribution of the participants cannot be uniform or

stationary.6 Selling has to diminish one classification while buying augments the other. Thus, the

6 Viole and Nwrocki [2011d] thoroughly dismiss stationarity by utilizing data sets with surviving components, a


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0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

% Ownership of the Company

% Ownership of the

Company

kurtosis of that distribution cannot equal the kurtosis of the prior participant distribution (if

classification bins are sufficiently specified).

(+) ()

Thus,

(+) ()

The only possible way information can equal prior information for extended periods is if all of the

constituents are identical for the period under analysis. Given that information is equal tof(log 1/p)

and every individual can assign a different probability p to an event, uniform information content is

impossible in an open system such as the market where participants are free to enter and exit.

One participant can account for a majority of the participant distribution, as evidenced with the

Hunt Brothers. Conversely, many participants with similar holdings can replicate this histogram effect.

In both majority participant instances, increasing their holdings will increase the kurtosis of the

participant distribution.

prerequisite for a stationary process. The failure of all MPT and PMPT metrics out of sample rejects the hypothesis of astationary process.

Figure 4.

Hypothetical

distribution of

market

participants.


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The largest participant at the time of an initial public offering is the company itself, with its ability to

offer more shares to the secondary market. Evidence of kurtosis effects of distribution Z rests in

secondary offerings. A decrease in the largest

will decrease the kurtosis ofZ, thus lower the

securitys price. Conversely, a stock buyback will increase the companys and raise the price.

DISCUSSION POINTS WITH MCIMPLICATIONS

Why are there trends in random data?

In retrospect, even the most ardent critics of LTCM and other fixed-income relative-value investors now acknowledge that their spread positions were quite rational, and thattheir demise was largely due to an industry-wide underappreciation of the commonality

of their positions and the degree of leverage being applied across the many hedge funds,investment banks, and proprietary trading groups engaged in these types of spread trades

(Lo , 2004).

Lo goes further by identifying these participants as species:

By species, I mean distinct groups of market participants, each behaving in a common

0%

10%

20%

30%

40%

50%

60%

% Ownership of the Float

% Ownership of the Float

Figure 5.

Hypothetical

distribution of

market

participants

relative to the float.


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manner. For example, pension funds may be considered one species; retail investors,another; marketmakers, a third; and hedge-fund managers, a fourth. and references theirstatus later by saying, In this context, natural selection determines who participates inmarket interactions; those investors who experienced substantial losses in the technologybubble are more likely to have exited the market, leaving a markedly different populationof investors today than four years ago.

These points by Lo illustrates how individual participants can have near identicalMC's. The drift terms

used by former random walk models can be explained by similar market participants or indeed the very

same market participant executing a larger number of transactions, resulting in a net positive kurtosis

change (trend) for an observed period of time, or sum of transactions. Coincidental increased

dependence readings will be a tell to th is convergence of expectations. Typically species will

announce their transactions ex post in order to continue / accentuate theirMCvia entropic manipulation

in order to generate this convergence. Henry Blodget's actions during the dot-com boom were the most

egregious of this manipulative activity.7 Convincing investors to purchase securities through nefarious

research reports contradictory to his personal opinion of the securities (as evidenced in his personal

communications) was entropic manipulation in its worst form, a lie. Blodget's recommendations

generated the counterparties to which the trading arm of his firm could transact with, essentially

passing a hot potato while enriching the manipulators.

How does EMH deal with distributions that are clearly not Gaussian?

Viole and Nawrocki (2011d) provide an argument that supports Lo's (2004) investment strategy

observation. A positively skewed platykurtic distribution is actually the ideal investment and has a

direct correlation to an investment's survival. The fatter right tail of a positively skewed distribution

enables a platykurtic luxury to the manager whereby their frequency can be diminished in order to

7 http://www.sec.gov/news/press/2003-56.htm
http://www.sec.gov/news/press/2003-56.htmhttp://www.sec.gov/news/press/2003-56.htm


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generate the same mean as a leptokurtic distribution. In this instance, survival is correlated to the

postively skewed platykurtic investment due to its realization of the waning of strategies and losses

realized in the adaptation period. Negatively skewed or symmetrical leptokuric distributions do not

have this ability to be wrong at times. The EMH Gaussian assumption cannot explain the empirical

evidence of the correlation of a positively skewed distribution with survivability nor the existence of

such distributions.

