Behavioral finance definitions

Plan of the whole chapter

See also


Main concepts: BF vs. EMH
[see also
abstract (slides)]

Behavioral-Finance Gallery

500+ keywords BF glossary
and 1700+ members
informative BF forum

Part A

Individual and social behavioral biases

Part B (below)

Economic and financial incidences

pi-separ.gif (1205 octets)

Part B : Economic and financial incidences

  5. Market behaviors: market anomalies in
     volumes, prices, returns, volatilities

The introduction page of this section warned about all sorts of permanent
and temporary holes in the fabric of the EMH
(efficient market hypothesis)
applied to capital markets:

Here is a list of the major market anomalies:

Overtrading (bringing excessive volume and volatility) are inefficiency

If market where efficient, prices would adjust instantly, leaving no arbitrage

Price trends, momentums, entailing bifurcations / alternations / cycles
(although these are partly physical reactions, their main cause is changes in

With sometimes critical thresholds where the price behavior can change
completely, a phenomenon called percolation.

Possible trend disruptions (acceleration, reversal),
    or even a sudden illiquidity (lack of buyers facing a seller rush, or the
    other way round

 Price reactions to information are crucial for market behavior.

The influence of past / recent memory - neglected by the EMH - and of the
other cognitive and emotional biases shown above, affects those reactions.

Thus, we get misreactions (over- / under-reactions), which are among
the causes of:

Trends / momentums / fads,

Mispricing (over-pricing, under-pricing),


The statistical distributions of returns, prices, volatility, can differ from  

the standard Gauss-Laplace's "bell curve"(narrow dispersion of values,
situated symmetrically
close to an average) by showing sometimes
deviations from that 
"random law":

Asymmetries (skew): faster or longer or larger rises than drops, or drops
  than rises.   

Fat tails: prices or price variations spreading farther from the mean than
  is considered
proper and well-behaved  in the good old standard bell

Pareto's "power law": concentration on extreme values, not on a central
one, an alternation of periods of very high / very low prices, returns or

Congestions / clusters,

Heteroskedasticity (changes of volatility),

  Feedback loops (positive feedback, reflexivity)

A positive feedback (vicious circle) happens when the results of a previous
action, accelerate the drift (addition) instead of leading to a counter-action
that stabilizes the system (negative feedback, subtraction) and makes prices
oscillate softly.

In extreme positive feedback cases, when the change in belief is long
overdue, it results in bubbles / crashes.

Sometimes also the capital market evolutions can influence the
fundamentals (reflexivity)

Even professional analysts get more optimistic / pessimistic after
  prices go up / down. 

They adapt their earnings consensus accordingly.

That revised consensus makes prices go up / down again.

Something that make them raise / lower again their earnings
expectations, and so on.

In some cases, it could be that private information (or superior
research ?) feeds the momentum.

But often the real reason seems that an analyst alone don't find it
wise to issue a buy or sell recommendation that goes against the
current trend, and thus to take the risk that its results will differ
(in the bad direction) from those of the "crowd" of the other analysts.

6. Behavioral assets pricing / risk measurement

Investors profiling. Investors (as well as each stock and other asset), can
be classified in types / categories.

We can also say in clans, if we take into account the peer influence.

Each type has its own type of behavior or investment / trading style.

Stocks profiling, starts also by identifying types / categories of stocks
behavior, and to see for each stock how closely it may fit one (or several)
    of them.

Stock profiling brings the notion of stock image coefficient, my
operational concept described in this site.

That coefficient is obtained by dividing the stock price by its "EEV -
Estimated Economic Value". (note that EEVs are a bit delicate to
calculate objectively, as based on future earnings' projections).

This site shows how to estimate stock price potentials by
multiplying the EEV by the potential images.

Technical analysis (TA) supposes that markets have memory.

If so, past prices, or the current price momentum, can give an idea of
the future price evolution.

TA is a tool to detect if a trend (and thus the investor's behavior) will
persist or break.

TA gives some results but can be deceptive as it relies mostly on graphic
that are often intertwined, unclear or belated.

It might become a source of representativeness heuristic (spotting
patterns where there are none).

