Midas Oracle .NET » United States

Socialism is a an addiction.

Chris F. Masse — Tue, 03 Mar 2009 09:37:46 +0000

“U.S. companies, consumers and communities may grow so addicted to government financial help that cutting them off could trigger another recession soon after the current one ends.” Midas Oracle = About + Archives + Authors + Best + Contact + … Continue reading →

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Will the US economy have “absorbed” the financial crisis by Feb. 2010?

Chris F. Masse — Sun, 15 Feb 2009 10:20:20 +0000

Will the US economy have “absorbed” the financial crisis by Feb. 2010?

The United States banks have a capital shortage of $500 billion.

Chris F. Masse — Fri, 13 Feb 2009 10:20:54 +0000

New York Times

Which country will first have ZIRP(Zero Interest Rate Policy) in the year 2009??

Will Obama’s administration set up a government run bank ?

Fantastic, Wall Street is going to suffer this Friday (October 31, 2008).

Chris F. Masse — Fri, 31 Oct 2008 12:05:44 +0000

If you are long Barack Obama for the US presidency, then you wish that the stock markets go spiraling into the abyss. The Wall Street Journal is reporting that “stock futures [are] falling ahead of another round of likely gloomy economic data”.

Excellent. I am so happy I could lay an egg.

Here’s the relevant HubDub prediction markets. (The other prediction exchanges don’t have anything equivalent.)

At what level will the Dow Jones Industrial Average close on Election Day?

Who owns America the Beautiful?

Chris F. Masse — Mon, 20 Oct 2008 13:30:07 +0000

Who will hold most of the U.S debt? (March 2009)

The US banks that will fail soon…

Chris F. Masse — Mon, 15 Sep 2008 10:15:19 +0000

The Best Resources On Prediction Markets

Chris F. Masse — Sun, 14 Sep 2008 21:08:21 +0000

The Best Resources On Prediction Markets

1. The Best Charts Of Prediction Markets

2008 US Presidential Elections

More Charts:

- Probabilistic Predictions = Charts Of Current Prediction Markets

- Charts Of Expired Prediction Markets

- Other Charts Of Expired Prediction Markets

2. The Best Midas Oracle Explainers On Prediction Markets

Prediction markets produce dynamic, objective probabilistic predictions on the outcomes of future events by aggregating disparate pieces of information that traders bring when they agree on prices. Prediction markets are meta forecasting tools that feed on the advanced indicators (i.e., the primary sources of information). Garbage in, garbage out… Intelligence in, intelligence out…

A prediction market is a market for a contract that yields payments based on the outcome of a partially uncertain future event, such as an election. A contract pays $100 only if candidate X wins the election, and $0 otherwise. When the market price of an X contract is $60, the prediction market believes that candidate X has a 60% chance of winning the election. The price of this event derivative can be interpreted as the objective probability of the future outcome (i.e., its most statistically accurate forecast). A 60% probability means that, in a series of events each with a 60% probability, then 6 times out of 10, the favored outcome will occur; and 4 times out of 10, the unfavored outcome will occur.

Each prediction exchange organizes its own set of real-money and/or play-money markets, using either a CDA or a MSR mechanism.

- The Midas Oracle Explainer On Prediction Markets + The Probabilistic Predictions Generated By The Prediction Markets

- The Full List Of The Midas Oracle Explainers On Prediction Markets

- All The Midas Oracle Explainers On Prediction Markets

3. The Best Internet Sites On Prediction Markets

Prediction Exchanges (a.k.a. Betting Exchanges)

#1. Real-Money Prediction Exchanges

- InTrade

- TradeSports

- HedgeStreet

- Iowa Electronic Markets

- Bet 2 Give

- UBC – Election Stock Market

- TradeFair

- iPredict New Zealand

- MatchBook

- BetFair

- Betdaq

- SpreadFair

- Smarkets

#2. Play-Money Prediction Exchanges

- HudDub

- NewsFutures

- Inkling Markets – CNN Political Market

- Foresight Exchange

- Reality Markets

- Cenimar

- Ask Markets

- YooPick – (a FaceBook application)

- Hollywood Stock Exchange

- Pop Sci Prediction Exchange

- The Sim Exchange

- Media Predict

- InTrade (play money) – InTrade .NET – WSJ Political Market – FT Predict

- TradeSports (play money)

- BetFair (play money)

- Yahoo!’s Yootopia

- List of the main real-money and play-money prediction exchanges (betting exchanges) at Midas Oracle

- Complete list of the real-money and play-money prediction exchanges (betting exchanges) at Midas Oracle

- Complete list of the real-money and play-money prediction exchanges at CFM

Software For Prediction Markets

- Inkling Markets – (MSR)

- Consensus Point – (CDA & MSR)

- NewsFutures – (CDA + SR)

- Xpree – (MSR)

- Zocalo – (CDA) – (open-source)

- HSX Virtual Markets – (CDA)

- MicroSoft PredictionPoint – (MSR)

- AskMarkets – (MSR)

- QMarkets – (MSR)

- Gexid – (?)

