Essays on the Efficient Market Hypothesis

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ECO 320v – Computable Economics

Dr. Mark McBride

Joshua Carpenter



Essays on the Efficient Market Hypothesis and Computational Economics

Throughout the course of economic history there have been many thoughts and theories

developed that have been accepted as truth right after their introduction. But over the course of

time and with a more in depth testing, those same thoughts and ideas were challenged by a more

informed body, demolishing the very foundation in which they stood. The efficient market

hypothesis is no different and may serve as the next example of a widely accepted precept in

finance that has been tested and found to be set upon an unstable foundation.

These essays attempt to examine two stances on the efficient market hypothesis and the

cost and benefits of using agent-based computational economics as a tool for financial market

efficiency. The first essay analyzes an article by Burton G. Malkiel. This work is a general

defense against those who feel there are flaws in the efficient market hypothesis. He highlights

different works that have been done by the EMH’s adversaries and addresses empirical concerns

about their findings. In the second essay we look at an article by J. Doyne Farmer and Andrew

W. Lo. This article explores recent advances in the quantitative modeling of financial markets.

The authors also expand on the evolutionary framework of those markets and the interaction

between its components as well as address the efficient market hypothesis from a behavioral

point of view. The third essay addresses two articles; one by Mark Buchanan and the other by

Farmer and Duncan Foley. Both articles address the need of agent-based computer models in

real world settings, the different ways they could be used, and the approaches that are being used

today. Finally, the fifth essay highlights an article by Edward P.K. Tsang and Serafin Martinez-

Jaramillo, and attempts to define the scope and agenda of research in computational finance.



An Essay on Efficient Markets

Burton G. Malkiel explored specific questions which addresses concerns with identifying

patterns in the stock market. The idea was that if there were patterns in the stock market how

would they be found. When a specific pattern was found, he then raised the question: is that

pattern able to be exploited, if so how exploitable is it? In the article, he elucidates his view of

the efficient market hypothesis. That is, all subsequent price changes represent random shifts

from previous prices and if information is unimpeded and immediately reflected in the stock

prices, then tomorrow’s price change will reflect only tomorrow’s news and it will be

independent of the price changes today. And because prices fully reflect all known information

the uninformed investors will achieve a rate of return that is just as generous as that obtained by

expert investors.

In supporting his claim Malkiel brings up several questions about the research performed

by those who have tested the efficient market hypothesis. The majority of his article is directed

toward behavioral economists and psychologists who believe there are predictable correlations

within the stock market that can be exploited to obtain returns in excess – or in other words the

market is inefficient. The first question he addresses is whether short-run momentum patterns in

the stock market are able to be identified or measured by using either a quantitative (technical)

approach or a fundamental approach. The quantitative approach uses empirical data to postulate

a predicted price of a stock or stock index. The fundamental approach evaluates the health of a

company by looking at its balance sheet and/or other pertinent fundamental indicators of a

company’s overall health.



Malkiel states that the economists and psychologists in the behavioral finance field

believe that short-run momentum is consistent with psychological feedback mechanisms. For

example, as stock prices rise, investors are drawn into the market in a kind of “bandwagon”

effect seeing that there are potential gains in buying stocks. Another possible explanation of

short-run momentum patterns is that investors tend to underreact when new information is

presented. The author believes that several factors should prevent us from concluding that

markets are inefficient by using the above examples as a reference.

First, the author suggests that the economic significance of the behavioralists model

needs to be addressed. He notes that the transactions costs of investing limits momentum and

impedes any likely trading strategy that will beat a buy-and-hold strategy. He provides support

for this claim by referring to studies which show that momentum investors do not realize returns.

Additionally, he explained that by using a sample taken from momentum investors, the data

suggested that they did far worse than buy-and-hold investors, even during a period of positive

momentum. Moreover, he said it was the large transactions costs that were involved in finding

existing momentum that caused their deficiency.

The second point the author makes is that there is little evidence showing that the

“bandwagon” effect occurs in a systematic way that allows it to be exploited. Malkiel supports

his case by citing a study performed by Eugene Fama in 1998 that sought to determine whether

stock prices responded to information efficiently. The study found that the underreaction to

information was about as common as the overreaction and that post-event returns that were



abnormal were as frequent as the reversals. Therefore, the data from that study only supports his

claim that markets run efficiently.

