Extent3 witology prediction_markets_2012

Prediction Markets
as a Tool of
Crowdsourcing and
Collective Intelligence

Anton Kondratyuk,
Knowledge | Innovation | Talent Witology

Brief information

Kondratyuk Anton,
Witology

Mathematics and Computer Science, B. A., MSU
Financial Markets and Investment, M. A., HSE

4 years experience in crowdsourcing, idea management and
collective intelligence technologies

Witology | All rights reserved. 2

What is information aggregation?

Information aggregation means gathering some kind of information from the
sources that contain it with help of specific methods and tools.

Crowdsourcing means using wide range of people (community) instead of
experts/specialists for solving some specific task.

Solution

Information from
the crowd

People


Principles of crowdsourcing

• Decentralization (there’s no one source of information for participants)

• Independence (people don’t influence each other)

• Knowledge (participants are not specialists but they must have opinion about
what they’re asked)

• Diversity (participants must represent various points of view on a problem)

• Motivation (to be sure they give all information they possess)


Effects of crowdsourcing

1. The more people take part, the better results we have.

2. We don’t look for experts, we look for missed “chains” in a crowd
(in information we get).

3. The efficiency of average participant is not high BUT the overall efficiency
is high. And every participant is much cheaper than any expert.

4. It’s difficult to find really unique solutions inside group of experts who have
definite view on the problem field.


Prediction Markets
emulation of market activity, where future news are traded as assets.


Prediction Markets
aggregate information in prices
exactly as stock markets do

• It’s an instrument of future events prediction
• Traders buy and sell stocks in outcomes of a definite event and in such a way
push the price up/bring it down.
• When a trader believes in some outcome of the given event, he buys; otherwise
he sells. The amount of stock he chooses for a transaction depends on his
confidence in the outcome.
• So if there’s enough liquidity on the market current price is supposed to be an
effective determinant of beliefs in this outcome of the event, weighted by
amounts bought&sold.
• BUT unlike stock exchange the price on prediction markets usually fluctuates
between 0 and 100 and is interpreted as probability measured by traders.
• Traders do what they always do – try to make successful deals according to
information they have. Probability of the outcome is a “side” product of their
activity but still the most valuable.

Prediction Markets scheme

Multi-outcome Binomial

Barack Obama 60,7%

Yes 60,7%
Who will win Will Obama win
presidential Mitt Romney 38,1% 100% presidential 100%
elections 2012? elections 2012?

No 38,1%

Other candidate 1,9%

Data source: Intrade.com


Use and benefits of Prediction Markets
for society, economics and business

• Prediction Markets are used in all fields which are related to risk management and
prediction-based decisions such as politics, economics, science, business and corporate
finance
• Predictions Markets make it possible to aggregate all the information possible at the
moment
• Market mechanism makes it possible to change prediction by any trader at any time
according to information change. So it’s different from betting, polls, etc.
• Prediction Markets give the whole picture of the probabilities of outcomes and, what is even
better, the history of opinions change (information change)
• Prediction Markets are stable to manipulation
• It’s possible to draw tacit and inside information
• The accuracy of Prediction Markets is proved to be better than other methods in not all but
in many cases.
• Effectiveness of virtual currency PM and real-money PM are similar
• It’s a new way for measuring key business points by its own employeesWitology | All rights reserved.
9

Open questions about Prediction Markets (PM)

• What scope they can be efficiently applied to?
• There’s a problem of liquidity similar to real markets, so on small unpopular
markets probabilities can show nothing
• Law hindrances because in many countries PM are treated as gambling (in
the USA the dispute is still on about permission on public real-money PM)
• The accuracy of PM is worse for low (0-5%) and high (95-100%) probabilities
• There’re some markets which can cause public protests (for example, US
DARPA market on Osama bin Laden to be caught)


Prediction markets examples

Social markets:
• Intrade (real-money)
• iPredict
• Iowa Electronic Markets (real-money with limit)
• CantorExchange

Corporate vendors for PM:
• ConsensusPoint
• InklingMarkets

Companies using PM for business predictions:
• Google
• HP
• GM
• Pfizer

Social Trading

Social trading is the process through which online financial investors rely mostly (or solely) on
user generated financial content gathered from various applications as the major information
source for making financial trading decisions.
Today the main application for social trading is using other investors’ transactions for making
own decision. It’s a way of “following” other traders to make the strategy as a combination of
other peoples strategies.
So the main competence changes: instead of trying to use technical, fundamental and all other
type of information available traders try only to find investors whose style they like and choose
weights for them to use.
There’re social trading networks:
• eToro
• Zecco
• Zulutrade
• Currensee


Twitter predicts DJ index
Is it really possible?

Some researches are already done which show correlation between mood state in Twitter and Dow
Jones Industrial Average (DJIA). Moreover, some mood states can predict DJIA 1-3 days before.
The most used mood tweet model by now is GPOMS which includes definite mood states such as:
• Sure
• Happy
• Calm
• Vital
• Alert
• Kind
One really useful state from this set is “calm”. Lagged 2-6 days normalized changes in values show
strong prediction power with DJIA, with accuracy as high as 87,6%. Included into DJIA prediction
models this variable can improve them dramatically.
There are papers that research connection between US indexes and sentiment of twitter-based
community. Results prove that common fear, worry, indifference on the given date can predict
indexes change the next date.


The most famous correlation
DJIA vs normalized “Calm” value

• Period shown is 28 February, 2008 to 3 November. 2008
• Despite high correlation twitter “calm” value can’t predict some important economic
events such as large bank bailout which still have much influence on the market
• The origin of this correlation is still not clear

Extent3 witology prediction_markets_2012

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