- Nosco – (?)

- ProKons – (?)

- iPredict New Zealand – (CDA)

- Google‘s Internal Prediction Exchange – (CDA) – (not licensed)

- Yahoo!‘s Internal Prediction Exchange – (DPMM) – (not licensed)

- Trading Technologies International – (CDA) – (not licensed)

- BetFair – TradeFair – (CDA) – (not licensed)

- InTrade – TradeSports – (CDA) – (not licensed)

- HubDub – (MSR) – (not licensed)

- List of the software packages for prediction markets at Midas Oracle

- Complete list of the software packages for prediction markets at CFM

- Complete list of the prediction markets consultants at CFM

Books On Prediction Markets

- Predictocracy: Market Mechanisms For Public And Private Decisionmaking – by Michael Abramowicz – 2008

- Winning On Betfair For Dummies – by Alex Gowar and Jack Houghton – 2006

- Information Markets – A New Way Of Making Decisions – (PDF file) – edited by AEI-Brookings’ Bob Hahn and Paul Tetlock – 2006

- The Wisdom Of Crowds – Why The Many Are Smarter Than The Few And How Collective Wisdom Shapes Business, Economies, Societies and Nations – by James Surowiecki – 2004

Research On Prediction Markets

- Prediction Markets: Wisdom Of The Crowd Comes To The Enterprise. – (MO mirror soon) – by Forrester – 2008-07-14

- Private Prediction Markets and the Law – (PDF file) – (MO excerpts) – by Tom W. Bell – 2008-05-18

- Using Prediction Markets to Track Information Flows: Evidence from Google – (PDF file – PDF file) – (MO excerpts) – by Bo Cowgill, Justin Wolfers, and Eric Zitwewitz – 2008-01-06

- Using Forecasting Markets to Manage Demand Risk – by Intel Corporation’s Jay W. Hopman – 2007-05-16

- Prediction Markets – (PDF file) – by Eric Zitzewitz and Justin Wolfers – 2005

- More academic papers and scientific articles will be listed here, in the near future.

- Prediction Market Science – (at MO)

- Complete list of academic papers on prediction markets at CFM

- The Journal of Prediction Markets – (JPM)

Conferences On Prediction Markets

- Third Workshop on Prediction Markets @ Chicago, Illinois, U.S.A. – 2008-07-09

- Conference on corporate applications of prediction markets – by Koleman Strumpf – 2007-11-01

- Second workshop on prediction markets – 2007-06-12

- Confab Yahoo! on prediction markets – Streaming Video: 100k – 300k – 2006-12-13

News Articles And Opinion Pieces On Prediction Markets

- How do InTrade’s prediction markets work, and are they really accurate? – by CNBC – 2008-08-25

- Regulation Looms for Prediction Markets. – The Commodity Futures Trading Commission is likely to become involved in regulating event futures—and it just may boost these markets. – by BusinessWeek’s Ricky McRoskey – 2008-07-07

- Software taps into the zeitgeist to predict the future. – by CIO New’s Linda Tucci – 2008-05-27

- Prediction Market prices can point to a sure thing. – by Financial Times’ John Authers – 2008-05-17

- BetFair Puts Focus on the Fix in Sports Bets. – by New York Times’ Joe Drape – 2008-05-15

- Prediction Markets: Co-Creating An Organization’s Future – (PDF file) – by Inside Knowledge Magazine’s Victoria Axelrod and Jenny Ambrozek – 2008-05-10

- Foretelling The Future: Online Prediction Markets – (video) – by ABC 20/20′s John Stossel and Maxim Lott – 2008-05-09

- The social utility of the event derivative markets – by professor Vernon Smith – 2008-05-01

- Betting to Improve the Odds – by New York Times’ Steve Lohr – 2008-04-09

- New Understandings in Sports Betting – (PDF file) – by BetFair’s Mark Davies – 2008-04-04

- The Promise Of Prediction Markets – by McKinsey – 2008-04-XX

- Prediction market platform taps the wisdom of employees, partners, customers – by Manufacturing Business Technology’s Karen Dilger – 2008-03-01

- When Markets Beat The Polls – (PDF file) – by Scientific American Magazine’s Gary Stix – 2008-03-XX

