If you have 10 bottles of water, and one bottle had poison in it, and you didn’t know which one, you probably wouldn’t drink out of any of the 10 bottles; that’s basically what we’ve got there.
Chris F. Masse March 29th, 2008
Paul O’Neil, about the subprime mortgage crisis.
Implied Probabilities
Chris F. Masse March 27th, 2008
Via Stan Jonas:
Sphere: Related ContentBORING PLUS ONE BET… FEAR OF TIGHTENING BY XMAS OF 2008????
FOMC Imagined Euro$ Euro$ Diff FUNDS
Dates Prob Contract Current Imagined ticks
04/30/08 -150% EDM8 97.730 97.726 0 1.88%
06/25/08 -65% EDU8 97.850 97.834 -2 1.71%
08/05/08 -10% EDZ8 97.765 97.784 2 1.69%
09/16/08 0% EDH9 97.720 97.694 -3 1.69%
10/29/08 0% EDM9 97.510 97.504 -1 1.69%
12/16/08 26% EDU9 97.280 97.265 -2 1.75%
01/30/09 26% EDZ9 97.010 96.996 -1 1.82%
03/16/09 26% EDH0 96.805 96.788 -2 1.88%
01/25/10 56% EDZ1 95.665 95.643 -2 2.36%
03/11/10 46% EDH2 95.575 95.552 -2 2.52%ONE POTENTIAL OUTCOME? ALL ELSE HELD CONSTANT.
FOMC Imagined Euro$ Euro$ Diff FUNDS
Dates Prob Contract Current Imagined ticks
04/30/08 -200% EDM8 97.735 97.851 12 1.75%
06/25/08 -65% EDU8 97.865 97.960 9 1.59%
08/05/08 -10% EDZ8 97.785 97.909 12 1.56%
09/16/08 0% EDH9 97.735 97.819 8 1.56%
10/29/08 0% EDM9 97.535 97.629 9 1.56%
12/16/08 26% EDU9 97.305 97.390 9 1.63%
01/30/09 26% EDZ9 97.030 97.122 9 1.69%
03/16/09 26% EDH0 96.830 96.914 8 1.76%
01/25/10 56% EDZ1 95.680 95.769 9 2.24%
03/11/10 46% EDH2 95.590 95.678 9 2.39%
09/07/10 35% EDH3 95.245 95.364 12 2.92%
03/06/11 29% EDH4 94.965 95.123 16 3.33%
09/02/11 24% EDH5 94.760 94.928 17 3.84%
10/17/11 24% EDM5 94.715 94.888 17 3.84%
Google’s enterprise prediction markets
Chris F. Masse March 26th, 2008
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Using Prediction Markets to Track Information Flows: Evidence from Google - (PDF file - PDF file) - by Bo Cowgill, Justin Wolfers, and Eric Zitwewitz - 2008-01-06
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ABSTRACT: In the last 2.5 years, Google has conducted the largest corporate experiment with prediction markets we are aware of. In this paper, we illustrate how markets can be used to study how an organization processes information. We document a number of biases in Google’s markets, most notably an optimistic bias. Newly hired employees are on the optimistic side of these markets, and optimistic biases are significantly more pronounced on days when Google stock is appreciating. We find strong correlations in trading for those who sit within a few feet of one another; social networks and work relationships also play a secondary explanatory role. The results are interesting in light of recent research on the role of optimism in entrepreneurial firms, as well as recent work on the importance of geographical and social proximity in explaining information flows in firms and markets.
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DISCUSSION: In the past few years, many companies have experimented with prediction markets. In this paper, we analyze the largest such experiment we are aware of. We find that prices in Google’s markets closely approximated event probabilities, but did contain some biases, especially early in our sample. The most interesting of these was an optimism bias, which was more pronounced for subjects under the control of Google employees, such as would a project be completed on time or would a particular office be opened. Optimism was more present in the trading of newly hired employees, and was significantly more pronounced on and immediately following days with Google stock price appreciation. Our optimism results are interesting given the role that optimism is often thought to play in motivation and the success of entrepreneurial firms. They raise the possibility of a “stock price-optimism-performance-stock price” feedback that may be worthy of further investigation. We also examine how information and beliefs about prediction market topics move around an organization. We find a significant role for micro-geography. The trading of physically proximate employees is correlated, and only becomes correlated after the employees begin to sit near each other, suggesting a causal relationship. Work and social connections play a detectable but significantly smaller role.