One important question is whether there are predictable patterns that are consistent over

time. The issue with finding exploitable patterns is whether they are patterns that can be applied

over time, or if it is time specific. For example, a pattern exploited in 1990 could produce excess

returns, but in 2000 it produced negative returns. As the author states, many predictable patterns

seem to disappear after they are published in the finance literature and these patterns weren’t

capable of guaranteeing consistent excess returns. Another example used to show this was the

thought that there were patterns or anomalies in certain days of the week or times of the season.

The general problem the author states is that there isn’t any consistency, at least not enough to

rely on from a period to period basis. Further, the effects are miniscule compared to transactions

costs accrued by trying to exploit them.

In the field of finance it is claimed that valuation ratios may provide considerable insight

in predicting future stock returns. Malkiel addresses the idea of predicting future returns from

initial dividend yields, market returns from initial price-earnings multiples, short-term interest

rates, and risk premiums. In short, his first example shows that investors earned a higher rate of

return from the stock market when they purchased a market basket of equities with an initial

dividend yield that was relatively high and relatively low future rates of return when stocks were

purchased at low dividend yields. Malkiel concludes that the dividend yields of stocks tend to be

high when interest rates are high, as well as the converse. He explains further that the use of

dividend yields to predict future returns hasn’t been effective since the mid-1980s. Also,



companies today may be taking a new approach using creative methods like accelerate share

repurchasing programs. This in itself would render the dividend yield’s utility for predicting

future returns.

Concerning the prediction of market returns from initial price-earnings ratio, studies have

found that investors have tended to earn larger long-horizon returns when purchasing a market

basket of stocks at relatively low price-earnings multiples. But Malkiel dispels this by giving a

current example that shows the inability of the mentioned study to apply that same logic at a later

date. As far as interest rates and risk premiums are concerned, he admits that it is far from clear

that any of the results given can be used to generate profitable strategies.

When valuing a stock there is a belief that an important key to prediction could lay in

fundamental aspects of a firm. That is, the characteristics and different valuation parameters of a

firm. One of those ways is the tendency of portfolios made up of smaller stocks to achieve a

higher monthly average than those of larger stocks. The research performed on this subject has

discrepancies. If a researcher would examine the long-run performance of small company

stocks, she would be measuring the performance of only the companies that have survived in that

long-run time frame, thus, only blurring the predictability of long-run returns.

The author concludes that although the data are statistically significant, they still show no

evidence that they can be used to predict stock prices in different time periods other than those in

which the data were drawn from. He goes on and says that even if there were patterns that

existed which allowed investors to earn excess returns, they could follow the past so-called

patterns and self-destruct in the future. Other issues he brings up are those in data mining. He



suggests that with the ease of experimenting with almost any type financial data, investigators

are bound to find some statistically significant correlation whether or not it is meant to be

‘teased’ out or not. Finally, he explains that as times passes and empirical techniques become

more sophisticated there will be documentation of further apparent departure from efficiency.

An Essay on the Quantitative Modeling of Financial Markets

There have been several recent advances in the quantitative modeling of financial

markets. Farmer and Lo explain how financial agents can compete and adapt, but not necessarily

optimally. When looking at other conventional models used today, it is this aspect that sets

agent-based computation economics apart from current economic theory. In a perfect world

there would be a simple answer for the question of market efficiency. Moreover, there would

also be a simple answer for finding ways to predict future earnings. The need for instruments

that predict future economic conditions are well overdue. Unfortunately, finding an answer for

these economic concerns are difficult and very complex. Modern economic theory, although

complex, only serves as a simplified version of a super-complex system which harbors what

seems like an infinite amount of variables. This simplified approach views models only from a

post-event perspective and does not account for those variables that cannot, or are hard to

measure.

The efficient market hypothesis states that price changes are unforcastable if they

incorporate the expectations and information of all market participants. As Farmer and Lo state,

the EMH is counter-intuitive and contradictory. That is, the most efficient market is one in



which the price changes are completely random and unpredictable so that no one can receive

excess returns because the market already reflects all available information and all profits have

been captured.

Several studies have shown that prices are not completely random, which would violate

the EMH’s principle rule – prices are unpredictable and random. Consequently, this leads to a

disagreement within the economics academic community. This, though, raises the question

about whether some anomalies are just isolated incidents or can the market be beaten.

Specifically, what about some of some of the high-profile portfolio managers or successful

investment firms? These types of discrepancies lead us to the question of whether or not the

EMH is able to withstand the scrutiny.

