Behavioral finance FAQ / Glossary (Quant - Quantitative)

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Dates of related message(s) in the
Behavioral-Finance group (*):
Year/month, d: developed / discussed,
i: incidental

Quant

01/12i - 02/01,7i - 05/2i -07/9i
+ see quantitative analysis /
investment, stochastic, model

Heard in The Stochastic Café, on Greek Avenue:

"My equation is better than your equation"

Definition:

Quant (quantitative analyst) is a colloquial way (*) to call a
financial
practitioner who applies

advanced mathematics (**)
and computing software
to market data,
for
analysis, simulation and decision making.

(*) "Rocket scientist" is another popular appellation.

(**) Those mathematics cross a multitude of data ("big data"),

from fundamental ratios to market fluctuations and try to
find statistical correlations and to
apply stochastic
calculations
(see stochastic) based on the
probabilities  of future events.

History and applications

The golden years of Elvis and Markowitz
From the electric guitar and the physics lab
to the market pit.


There is a before and an after, here!

Until the first half (included) of the 20th century, asset portfolio
management,
apart using some economic and financial analyses
(and private info), was the realm of rules of thumb (some of them
quite sound), spiced with actuary mathematics (quite relevant
also) and some price chart observations (supposed to show a
path through the market minefields).

But in the 50's - 70's, not only Rock and Roll, but also physical maths
applied to financial quantitative analysis
(QA), took over the stage.

Quantitative Analysis started with the first academic
works that launched
the "modern portfolio theory /
modern financial theory"
.

It includes for example the CAPM (see that acronym)
and the Theory of options.

They make an intensive use of stochastic calculation (see that phrase).

Random laws served for breakfast, lunch and dinner.

The main applications were, and still are:

Quantitative value analysis: see the related article
    below,

Quantitative investment decision

models: see model, quantitative investment, system trading.

Quants work mostly in banks and hedge funds and deal mostly with
operations that involve derivative contracts.

Rationale and limits

Can equations cover all real life cases?

Or even avoid a piano to fall on you from the sky?

However rational and useful are the methods used by quants.

They bring the risk of an excessive trust in

statistical series, mathematical formulas (see
quantitative analysis, numeracy bias) and random
laws (see anomalies).

The advantage is to avoid human biases. One of
   them is the "base rate neglect" (see that phrase) that
   shuns probabilities, although they are crucial tools,
   at least in standard / well known situations .

The main flaw might be that quants tend to
   underestimate
the probability / frequency of

extreme risks ("black swans"), when the situation
leaves its usual / standard track.

In other words, they might confuse (well known)
risk, and uncertainty.

What is deemed to happen only once in a billion years
according to the normal law of distribution (see distribution, rare
events...) might
in fact have a real probability to occur
...in the next five years.

As most QA tools ignore or understate the possibility of events at
the same time exceptional
and disastrous,
the consequences might be dramatic.

Those tools usually do not prepare for those

rare events, and are useless (or even
counterproductive) to help manage them.

Human imagination, historical research and narrative methods,
when not biased, might be superior to standard math in defining
(and dealing with) some extreme scenarios.

Specifically those in which market price volatility changes its
nature
,

from an ordinary vibration reflecting a classical law
    of randomness,

to something more chaotic (see dynamical
    systems
).


A typical example: illiquidity

Most quant based trading models consider counterparts to be
always  available, therefore allowing to hedge and make arbitrages
whenever needed.

Therefore, they become impotent when a liquidity crisis (see
that phrase) strikes.

Probability laws used in normal times become obsolete in the
middle of a chaos and crisis akin to the 100 year storm.

The market models that quants use


The
"quantitative analysis" article below gives details about the types of
models that quants like to use.

Those tools make a massive use of mathematics, based largely on stochastic
calculation
(a subfield of probabilities that applies to random dynamical
systems
: see the phrase).

The risk-return parameters which are used (beta, gamma, delta...)

are called colloquially "the Greeks".

Quant fund

See quantitative investment

Quantitative analysis / QA

01/10i + see models, numeracy,
probability, model, stochastic

Questioning the market with a computer chip.

Quantitative (investment) analysis / QA
     covers two kinds of practices

(that coexist or not in the same model):

1) Asset value

calculations

 


Those equations / models take into
account various data and
parameters
related to:

The company (endogenous data)

The market (exogenous data):
   expected
incomes, interest rates, risk
   premia, volatility...