An important caveat to our results is that they tell us about information flows about prediction market subjects, many of which are ancillary to employees’ main jobs. This may explain why physical proximity matters so much more than work relationships – if prediction market topics are lower-priority subjects on which to exchange information, then information exchange may require the opportunities for low-opportunity-cost communication created by physical proximity. Of course, introspection suggests that genuinely creative ideas often arise from such low-opportunity-cost communication. Google’s frequent office moves and emphasis on product innovation may provide an ideal testing ground in which to better understand the creative process.
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PAPER BODY: In the last 4 years, many large firms have begun experimenting with internal prediction markets run among their employees. The primary goal of these markets is to generate predictions that efficiently aggregate many employees’ information and augment existing forecasting methods. [...] In this paper, we argue that in addition to making predictions, internal prediction can provide insight into how organizations process information. Prediction markets provide employees with incentives for truthful revelation and can capture changes in opinion at a much higher frequency than surveys, allowing one to track how information moves around an organization and how it responds to external events. [...]
We can draw two main conclusions. The first is that Google’s markets, while reasonably efficient, reveal some biases. During our study period, the internal markets overpriced securities tied to optimistic outcomes by 10 percentage points. The optimistic bias in Google’s markets was significantly greater on and following days when Google stock appreciated. Securities tied to extreme outcomes were underpriced by a smaller magnitude, and favorites were also overpriced slightly. These biases in prices were partly driven by the trading of newly hired employees; Google employees with longer tenure and more experience trading in the markets were better calibrated. Perhaps as a result, the pricing biases in Google’s markets declined over our sample period, suggesting that corporate prediction markets may perform better as collective experience increases.
The second conclusion is that opinions on specific topics are correlated among employees who are proximate in some sense. Physical proximity was the most important of the forms of proximity we studied. Physical proximity needed to be extremely close for it to matter. Using data on the precise latitude and longitude of employees’ offices, we found that prediction market positions were most correlated among employees sharing an office, that correlations declined with distance for employees on the same floor of a building, and that employees on different floors of the same building were no more correlated than employees in different cities.4 Google employees moved offices extremely frequently during our sample period (in the US, approximately once every 90 days), and we are able to use these office moves to show that our results are not simply the result of like-minded individuals being seated together. [...]
Our findings contribute to three quite different literatures: on the role of optimism in entrepreneurial firms, on employee communication in organizations, and on social networks and information flows among investors. [...]
The lessons of the literature informed Google CEO Eric Schmidt and Chief Economist Hal Varian’s (2005) third rule for managing knowledge workers: “Pack Them In.” Indeed, the fact that Google employees moved so frequently during our sample period suggests that considerable thought is put into optimizing physical locations. To this literature, which has largely relied on retrospective surveys to track communication, we illustrate how prediction markets can be used as high-frequency, market-incentivized surveys to track information flows in real-time. [...]
Google’s prediction markets were launched in April 2005. The [Google prediction] markets are patterned on the Iowa Electronic Markets (Berg, et. al., 2001). In Google’s terminology, a market asks a question (e.g., “how many users will Gmail have?”) that has 2-5 possible mutually exclusive and completely exhaustive answers (e.g., “Fewer than X users”, “Between X and Y”, and “More than Y”). Each answer corresponds to a security that is worth a unit of currency (called a “Gooble”) if the answer turns out to be correct (and zero otherwise). Trade is conducted via a continuous double auction in each security. As on the IEM, short selling is not allowed; traders can instead exchange a Gooble for a complete set of securities and then sell the ones they choose. Likewise, they can exchange complete set of securities for currency. There is no automated market maker, but several employees did create robotic traders that sometimes played this role.
Each calendar quarter from 2005Q2 to 2007Q3 about 25-30 different markets were created. Participants received a fresh endowment of Goobles which they could invest in securities. The markets’ questions were designed so that they could all be resolved by the end of the quarter. At the end of the quarter, Goobles were converted into raffle tickets and prizes were raffled off. The prize budget was $10,000 per quarter, or about $25-100 per active trader (depending on the number active in a particular quarter). Participation was open to active employees and some contractors and vendors; out of 6,425 employees who had a prediction market account, 1,463 placed at least one trade. [...]
