This is a separate page of the M and S sections of the Glossary
Behavioral finance FAQ / Glossary (Model / System trading)
Dates of related message(s) in the Behavioral-Finance group (*):
Year/month, d: developed/ discussed, i: incidental
02/7i - 08/11i - 09/3i + see quant, system trading, decision-making, computer trading
Robotic maids to dust the family portfolio.
Can they really be trusted or might they break the crockery?
Definition: a financial market model is typically a mathematical software that processes market data and tries to detect investment opportunities.
Modeling is quite popular in
finance. There are plenty of mathematical algorithms
and computer models
related to asset markets (stock markets among others).
This might be because numbers are available, and because to process numbers gives a feeling of certainty in a field that is actually a realm of uncertainty (see numeracy bias).
Those
and other market plays on
risks and returns.
They focus usually more on
short term operations than on long term ones
(although some are used for long term previsions and periodic asset allocations).
To cover the whole ground, economics also has been using for a long time statistical models and the related mathematics. Econometrics is based on those.
How are financial models built?
Number grinding through mathematical mills.
Financial market models are created, operated and adjusted by "Quants" (see that word). They use intricate mathematics, derived from hard sciences like physics, and notably "stochastic calculation" (see that phrase), based on probabilities
and historical data series.
We have here market robots guided by mathematicians. Such man-man machine bionic combination gives them:
Models are supposed to be more rational than humans
Specifically, they are better at mimicking (what is known about) realities
Numbers do not fully reflect the pitfalls and opportunities linked to the real world in highly human / social areas such as economics and finance: see numeracy bias.
Also, being developed from historical data, they are rather unfit to anticipate new situations, unless (wise) man-made scenario and disruptive parameters are forced into them.
Finding appliances for every purpose in the quants' kitchen.
The basic - we could even say the traditional - financial models, from which most other more elaborate ones are derived, are
The CAPM (see that glossary article),
Not only some "quant funds", use intensively such tools, but more and more institutions use them in some way, and commercial software are available to all traders.
Here are the main uses:
Generally, for researchers as well as for players,
understand the current market working and its trends, and adapt to it.
More specifically,
buy / sell orientations to help money managers in trading and investing decisions (see decision-making).
to emit directly buy and sell orders, without human intermediation
(computer driven trading / system trading / algorithm trading).
This approach, that leaves robots
Little by little, the market gets in the hand of robots trained at finding and unearthing truffles, and fighting each other to grab them.
Are they good at it? This is the question below.
Caveat, financial model users!
Kick the tires and see if there are hidden bugs and rusty cogs in the machine!
The degree of reliability / predictability of a pricing model is obviously an issue, and here the Murphy law can be at play, as several obstacles intervene:
Thus models might underestimate the real risks, by ignoring some types of
anomalies, notably the rare but extremely dangerous ones (black swans, see below).
Also, statistic-fed machines seems to have less capacity than the human mind to imagine extreme scenarios.
For example a sudden market illiquidity
(see liquidity squeeze), in other words a sudden lack of counterpart for buyers and sellers, might strike because of an external shock or a sudden reversal of investor trust.
This illiquidity factor intervenes exceptionally in large markets, but when it does it has a crucial importance for investors and in some cases for the whole financial system (systemic crisis when it takes the form of a contagious crash). As this is quite rare, model makers have problems to integrate liquidity as a modeling parameter. They might even neglect it totally because of overconfidence.
Such sudden and important changes in the general picture are proper to evolutional or chaotic dynamical system
, which standard "probability laws" might misrepresent, because of disruptions, mutations and emergences (see percolation, bifurcation
, uncertainty...)
Therefore most models work (or worked) in a specific period, market or state of affairs and might be out of step in another one.
If they are only trained to swim, how can they behave when the lake dries up? If they did not envision unprecedented scenarios (see that word), how can they spot them if they emerge?
Therefore, they become easily obsolete. Some even have a very short-lived usefulness.
"best before" date.
After that you will have to scrape them, or at least to make a heavy 15,000 miles maintenance and upgrade.
Use the manual mode as rules might be different than those implanted into the electronic brain.
All in all, they are slaves of what is already known, and of the current interpretations (paradigms).
They might tend to look for the lost keys under the lamppost instead or where they might have fallen.
Thus they might not be too able to understand the unknown
and to adapt to it.
, which might distort prices in an unexpected way.
Instead of correcting those distortions, the human behavior might:
OK, but how to monitor human attitudes?
Polls are not fully reliable (see "sentiment"). An alternative, but only slightly better, would be to sit in a bus or at a cafe terrace and hear people talk.
Here human herding (see that word) gets multiplied by massive and instant "computer herding" (see herding). This conjunction creates a lack of counterparties and an illiquid market, as usually found in market crashes (see that word) or in a milder way in periods of excessive volatilities.
Financial market models are usually not well adapted to long term investment. Models are built mostly for short term trading (and for some of them for medium term allocation arbitrages). The drawback is that unexpected long term / rare effects can suddenly strike in the short term (here again, crashes are the most typical of those effects).
agent-based models (see that phrase), they are only as good as the agent categories are relevant and their behavior is reliably recurrent. This supposes
Therefore, however useful models might be, better
look closely at their assumptions, and at their construction.
The market reactions to the recent "subprime crisis" has again shown the limitations and even some dangers of those trading systems.
Humans should respect the laws of robotics when living with robots.
Whatever their limitations seen above, financial models (which are more and more available as generic computer software) might be helpful:
They are more disciplined we might say. Greed and fear are unknown to robots.
Of course we are not talking here of some toy robots programmed to mimic our basic behavior.
Those are anomalies which humans would not see and would not have the time to take advantage of.
Such financial meltdowns could have happened as well if human beings were in control. They are often the consequence of prior exuberant behaviors. Or in some cases of unforeseeable external shocks.
Anyway, those market androids have to be used with some judgment.
This is exactly what is also said about technical analysis - see that phrase - as a crude ancestor of quantitative analysis and system trading.
Fully automated trading can bring very bad surprises. Not in normal time, but when something new and big happens suddenly while traders were being too confident that computers and advanced stochastic calculations would show the way.
They had the comfortable illusion /
dependence / obedience / addiction that those automatons would do their job or compensate for their own "exuberant" lack of foresight, self-discipline and common sense.
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