Midas Oracle .NET

BORING 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%

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Tags: Federal Reserve, FOMC, Stan Jonas

Google’s enterprise prediction markets — Using Prediction Markets to Track Information Flows: Evidence from Google

Chris F. Masse — Wed, 26 Mar 2008 18:39:21 +0000

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

VIDEO — Bo Cowgill on Google’s enterprise prediction markets — O’Reilly Money:Tech

Blip.TV — (FLV file)

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

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:

More info on Google’s enterprise prediction markets on Midas Oracle .ORG…

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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Tags: accuracy, Bo Cowgill, corporate prediction markets, efficieny, enterprise prediction markets, Eric Schmidt, Eric Zitwewitz, gas prices, Google, Google's enterprise prediction markets, Google's prediction exchange, Google's prediction markets, Hal Varian, internal prediction markets, Justin Wolfers, prediction markets, Robin Hanson, Social Networks, USD

The latest from the Fed’s prediction market…

Chris F. Masse — Wed, 26 Mar 2008 15:54:41 +0000

Via Stan Jonas:

Stan Jonas in his own words:

What’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.

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Tags: Federal Reserve, Federal Reserve System, Heath Jarrow Morton, Stan Jonas