Reflexivity, Coherent Markets, and Financial Instability:

Reconsidering Alternative Explanations for Departures from

Generally Accepted Economic and Financial Theory

J. Douglas Barrett

Professor of Quantitative Methods and Chair

Department of Economics and Finance

University of North Alabama

Florence, AL 35632

[e-mail]

Peter M. Williams

Professor of Economics

Department of Economics and Finance

University of North Alabama

Florence, AL 35632

[e-mail]

Reflexivity, Coherent Markets, and Financial Instability:

Reconsidering Alternative Explanations for Departures from

Generally Accepted Economic and Financial Theory

ABSTRACT

The current financial crisis has caused a reassessment of many canonical assumptions

underpinning traditional theory in economics and finance. Specifically, the real estate and
financial markets have exhibited behavior that belies previously expected conditions.
Nonstandard theories have existed for decades, but have been largely ignored by mainstream
academia. The Reflexivity Theory of Soros, the Coherent Markets Hypothesis of Vaga, and the
Financial Instability Hypothesis of Minsky are three potentially viable theories. The current
work is an investigation of these and other alternative theories in economic and financial
analysis.

Reflexivity, Coherent Markets, and Financial Instability:

Reconsidering Alternative Explanations for Departures from

Generally Accepted Economic and Financial Theory

INTRODUCTION

Traditionally, the dominant school of thought in finance is the Efficient Market

Hypothesis (EMH). (See, e.g., [3].) In its simplest form, the EMH asserts that market prices
reflect all available information. Theoretically based in mathematics, the EMH is the foundation
for much of the inquiry in the discipline. Empirical studies have shown results that are, at best,
mixed. The recent economic crisis has exacerbated the situation.

The EMH is based on several assumptions. It asserts that past information does not affect
market activity (i.e., the process is “memoryless”), once this information is generally known.
Another assumption is that capital market behavior follows a “random walk.” Furthermore, with
a sufficiently large sample, the returns become well approximated by a normal (Gaussian)
distribution.

The purpose of the current study is to discuss issues with the EMH, and highlight the current
alternative theories. In the next section, empirical departures from the aforementioned
assumptions for the EMH are discussed. The succeeding section highlights the list of alternative
theories, with a brief description of each. The paper concludes with a summary and points of
convergence for the competing theories.

ISSUES WITH THE EFFICIENT MARKET HYPOTHESIS

Studies by Mandelbrot ([9] and [10]), Sharpe [19], and Turner and Weigel [23] found

departures from the EMH assumptions present in the data over certain time periods. Skewness
and leptokurtosis were present, as well as a general lack of Gaussian fit for returns. A lack of
stability of the variances, as well as excessively large sample variances were in violation of the
Central Limit Theorem. Mandelbrot [9] found that the random walk assumption failed to hold
for data on cotton prices, but did find that such markets tend to exhibit elements of nonlinear
dynamics (chaos) rather than random statistical behavior.

Another inherent assumption of current capital market theory is that investors are “rational”.
The field of behavioral/experimental economics is rife with studies that refute the assumption of
“rationalityy,” at least as defined in classical economics. (See, e.g., [24], [5], or [1].) This
literature suggests that the EMH does not always hold. There is little evidence that investors are
more "rational" in the aggregate? Rather, the opposite is often true. (See, e.g., [13] and [14].)

These studies also suggest that some investors are actually risk-seeking rather than risk-averse.
Bias exists, as investors tend to trust their own judgment even when facts fail to comport with
their judgment. Group-think may exacerbate the bias, as many will “follow the group” even if
doing so runs counter to previous personal sentiments. Other market-behavior anomalies, such
as the small firm effect, the January effect, and the P/E effect, offer further evidence against the
EMH.

ALTERNATIVES

The evidence suggests that the EMH is found wanting. However, there is not a single

alternative to fill the void. There are at least seven! These are briefly summarized below. The
interested reader is referred to the citations for further study.

Adaptive Markets Hypothesis

The Adaptive Markets Hypothesis (AMH) was proposed by Lo [8] as a qualitative alternative to
the EMH. Based on evolutionary psychology, sociology, and biology, and behavioral research,
the AMH focuses on the decision-making of investors. Rather than being “rational optimizers”,
decision-makers often employ heuristics that are environmentally contextual. While behavioral
biases exist, they cannot be dismissed as “irrational”.

Prices reflect information as dictated by the environment and “ecology”. The information itself
contains biases. Simon’s [21] seminal work on satisficing is explained through trial and error
decision-making, and natural selection. Changes in the environment yield new prices. In some
cases, the new prices will greatly deviate from the old ones. Fear and greed change over time (as
do other departures from “rational” thinking). Thus, the assumptions of random walks,
rationality, and a lack of bias are unwarranted.

Coherent Markets Hypothesis

Vaga [25] proposed the Coherent Markets Hypothesis (CMH) as an alternative to the EMH that
views market progressions as a complex dynamical process. Based on Social Imitation Theory
[2], the CMH is in a sense a generalization of the EMH. The CMH asserts that market variables
follow a stable (Paretian) distribution, with two parameters determined by the group sentiment of
investors (bias) and by the fundamental environment. (See [7].) Combinations of bias and
environment allow for four possible market phases: random walk (efficient market), transition,
chaotic, and coherent.

The random walk (efficient market) phase occurs when all investors are acting independently of
each other and prices quickly reflect all available information, allowing the market to quickly
discount the information. The random walk phase meets the assumptions of the EMH, and the
normal distribution is a special case of the stable distribution under these circumstances.