Quantitative analysis (QA) is based on statistical analysis via probabilistic

It works in ordinary circumstances but can completely fail when it has to
deal with uncharted territories (rare events, totally new situations).

Nonlinear pricing / decision models, a field with more and more active

It take into accounts that sudden price changes can break trends.

The trick is to simulate the competing behavior or various category of
investors (agent-based models), or the whole market behavior.

It uses such tools as dynamical systems models (such systems behave
in a more complex, and at the same time more determinist way, than the
random walk), and soft computing based on chaos theory, fractals,
fuzzy logic, neural nets, genetic algorithms
... (see reader's contributions

on those topics)

Value at risk, extreme risks calculations.

The real risk is not the standard price or return deviation used by the
EMH, but the extreme risk that can cause total ruin, or that is deemed
not acceptable.

The above concepts of fat tails and heteroskedasticity, leading to the
so called ARCH and GARCH models, are useful to measure it.

7. More general or complementary topics

Types of probabilities: statistical, subjective, Bayesian, imprecise (fuzzy
logic), etc.

The (expected) utility concept (leading to the risk premium)

The game theory (i.e. the prisoner dilemma) supposes rational players

Some might find similarities between the stock market and a game with
money at stake, either a positive game (more gains than losses), or a
negative one (more losses).

But this is not completely relevant as the game theory considers usually
the actions of only a few players

Anyway, there are two main categories of game:

Zero-sum games, which are most of the time competitive (grab the
share of a limited resource),

Positive sum games, which are sometimes cooperative (make a
  bigger cake by cooperating).

 Experimental economy / finance, you know, the rats in the labyrinth thing,
with in vitro or in vivo observation of players' decisions / actions, whether
    alone or within groups.

This goes farther than the game theory as it shows the real behavior and its
monetary consequences.

Behavioral finance draws largely from those experiments (together with other
techniques, such as the analysis of market statistics). Some examples are:

The "paradoxes" seen in the probabilities, utility and decisions page,

The prospect theory (see above): people think it worse to have lost a
money than not to have gained a bigger amount of money.

Experiments in mimicry, in collective utility and so on...

Neuroeconomics, a division of neurosciences, adds a new dimension to those

By scanning the brain it detects which of its areas / chemical agents / electric
waves correspond to specific emotions (pleasure, suffering) or on the
contrary to cognitive functions (logic, knowledge...) and are at play, often
unconsciously, in money-related decision making.

Marketing techniques applied to the creation and distribution of financial
(= assets / liabilities) products: customers segmentation, types, motivations,
(see above).

  8. The limitations of BF

BF/BE are still not considered fully scientific and practical fields.
They have some weaknesses, such as their
overemphasis on:

* Anomalies compared to standard finance.

BF/BE concepts refer quasi-exclusively to mental deviations.
Therapeutic purpose or witch hunting?

Those references limit the main criteria of "normality" in finance (and
economics) to expected monetary returns and risks.

This contributes to consider as a "market anomaly" any divergence
 from those two criteria.

This seems too obvious to fit realities fully.

- What about uncertainty as a broader concept than statistical risk?

- More important, what about non monetary returns, that might also
   have their
degree of legitimacy?

Such "soft" returns are empathy, fun, power, human challenge, search
for experience and knowledge and myriads of other goals.

* Reactions to events / information (underreaction, overreaction...).

What about the observation of the players' behavior when there is a
lack of new relevant events, just "noise" ?

There are still many things to observe and study in those fields, and more
generally in what social sciences see as the reasons, processes and effects of
what is called "decision making".

We can have doubts that this would ever be a fully predictive science or
technique (uncertainties will not disappear), other than helping to find
the range of possible scenarios between which deciders might choose.

Maybe we will never end to learn more and more about the human being
and about human societies, a quest that started thousands of years ago!

Other sections of the chapter

See also


BF vs. EMH

Behavioral-Finance Gallery (*)

500+ keywords BF glossary and

1800+ members BF forum

Part A

Individual and social
behavioral biases

(*) The place where you find the full definitions of the various BF phenomena


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