Prediction Markets at Google
Origins, Objectives and Implementation
Objective:
- Empowering average employee
- Improve awareness
- Quantify unquantifiable
- Improve morale
25 markets per quarter for 2 years.
Markets about:
- Product launches (number of Gmail users)
- Office/position openings (will this important position be filled on time)
- Employee quality of life (when will gym re-open)
- Competitors’ actions
- Fun markets (who will win the NBA championship)
Implementation
- Continuous double auction with limit orders
- Alternative would be with market makers.
- People can not see each other’s position.
Growth and Usage
Market stats
- Number of shares and number of trades has grown over time (due to added liquidity and more participants)
- Approximately 68,000 trades had occurred by the end of 2006, through a total of 2000 trade accounts
- Long tail distribution:
- One participant has made 3100 trades in 1 year 8 months (~5 per day)
- Approximately 400 participants have made 2 or less trades
- A little more than 50% of trades are made by engineers (but this reflects the overall structure of the company).
- People that are higher up (closer to CEO) and have a long tenure at Google are more likely to participate.
Individual performance
- Geography was a better predictor than job function of success
- Finance and software engineers have done better (best explained by bots?)
- "Batting Average" increases slightly with market participation (evidence of learning or do individuals who are not good quit?)
- Success for certain individuals has been consistent from quarter to quarter
Market set up:
- Typically 5 “options”
- E.g., when does a certain project launch: Jan, Feb, Mar, Apr, May or later
Analysis of Predictiveness
- Average winning price 0 weeks before closing was $55; average losing price was $15
- Some predicted better (close to $100 the week of closing), whereas others like the Bush v Gore election was still 50/50 on election day
Rewards
- Money earned through trading is used to buy raffle tickets
- Prizes are then awarded through a random drawing
- Financial rewards seem not as strong as ‘Reputation’
Further Research
- Robin Hanson, professor of Economics at George Mason University (see webpage)
- Paper on presidential outcome prediction by Paul Rhode and Koleman Strumpf
- Research on the 2004 presidential race by Justin Wolfers and Eric Zitzewitz