- Best Bet for Next President: Prediction Markets – Prediction markets can cut through the clutter of polls and pundits. – by Justin Wolfers in the Wall Street Journal – 2007-12-31

- Mob wisdom means business – So-called ‘crowdsourcing’ lets companies create massive focus groups, garner fresh ideas, and even predict the future. – by InfoWorld’s Lena West – 2007-12-10

- YouBet – The wonders and dangers of online sports wagering. – (page 2) – [BetFair explained to the Americans] – by Slate’s T.D. Thornton – 2007-11-28

- The future of futurology = The prediction markets – by The Economist – 2007-11-xx

- Double or Nothing on the Democrat – [The history of prediction markets] – by The New York Times’ J. David Goodman – 2007-11-04

- Before it’s too late – [enterprise prediction markets] – by Adam Siegel (Inkling Markets CEO) for Forbes – 2007-11-02

- Crowdsourcing The Crystal Ball – by Forbes’s James Surowiecki – 2007-10-15

- Bet on It! – (page two – page three) – by Spectrum’s Steven Cherry – 2007-09-01

- Ask The Market – Companies are leading the way in the use of prediction markets. The public sector may soon follow. – (PDF) – by Region Focus’ Vanessa Sumo – 2007-07-20

- The Science of Success – The New Yorker’s James Surowiecki – 2007-07-09

- When enough people take a stake in the future, it’s like a crystal ball. – Page two. – Republished. – by The Associated Press’ Matt Crenson – 2007-06-23

- The Science Behind PPX – What’s the most accurate way to forecast the future? Simple: make predictions profitable—just like on the PopSci Predictions Exchange. – by Popular Science magazine’s Michael Moyer – 2007-06-15

- Tech lessons learned from the wisdom of crowds – CNET News’s Declan McCullagh – 2006-12-14

- The Election Investors – Political bettors were in force on trading sites, wagering on red or blue. - [WSJ $$$] – by Wall Street Journal’s E.S. Browning – 2006-11-09

- Why Gambling at the Office Pays. – Prediction markets that let workers place bets on business events help HP, GE and other companies make smarter moves. – by Business 2′s Erick Schonfeld – 2006-10-02

- CEO Guide to Technology – Workers, Place Your Bets – More corporations are setting up their own markets for economic forecasts, hoping to tap into the wisdom of employees. – by BusinessWeek’s Rachael King – 2006-08-03

- Business Solutions – How to Decide? Create a Market. – by Wall Street Journal’s Michael Totty – 2006-06-19

- The Future Divined by the Crowd – page two – (mirror) – by The New York Times’ Joe Nocera – 2006-03-11

- Capital Markets – Betting the Ranch on Your Company – While ”prediction markets” offer a venue to wager on the rise of a politician or the fall of a business executive, they might also provide a basis for useful corporate decision-making. – by CFO magazine’s Helen Shaw – 2006-03-06

- Can markets be used to help people make nonmarket decisions? – by Hal Varian – 2003-05-08

Petitions On Prediction Markets

- The Promise of Prediction Markets – (PDF file) – (MO excerpts) – by American Enterprise Institute – 2008-05-xx

Regulations Of Real-Money Prediction Markets

- CFTC’s Concept Release – (PDF file) – (MO excerpts) – 2008-05-XX

Blogs On Prediction Markets

- Midas Oracle .ORG = Prediction Markets For All

- Midas Oracle .NET = Prediction Markets For Enterprises

- Midas Oracle .COM = Prediction Markets For People

- Odd Head – Category: “prediction markets”

- Freakonomics – Category: “prediction markets”

- Overcomig Bias – Category: “prediction markets”

- Marginal Revolution – Category: “prediction markets”

Prediction Market Scholars

- Robin Hanson

Prediction Market Experts

- Chris F. Masse

- Michael Giberson

- Chris Hibbert

- Jason Ruspini

- Emile Servan-Schreiber

Vortals To Prediction Markets

- Links on Prediction Markets – at Midas Oracle

- Chris F. Masse. COM = Vertical portal to Prediction Markets

- IIF’s SIG on Prediction Markets

- Social Bookmarks on Prediction Markets – (All Links – Popular Links) – at Delicious

Economics Resources

- IIF’s Principles of Forecasting

- Social Science Research Network – (SSRN)

- National Bureau of Economic Research – (NBER)

- Resources for Economists – (RFE)

Economics Blogs

- Freakonomics

- Marginal Revolution

- Econ Browser

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

The US economy is now in recession.

Chris F. Masse — Thu, 13 Mar 2008 20:53:13 +0000

Wall Street Journal

The US economy will go into recession during 2008.

Chris F. Masse — Sat, 01 Mar 2008 14:31:12 +0000