If the prices of securities were Gaussian, then there would be no kurtosis effects noticed in the

distribution of participants. Also, there would be 0 serial correlation present. This is just not possible

unless all of the trading in a security occurs within the same classification between participants.

Why do bubbles and crashes exist?

George Soros is an active participant in irrationality.8 He actually attributes this to his vast

wealth.9 How can so much in excess returns be accumulated if the market is efficient? Because it is

not efficient according to any of the forms of the EMH. Soros goes on to explain qualitatively how

positive and negative feedback dynamics are formed from fallibility and reflexivity.

I can state the core idea in two re latively simple propositions. One is that in situationsthat have thinking participants, the participants' view of the world is always partial anddistorted. That is the principle of fallibility. The other is that these distorted views caninfluence the situation to which they relate because false views lead to inappropriateactions. That is the principle of reflexivity. (Soros, [2009])

Viole and Nawrocki [2011a,b,c] identify the seeds for reflexivity in their utility work. An important

milestone in this was a convex utility for market participants' gains and a concavity for losses,

8 See Ellsberg [1961] for a complete argument on why individual actions should not be considered irrational.9 http://www.youtube.com/watch?v=MUEGC4btm64


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essentially risk seeking behavioral patterns for both outcomes instead of the historical relative risk

aversion ideologies. Wyart and Bouchaud [2003] propose that feedback dynamics among a subset of

market agents are sufficient to create trends in anticipation of correlations. This supports not only the

utility work of Viole and Nawrocki [2011a,b,c] and risk seeking for gains of individuals, but the fact

that bifurcations are endogenous.

CONCLUSION

The market may be efficient, however, the supportive assumptions and dogma surrounding the

EMH is clearly inefficient. It is also difficult to argue the adaptive nature of a market which has had

two crashes in an eight year span (dot com and housing). The constant is the economic agent; the

adaptation is in the products traded. Physics envy for this social science will be its ruin. While

quantitatively defining flawed assumptions seems to be the direction this field has headed for the past

few decades, perhaps a bifurcation is at hand. And we have learned that these bifurcations are

endogenous, created from within by the very constituents of the system. It is okay to admit you are

wrong, as the late Russell Ackoff noted that you only learn from doing something wrong, otherwise

you already know how to do it. In doing so, we would hope that the immense cognitive resources

available to this science will generate the only possible action to rectify a bifurcation, the opposite of

what has been done.

In the first paper to empirically link trading networks (a set of traders engaged in transactions

within a period of time) that trace the execution of the limit order book with the dynamics of high

frequency variablestransaction prices, quantities and duration, Adamic et al. (2009) use an audit trail-


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level of detail. They uniquely identify two trading accounts for each transaction: one for the broker

who booked a buy and the opposite for the broker who booked a sale. There were a total of 6.3 million

transactions that took place among 26,950 trading belonging to 346 brokers. The underlying security

analyzed was the E-mini S&P 500 futures contract and the transactions took place during August of

2008.

We find that star-shaped or diamond-shaped patterns characterized by highcentralization or assortativity and low transitivity (clustering coefficient) andconnectedness are positively related to returns and volume and negatively related toduration and volatility. In contrast, less heterogeneous patterns those withcentralization and assortativity close to zero (their averages), high transitivity and highconnectedness are associated with average returns and volatility, and volume andduration above average. (Adamic, Brunetti, Harris and Kirilenko, [2009])

The information dissemination analyzed on a transaction interval (entropic time) provides an excellent

foundation for further research and is fully consistent with our markets hypothesis whereby individual

participant utility functions and risk profiles are not assumed away into obscurity due to fear of chaotic

nonlinearity. These traits are represented within the distribution of the participants, Z. An efficient

market allows for this distribution to change, and an adaptive market allows for this distribution to be

comprised of different participants at any point in time. Just as Samuelson [1965] and Lo [2004], we

leave the reader with more questions than answers, in hopes that future availability of transaction level

data will provide us with the information needed to properly model this hypothesis.


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REFERENCES

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The Markets Hypothesis