2) Investment /

trading models.

Here we go from
valuation to money
management
decisions 
(see "quantitative
investment").


Those models try for example to detect
"buy or
sell signals" and "arbitrage
opportunities" when asset prices and
returns deviate from "normal" random
distribution laws.

A model's time horizon is important,
as a robot with a short time view might
misunderstand what signals those
deviations give, not seeing that a blip
might show (or hide) the start of a
trend.

Many of those models are based on "stochastic calculation"
(see that word).

It is a field of probability-related advanced
mathss  that applies to dynamical systems that follow random
processes

(this is not the case at all times for all dynamical systems - see that
phrase - as many of them, here is the trap, often diverge from
standard probability laws).

Also, among the investment models, some are akin to technical analysis,
but in a more sophisticated and mathematical way that try

Either to spot a short term "anomaly" that opens a possibility of

fast reversion to the statistical average or to the middle of the trend
corridor.

Or, on the contrary, the start of a new trend, thus opening the

opportunity of a new medium term strategy.

Does QA opposes or complements
     other market approaches?

Still not the do-it-all / Swiss army knife of finance.


QA is one of the four analysis tools that market professionals usually quote :
BA, FA, QA, TA (see those acronyms).

There is still no "string theory" to unite those types of analyses.

It is true that they have at least as many points of contradictions than of
convergence.

This tool might detect recurrent market anomalies, by comparing its
findings to what an "efficient" model would give.

It can thus be used as well in everyday trading as in behavioral
finance research
.

That is what makes quantitative behavioral finance (see
that phrase)
one of the behavioral finance sub branches.

? pi.arlef.gif or ?


Quantitative analysis and their models
* have their usefulness as seen above.

  * also have drawbacks, as seen in the
  "model" article and summed up below :

They might fall in the "numeracy" bias trap (see that phrase), as
   an excessive trust
in

Past series,

Theoretical equations,

Standard random laws,

Market indicators such as "implied volatility"...,

They might become used blindly, as standard recipes in
   cases to which they
can not be applied soundly, therefore turning
   into a
bounded heuristic and a herding tool, 


They are at a loss to foresee and/or take into account some

    events unknown to the model.

This is the case of:

* events or combinations of events without known precedent

* "rare events" (see that phrase), for example sudden liquidity
    crises (see liquidity squeeze).

Robots' fuses blow when exceptional events strike!

A broader economic and market culture and experience might be
able to compensate somewhat that overconfidence in mechanical
prevision tools.

To conclude on a more general psychological topic, too much trust in
directly
available or well known data
can lead to the well known
paradox, of looking for one's lost keys in the street at night, not where
they might have fell, but under the lamppost because it is where there is
enough light.

 

Dates of related message(s) in the
Behavioral-Finance group
(*):

Year/month, d: developed / discussed,
i: incidental

Quantitative behavioral finance

See behavioral finance

Quantitative investment / trading


01/12d + see systems,
system trading, model,
algorithmic trading

Robotic money dealers

Quantitative investment / trading is the use of mathematical models (see
"model", and "quantitative analysis"):

To make previsions of returns (due mainly to price
    moves),

To evaluate risks,


To help decide financial portfolio operations (arbitrages),

so as to optimize asset allocation or diversification,

Mostly for day-to-day trading (or even "system trading", see
    that phrase).

Therefore less for long term investing (except if the model
includes it).

This short term inclination, not to say bias, contributes
to an abundant use of sophisticated derivative contracts
devised by ...quants.

Those investment models take thus into account, among other parameters,
risk-return ratio
and related data.

They are used by - among other financial institutions - some mutual funds
called "quant funds".

Also some commercial software is available to individual traders.

Quantum jump, lump

00/6i,10i,12i + see percolation
threshold,
Markovian jump

When a market suddenly chooses to ride a rocket
or to dive from a cliff.

The quantum word is taken here just as a metaphor.

It means that market prices and returns might leap suddenly from
one behavioral phase to another.

Such an abrupt change could be linked to percolation (see that word),
Markovian jumps, external shocks...

(*) To find those messages: reach that BF group and, once there,
      1) click "messages", 2) enter your query in "search archives".

Members of the Behavioral Finance Group,
please vote on the glossary quality at
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