Common types of markets included those forecasting demand (e.g., the number of users for a product) and internal performance (e.g., a product’s quality rating, whether a product would leave beta on time). [...]
In addition, about 30 percent of Google’s markets were so-called “fun” markets –markets on subjects of interest to its employees but with no clear connection to its business (e.g., the quality of Star Wars Episode III, gas prices, the federal funds rate). Other firms experimenting with prediction markets that we are aware of have avoided these markets, perhaps out of fear of appearing unserious. Interestingly, we find that volume in “fun” and “serious” markets are positively correlated (at the daily, weekly, and monthly frequencies), suggesting that the former might help create, rather than crowd out, liquidity for the latter. [...]
Google’s prediction markets are reasonably efficient, but did exhibit four specific biases: an overpricing of favorites, short aversion, optimism, and an underpricing of extreme outcomes. New employees and inexperienced traders appear to suffer more from these biases, and as market participants gained experience over the course of our sample period, the biases become less pronounced. [...]
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FOOT NOTE: One trader in Google’s markets wrote a trading robot that was extremely prolific and ended up participating in about half of all trades. Many of these trades exploited arbitrage opportunities available from simultaneously selling all securities in a bundle. In order to avoid having this trader dominate the (trade-weighted) results in Table 9, we include a dummy variable to control for him or her. None of the results discussed in the above paragraph are sensitive to removing this dummy variable.
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APPENDIX:
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More info on Google’s enterprise prediction markets on Midas Oracle .ORG…
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Sphere: Related ContentThe latest from the Fed’s prediction market…
Chris F. Masse March 26th, 2008
Via Stan Jonas:
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Stan Jonas in his own words:
Sphere: Related ContentWhat’s the BET post the March FOMC? The first rule of “betting” is that one must know what the current market odds are. As of today, there is no doubt as to what the “consensus” bet is. Popular press analogies to the Great Depression aside, there is really only one compound option that remains to be priced.1) When will the FED stop Easing? Currently close to 90% “probability” than one way or the other the FED will be done by June of 2008!!
2) Second: When will ‘everyone else’ believe that the FED will start tightening again. Currently 6 months post the conditioinal cessation by June. That is as the chart shows… high subjective probability that the market will be pricing a positive probability of tightenting at every FOMC meeting from 2009 on out.The chart above… is not just a derived calibration… but to a large extent as in a perfect Heath Jarrow Morton world… each of the forward “probabilities” trades simultaneously.
Thus one can and does “trade” the probability that the FED will be tightning again in September of 2009… just as easily as one can trade the probability that it will be easing or tightening in April of 2008. These “tradeable” bets are then aggregated to derive the value of any fixed income instrument over the corresponding time period.
Being “right” as an investor is exactly what one would expect in a Keynesian/DeFinetti world. Being “right” means that your current subjective “bet” as to the probabilities turns out to be “correct”… not by say September 2009… which is a lifetime away… but by next week!
In fact, this prediction market has acheived the sure sign of maturity. Conditional bets dominate. What will happen if…..
For those with a technical bent and who are familiar with what has been going on in the global fixed income marketplace… the only interesting question should be: How does one derive “the probabilities”?
That’s essentially what the “marketmaker” or oddsmaker in this case gets paid for. Deriving the “hedge” that can capture the conditional probabilities that are being bet on.
We will never have a perfect model of risk. — by Alan Greenspan
Chris F. Masse March 17th, 2008
The most credible explanation of why risk management based on state-of-the-art statistical models can perform so poorly is that the underlying data used to estimate a model’s structure are drawn generally from both periods of euphoria and periods of fear, that is, from regimes with importantly different dynamics.
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Sphere: Related ContentWhat Jim Cramer was saying about Bear Stearns one week ago on CNBC
Chris F. Masse March 17th, 2008
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Dear Jim: Should I be worried about Bear Stearns in terms of liquidity and get my money out of there? –Peter
Cramer says: “No! No! No! Bear Stearns is not in trouble. If anything, they’re more likely to be taken over. Don’t move your money from Bear.
UPDATE: In defense of Jim Cramer…
UPDATE: Barry Ritholtz says Jim Cramer’s TV show should be revamped totally.
UPDATE: Henry Blodget
UPDATE: Crossing Wall Street
Sphere: Related Content