The issue noted in this article refers to the testing of the EMH. In order to test the EMH

you would have to deal several auxiliary hypotheses as well as additional structure in the model

– investors’ preferences, the structure information. Additionally, an issue with the EMH arises

with assuming that investors make fully rational decisions. Especially since the decisions made

are by agents that are perfectly informed, not mentioning the fact that they are assumed to make

the correct decision. This article suggests that, yes agents can make good decisions, but not

perfect decisions. They cite an example which compares an agent’s efficiency with the concept

of efficiency used in physics. That is, the fraction of available energy converted into useful

work. Thus, someone would prefer a refrigerator with 40% efficiency over one that has 35%

efficiency, but no one would expect 100% efficiency. This topic as it pertains to financial

markets is still in the beginning stages of research.



Another approach this article takes is by applying the efficient market hypothesis to other

aspects where profits are possible to those who have the competitive advantage. When the EMH

is applied to aspects other than the financial market, there is a vast deficiency found. For

example, through research and development a firm can develop a vaccine for the AIDS virus. If

this happens and the profits earned are in the billions of dollars, do we assume that this is just an

appropriate reward for the R&D, or could that be classified as excess returns? The point is that

financial markets should not be different in principle, but only in degree.

The disagreements that surround the EMH have led to the development of new ideas for

research. One of the most interesting and, according to the authors, one of the most promising

ideas stems from a biological perspective. This idea breaks away from the conventional

modeling where it is assumed that the households making decisions are rational. This model

accounts for the irrationality of agents within a complex system, modeling markets, societies, or

entire economies. Another aspect that these models capture is the fact that agents evolve and

learn. This is something that the simple model of modern economics has yet to account for. Nor

does the modern model account for the manner in which psychology influences the decisions of

a household. These agent-based models are meant to capture those complex behavioral

tendencies and dynamics that make market realistic relative to the actual setting. Financial

markets are view as a living organism or an evolving ecology of trading strategies. What is so

interesting is that the financial market’s actions are analogous to what an individual agent does

within the market. That is, even though there are patterns in individual behavior, the integration

of each individual’s actions in concert creates patterns in the whole.



Although different in the format in which each issue is approached, these models that

attempt to explain some characteristic of financial markets are directed at interpreting the EMH

in their own words. Since there is no lack of quantitative data, the opportunities are endless with

ACE and it’s almost certain that there will be considerable advances which link biological and

evolutionary ideas to the financial market.

An Essay on the Need for Agent-Based Computable Economic Models

In light of the past financial meltdown there seems to be a consensus that breaks

partisanships and socioeconomic barriers. The idea of having another catastrophe in the

financial market is enough to send chills down any Wall Street investor’s spine. Conventional

methods have yet to prove that it can withstand the expected level of predictability needed to

provide confidence not experiencing this again. In his article “Meltdown Modelling” Buchanan

uncovers an approach not as well publicized in the financial community as those methods

currently used on a daily basis. As Buchanan explains, the current methods used today are only

methods that can draw from past experiences. There are several issues with the current models

used to monitor and predict future happenings. These models have a hard time accounting for

nonlinearities and patterns that can only come from agents interacting with each other.

Another issue faced by academic economists is the idea of bringing outside help in from

other realms of study to give a fresh perspective. The idea is to get a fresh perspective on the

issues which could provide a way to purge ideological tendencies and failed assumptions.

Generally, this is meant to help the situation. But, some economists argue whether it is a good



idea to invite someone in that doesn’t have the experience within economics and finance to spur

scientific progress. Additionally, there is also the general distrust in agent-based computer

models. In general, this is due to a lack of understanding of what these models do. As a real-life

example, Buchanan mentions an experience with the NASDAQ chief Mike Brown. At the time,

NASDAQ faced the situation of switching from fractional stock prices to numbers that provided

the price in decimals. The main goal was to improve the accuracy of stock prices and this

change also allowed prices to change by smaller increments. The consequences of how this

would affect trading strategies were unknown. Using an agent-based model the idea was tested.

Because of this test the NASDAQ team was able to address inefficiencies in the magnitude of the

price increments, thus using the agent based model as a market wind tunnel.

The two models the United States government, the Foley and Farmer article says, has

flaws. The first model deals with the inaccuracy of econometric models, which is only able to

accurately forecast a couple quarters ahead as long as economic conditions stay more or less the

same. The other model rules out any type of crises, the model is founded upon the assumption

that it is a perfect world. Agent-based models account for “petri dish” type growth predictions,

not relying on the assumption that the world is perfect and everyone makes perfectly informed,

rational decisions. These two benefits in themselves provide a useful tool for modeling several

different portions of the economy. A few mentioned in this particular article are models of

financial markets which provide a plausible explanation for bubbles and crashes, simulation of

firm dynamics which models how firms grow and decline as workers move between them, and

credit sector models.



