11. Readers contributions
Please send your own proposals  

1a. Investors Categories and Profiles.

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.

1c2. How efficient are markets?

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. Behavioral finance.
This study is a help for our Behavioral-finance Group.

1c3. BF: will it last, or is it a fad ?

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
picturesque projection).

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

1c4. Do investors biases cancel out?

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.

1d. Mimetic rational expectations

Often, markets are inspired by their own behavior (reflexivity) rather than by reality
(fundamentals).

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 check.

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

1d. Probabilities, utility and decisions

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.

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 market price.

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
expectations.

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,
artificial intelligence...

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.

Weak signals are small, under the surface, unnoticed, overlooked events that may
announce big economical and financial evolutions.
The first one, from a Helge Loekke's contribution, deals with the:

3a. Internet value chain

4a. GDP-indexed bonds

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?

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 fun
or 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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