Download - 15.567 Economics of Information
15.567 Economics of Information
Prediction Markets
Rodrigo Mazzilli | Damien Acheson | Luis Prata
© 2007 MIT Sloan School of Management
Prediction Markets
Purpose
Produce dynamic probabilistic predictions of future events; Participants trade in contracts whose payoff depends on
unknown future events; The market price will be the best predictor of the event; Example:
Contract pays $1 if Hillary Clinton is elected
Market Price is $0.78
Prediction is 78% likelihood of Hillary becoming President
© 2007 MIT Sloan School of Management
Prediction Markets
Accuracy
Evidence shows that Prediction Markets give better predictions than other less sophisticated tools (i.e. opinion surveys or experts)
Example 1: Markets vs Polls in 41 elections
Average error: Markets (1.49%), Polls (1.93%) Joyce Berg, Robert Forsyth, Forrest Nelson, Thomas Rietz, “Results from a Dozen Years of Election Futures Market Research, University of Iowa (November 2000)
Example 2: Markets vs 1947 Experts in 208 NFL games
Rank: Markets (6th) vs Avg Experts (39th) Emile Servan-Schreiber, Justin Wolfers, David Pennock and Brian Galebach,”Prediction Markets: Does Money Matter?”, Electronic Markets, 14(3),
September 2004.
© 2007 MIT Sloan School of Management
Prediction Markets
Why they work?
The use of (play) money in trading contract prices incentives: Truthful revelation – behave accordingly with convictions; Information discovery – seeking and researching info; Aggregation of information – weighted collective view;
The quality of the prediction depends on: Clear definition of the contract/event; Incentive to Trade; The quantity of performed transactions; Disperse information.
© 2007 MIT Sloan School of Management
Prediction Market
Solution ProvidersAcademic B2CB2B
© 2007 MIT Sloan School of Management
Prediction Markets
Solution example: HP BRAIN
Proprietary algorithms which weight individual’s forecast according to predictive ability and behavioral profile
Forecasting accuracy with a small set of participants (10-20 people)
Removes personality, hierarchy, and bias Improves business prediction in enterprises
– Sales, revenues, operating profits– probability of a successful product– product delivery dates– other quantifiable business metrics
© 2007 MIT Sloan School of Management
Sale
s Fo
reca
st
Marketing scenario “X”, with no changes in sales force alignment, will increase product sales by “Y”% in the next 6 months ?
What will product sales reach in US$ by the end of this year?
Reven
ue
Fore
cast
What will the 1st quarter revenues be? (revenue choices must be created )
What will the 1st quarter operating profits be?
Will the new vehicle model X achieve sales of 5,000 units in its first month?
Pro
duc
t Succ
es
s
In US, 3 months after launching IPTV, the subscriber penetration rate will be?
If we modify the clinical protocol for scenario B when will we be able to show drug efficacy?
Prediction Markets
HP BRAIN and business questions
© 2007 MIT Sloan School of Management
Predict month-to-month operating profits and revenues 14 finance executives from various regions and levels 3-hour training (now greatly shortened) 49% improvement in operating profit predictability
Prediction Markets
Case example: HP Services
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Q2Y03 Q3Y03 Q4Y03 Q1Y04
Over-
/U
nder
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ject
ion in U
SD
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Official forecast
HP BRAIN
© 2007 MIT Sloan School of Management
Accurate prediction of DRAM prices is critical Very volatile pricing Pricing team discussions in the 1-, 3-, and 6-month time
frames 20+ prediction sections
– beat the normal process 13 times– tied 3 times
37% improvement over existing systems Less time and less frequent iterations
Prediction Markets
Case example: DRAM pricing
© 2007 MIT Sloan School of Management
Questions & Answers
Thank you!