The (unstable) transition phase occurs when groupthink (bias) rises and causes the impact of
information to last for long periods. In this case, reaction times will be slow. During the

transition phase, the process may revert back to the random walk phase or segue to another
phase. The chaotic phase is characterized by high bias and an uncertain fundamental
environment. Analogous to the transition phase, there is a possibility of moving into any of the
other phases. If the level of bias diminishes, the market will typically return to the random walk
phase. However, if the level of bias rises, then the fundamental environment will determine the
transition to the next phase.

The coherent markets phase is characterized by a “strong” fundamental environment and heavy
investor bias, and "strength" may be positive or negative. A strong positive fundamental
environment will cause a strong positive trend with low risk, and a weak fundamental
environment will result in a strong negative trend with high risk. One possible explanation for
the unusual growth of stocks during bubbles is that we had entered a coherent market phase.

Fractal Markets Hypothesis

The Fractal Markets Hypothesis (FMH), due to Mandelbrot ([9] and [10]) and Peters [16] is
similar to the CMH in that it posits a stable (Paretian) distribution. This again suggests that the
EMH is applicable under special circumstances. Mandelbrot ([9] and [10]) studied a wealth of
financial market data, and found that is tended to be chaotic over certain periods.

The FMH is based on four basic points. First, the market is stable and is sufficiently liquid when
it is comprised of investors with different time horizons. These investors tend to stay in their
“preferred” habitat (time horizon) regardless of what is dictated by market information.
Available information may or may not be reflected in the market prices. Finally, the trend in
market prices indicates the changes in expected earnings (which mirror long-term economic
trends).

Financial Instability Hypothesis

Minsky’s ([11] and [12]) Financial Instability Hypothesis (FIH) offers an alternative not focused
primarily on securities markets, but on consumer willingness to assume debt. As with the AMH,
CMH, and FMH, there are different phases (“cycles”) that may last variable durations. Each
phase is characterized based on ability to repay debt. The choice to assume the level of debt is
rooted in the biases of the creditors and debtors.

Minsky characterizes cycles in terms of debt-repayment ability, and cites three phases. The first
is “hedge”, in which borrowers are able to repay principal and interest. The next is
“speculative”, in which they are able to repay interest only. Finally, “Ponzi,” is a phase in which
repayment may occur only through asset appreciation. The hedge phase reflects conservatism on
both the part of the borrower and the lender. As optimism (bias) grows, people tend to become
more risky (speculative). At the zenith of optimism, credit is extended based on the expectation
of continued price inflation. The result is the bursting bubble.

Behavioral Finance

Based on the pioneering efforts of behavioral psychologists Tversky and Kahneman [24],
Behavioral Economics is the application of experimental psychological techniques in economics.
(See, e.g., [20]). When Behavioral Economics is applied specifically in the financial setting, it is
referred to as Behavioral Finance (BF). The general focus of BF is on market decision
dynamics, and it generally employs design of experiments as the primary statistical method of
inquiry.

Behavioral models challenge traditional economic views on “rationality”, “utility”, and “risk-
aversion”, topics that overlap economics and psychology. In BF, investor behavior is analyzed
in an experimental setting. This allows for cause-and-effect studies, as opposed to the traditional
economic use of regression and correlation that do not allow for establishing causal relationships.
Many results in BF contradict the assumptions of the EMH.

Reflexivity

Reflexivity is a condition of social systems in which the systems inquiry is not independent of
the participation of the observer. (See, e.g., [4]). Soros [22] extends the work of Popper [17]
into finance and economics to derive his own “Theory of Reflexivity” of markets. Soros’ work
is nonmathematical, and is based in foundations of systems in which reflexivity is applicable.

Soros delineates the differences between the expected outcomes of his Reflexivity Theory (RT)
and the EMH. A major RT feature is the gap between perception and reality (or “bias”). The
investigators in Finance are also market actors, and are therefore not independent of the milieu.
The mere act of investing influences the performance of the market, which in turn affects the
inquiry. Similarly, media and other organizations can create biases that influence investor
tendencies. The biases may grow over time, and the EMH becomes less and less applicable.

Socionomics

Prechter [18] postulated that there is a causal relationship between social mood and social
behavior. Socionomics is the ongoing study of this relationship. Contrary to the traditional
view that financial, macroeconomic, and political behavior drive social mood, Socionomics
challenges the arrow of causality.

Endogenous processes (and not exogenous causes, or “shocks”) steer decision-making behavior
in financial processes. Such endogenous existents as “social mood” are analogous (if not
equivalent) to bias. The “herding impulse” is basically “social imitation”. Uncertainty is at the
boundary of between unconscious, non-rational and conscious, rational behaviors. Parker and
Prechter [15] argue that this is also the threshold between financial and economic behavior.

SUMMARY

Each alternative to the EMH offers thoughtful reflection, if with varying levels of

investigational rigor. Some (CMH, FMH, and FIH) are quantitative, some (AMH, RT, and

Socionomics) qualitative, while BF is mixed. Most are based on behavior of financial markets,
but the FIH is founded in credit.

There are four points of convergence across the theories. Each either explicitly or indirectly
alludes to bias. Information does not reflect underlying reality. The psychology of actors is
context-dependent in each of the alternatives, based on the temporal ecology. For the CMH and
FMH, the stable (Paretian) distribution is applicable. This allows for certain times during which
the EMH holds (which is consistent with some studies). Finally, there is a departure from the
linearity assumption of the EMH.

Further study in this area is necessary. Since there are many alternative explanations, one is led
to the parable of the blind men and the elephant. Is each simply a restatement of the others? Are
there multiple correct theories? Do we need a completely new theory? The journey should be
illuminating and fascinating.

REFERENCES

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