The idea that an entire economy could be modeled may be a little far off, but with

continual achievements in the modeling of the tendencies of different sub-sets of an economy, it

is possible that in the aggregate each model can be tweaked and linked to create some sort of

organism which closely resembles our very own economy.

An Essay on the Scope and Agenda of Agent-Based Computational Finance Research

Twenty years ago computer systems were substantially slower and less able to compute

complex data compared to today’s machines. The advancements with computer systems today

are improving at an amazing rate. With improvement in computer technologies, there comes a

positive externality which provides us with the ability to compute complex data, sometimes,

from the convenience of our own home. The same stands for the growth in the use of

computation programs to improve firms’ disposition as a profitable company. As the first

spreadsheet ramped up capabilities of analyzing several complex datasets that in the past took up

a great amount of time, there are also other advancements which in the near future could help

control costs or generate revenue as much as that first spreadsheet did.

Modern economic theory and fundamentals have generally been accepted due to their

ability to accurately simplify a complex condition in order to understand it better. But as time

goes on, so does the complexity of the systems in which the principles of economics and finance

were founded upon. This raises the question to whether or not these simple models, maintaining

simple assumptions can continually be used as the systems in which they model increase

substantially in complexity. Agent-based models have an agenda to challenge these thoughts and



assumptions by providing a way to account for the irrationality of agents in a complex system.

Therefore, challenging the very foundation of the efficient market hypothesis. Moreover, it also

provides a way to set certain agent-specific assumptions to model system-specific results.

Another part of an agent-based model’s agenda is to understand financial markets. This

is seen in the vast amount of models that represent different markets within an economy, such as

the labor market we spoke previously about or even an orchestrated artificial stock market such

as the Santé Fe Stock Market. There is also a certain amount of importance weighted on agents

that evolve. This is found in the construction and attempts to model an accurate depiction of

competitive agents. ACE models are also used to study the microstructure of a market which is

used to developed different trading mechanisms in a variety of different types of markets. One

of the goals that these models seek to obtain is in predicting the future. Even though there is a

strong support in academia for the relevance of the efficient market hypothesis, this doesn’t

imply that the people within these institutions are discouraged from attempting to do it.

Conclusion

It is possible to say that both agent-based and modern economic models have pro’s and

con’s. But, it isn’t enough to leave it there. The simple fact is that both are seeking to find many

of the same things, but in different ways. ACE models challenge the history of economic

thought. And that in itself is something that professors of agent-based models have to face. But,

concerning the efficient markets hypothesis, there needs to be a fresh perspective or another

approach that attacks this from its blind side. The only way that there can be an improvement in

modern economic understanding is if there is a sense of openmindedness within the economic



community, the will to try anything that is better. It’s easiest to hold on to an understanding that

has long been a part of one’s life, but the true challenge is in opening your mind and entertaining

other ideas that may actually lead you that much closer to the truth you seek.

References

Foley, D., & Farmer, J.D. (2009). The Economy needs agent-based modelling. Nature, 460, 685-686.

Buchanan, M. (2009). Meltdown modelling. Nature, 460, 680-682.

Tsang, E.P.K., & Martinez-Jaramillo, S. (2004, August). Computational finance. IEEE

Computational Intelligence Society, 8-13.

Farmer, J.D., & Lo, A.W. (Ed.). (1999). Frontiers of finance: evolution and efficient markets.

Malkiel, B.G. (2003). The Efficient market hypothesis and its critics. The Journal of EconomicPerspectives, 17(1), 59-82.

van den Bergh, W.M., Boer, K., de Bruin, A., Kaymak, U., & Spronk, J. (2002). On intelligent-agent based analysis of financial markets. Working Paper, Erasmus University, Rotterdam.

Tesfatsion, L. (2006, July 29). Introductory notes on financial markets. Retrieved from

http://www.econ.iastate.edu/classes/econ308/tesfatsion/finintro.htm Tesfatsion, L. (2006, July 29). Information, bubbles, and the efficient markets hypothesis.

Retrieved from http://www.econ.iastate.edu/classes/econ308/tesfatsion/emarketh.htm

http://www.econ.iastate.edu/classes/econ308/tesfatsion/finintro.htm



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Essays on the Efficient Market Hypothesis