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15.567 Economics of Information

15.567 Economics of Information. Prediction Markets Rodrigo Mazzilli | Damien Acheson | Luis Prata. Prediction Markets Purpose. Produce dynamic probabilistic predictions of future events ; Participants trade in contracts whose payoff depends on unknown future events;

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15.567 Economics of Information

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  1. 15.567 Economics of Information Prediction Markets Rodrigo Mazzilli | Damien Acheson | Luis Prata

  2. Prediction MarketsPurpose • 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

  3. 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.

  4. Prediction MarketsWhy 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.

  5. Prediction Market Solution Providers Academic B2B B2C

  6. Prediction MarketsSolution 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

  7. Prediction MarketsHP BRAIN and business questions Marketing scenario “X”, with no changes in sales force alignment, will increase product sales by “Y”% in the next 6 months ? Sales Forecast What will product sales reach in US$ by the end of this year? Revenue Forecast 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? Product Success 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?

  8. Prediction MarketsCase example: HP Services 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

  9. Prediction MarketsCase example: DRAM pricing • 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

  10. Questions & Answers Thank you!

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