11. Readers contributions
Please send your own proposals
1 -Behavioral finance / economics
Thanks to Jan, Nemo and Seervas for their communications on these topics.
A field of research that seems promising.
To study, for example:
for a given investor, its money personality traits
for any investor segment, its content (types of institutions or private persons), its
motivation (cautious, gambler...) and its relative financial power,
how these investor groups could be somewhat linked to image categories,
the timing of each segment's intervention, and their interaction,
could throw some light on the images' origins, their "life cycles" and also the
"distribution" and "accumulation" phases in the stock market.
It could also help to build agent-based simulation models, and, last but not least,
help each investor to know what makes him/her tick.
1b. Image & manipulation
Although there are not conspiracies round every street's corner, we don't live in
an ideal world.
The temptations to manipulate stock markets are high. This may distort prices,
affecting both the EEV and the image, in a small or a big scale.
See jdddescript's contribution and my comment.
1c. Image & stocks screening
Jean (Leif Ericssen) tell us how his Proto-momentum model screens stocks
with a three step filtering process based on fundamentals, Technical analysis and
stock image potentials.
Leif Ericssen shows us once more what a great contributor he is, by starting a
full study (first two chapters already online) on the Efficient markets theory vs.
This study is a help for our Behavioral-finance Group.
Martin Sewell shows that behavioral finance is not that new, placing its birth with
a DeBondt & Thaler's paper published in 1985.
He feels it is here to stay, using comparisons with TA and EMH (here with a quite
Also, he explains brilliantly how the Random Walk Hypothesis makes is still
the only *stable* stochastic solution.
In other words, this leads to a "behavioral-based RWH".
That page gives also access to the interesting debate "Why this lull on BF?"
started in March 2004
Some say that even if investors have psychological biases, the market stays efficient,
as their actions cancel out in average.
Peter Greenfinch tries to show that collective biases bring durable mispricings and
even make prices to deviate farther and farther from the fair value.
Often, markets are inspired by their own behavior (reflexivity) rather than by reality
This imitative behavior has its rational sides and may help to win.
Except of course if, by wearing off, or after a massive event, it ends up being put in
Alexandre Delaigue seized a real-life example of mimetic rational expectation
by watching a TV game.
1e. Finance and Systems of thinking
Maths are dominant in economics and finance. They bring rationality.
But even Einstein deemed them inappropriate to really represent reality.
Above all, in their applications, they can be used to dress into apparent rigor
some arbitrary and elusive hypotheses.
Otium delivers us in this respect his convictions on the role of maths and
of systems of thinking in finance
Probabilities are less simple as one may think : they are several kind of them.
Also, utility is not such a clear-cut notion, as some famous paradoxes shows.
Ariel M. Viale shows us how all that influence investment decisions.
2 -Fuzzy Finance / Economy and Soft Computing
2a. Fuzzy market prices and expectations
Everything is true or false to a degree, never 0 % or 100 %.
Black or white / right or false math, even probability "laws" (i.e. the normal distribution )
are just simplistic representations of the real world.
To use a linear, probabilistic, monovalent model to determine the normal price of
an asset, gives most of the time a result slightly, or widely, different from the
The real price is one inside a range of many possible ones (multivalence).
Paul Victor Birke shows us the interest of multi-results pricing and of fuzzy market
This is Fuzzy (or multivalent) Finance, based on the logic of fuzzy sets (FL / fuzzy
logic), an advanced mathematic theory used also in control systems, neural networks,
2b. Soft computing, in Fundamental & Technical Analysis
Doug Elias enlarges the fuzzy concept via the Soft computing approach, a selective
use of various "soft" tools available in fuzzy logic, neural nets, imprecise probabilities,
genetic algorithm, learning and belief theories, genetic algorithm and others.
3 - Weak Signals
Weak signals are small, under the surface, unnoticed, overlooked events
that maybig economical and financial evolutions.
The first one, from a Helge Loekke's contribution, deals with the:
4 - Ideas of financial innovations (assets, tools, profession...)
An alternative financial vehicle, as a midway house between bonds and stocks
4b. What future for Financial analysts?
In our Internet age, and after the bubble, the crash, and some scandals,
how to organize stock research and financial analysis diffusion?
5 - Fun with behavioral finance
5a. The imaginary value (humor)
Where Winnie - Le Spéculateur Cuisinier jumps from the stock image to
the "imaginary value".
6 - About stock management (Dare to be dull, by Allan Roth)
Allan Roth, in his contribution "Dare to be dull", shows us one crucial side of
applied psychology for investors, by stressing the danger of getting too excited,
and of overtrading, when managing their hard earned money.
In other words, the common sense lesson from Allan is that if you are having too
much funor seek emotions too much, chances are that you are doing it wrong.
An important contribution to money management and investor profiles topics.
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