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

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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

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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?

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t Succ

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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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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!