Is WeatherBill doing well, really??

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WeatherBill does so well that TechCrunch has just published two –-yes, two–- blog posts on it, today (Wednesday, October 17, 2007). Here’-s the first one, which basically says that two VCs have just poured $12,5 million dollars in it. Good for them. The second blog post, written by another TechCrunch writer, and which has been quickly taken off their website, basically said the same, but with this twist:

CEO David Friedberg says that WeatherBill has hundreds of customers and faces such high demand that it needs to bring more people aboard to increase capacity. The site has launched not only in the US but Canada, the UK, the Netherlands, Spain, Germany, and Norway as well.

So, should we believe the content of this now-deleted blog post? Or was it deleted because this information is not accurate? Mystery. ValleyWag should investigate. :-D

APPENDIX: Here’-s the deleted TechCrunch blog post on WeatherBill. (The second item that follows is the first blog post that was published by TechCrunch.)

Deleted TechCrunch WeatherBill

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UPDATE: VentureBeat on WeatherBill…-

VentureBeat on WeatherBill

UPDATE: Mark Hendrickson of TechCrunch…-

Our apologies for misleading everyone into thinking Weatherbill enables people to gamble the weather as if it were a casino game. The service is meant rather to provide insurance for companies that could be aversely affected by fluctuations in the weather.

Weatherbill’s CEO informs us that only companies with a net worth of at least $1 million can participate due to regulations of the Commodity Futures Trading Commission. He also says that Weatherbill is the first service to ever provide access to hedges on the weather (online or otherwise).

Also, for anyone wondering why we had two posts up about this story, that’s because Duncan and I reported on it independently by accident. I guess you could say we both find the weather very interesting.

HISTORY: Prediction Markets Timeline

For an updated version of this document, see the “-paged”- Prediction Markets Timeline.

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CHRONOLOGY &amp- HISTORY: Prediction Markets Timeline

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Feel free to post a comment or contact me, and I’-ll correct or add a factoid. Thanks.

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#1. Historical Prediction Markets

According to Paul Rhode and Koleman Strumpf, prediction markets almost never got it wrong forecasting the 19 presidential elections that took place from 1868 to 1940. (PDF)

#2. The three Iowa Electronic Markets founders (Robert Forsythe, Forrest Nelson and George Neumann)

“-We ran our first market in 1988. We didn’t have regulatory approval at that point so we were restricted solely to the University of Iowa community. We had under 200 traders and under $5,000.”- –- [Robert Forsythe - PDF file]

- [CFTC's no-action letter to the IEM - 1992 - PDF file]

- [CFTC's no-action letter to the IEM - 1993 - PDF file]

#3. Robin Hanson

a) Robin Hanson set up and ran a rudimentary prediction exchange (a market board, PPT file) in January 24, 1989. The outcome to predict was the name of the winner of a Poker party.

b) Until evidence of the contrary, it seems that Robin Hanson was the first to set up and run a corporate prediction exchange —-at Xanadu, Inc., in April 1989. See: A 1990 Corporate Prediction Market + Anonymity is important for employees trading on internal prediction markets.

Robin Hanson: “-I started a market at Xanadu on cold fusion in April 1989. In May 1990, I started a market there on whether their product would be delivered before Deng died.”-

c) Until evidence of the contrary, it seems that Robin Hanson was the first to set up and run a bunch of imagination-based prediction markets. See the Murder Mystery Evening described by Barney Pell —-circa June 8, 1989.

d) Until evidence of the contrary, it seems that Robin Hanson was the first to write a paper on prediction markets created and existing primarily because of the information in their prices (as opposed to markets created primarily for speculation and hedging).

Could Gambling Save Science? –- (Reply to Comments) –- by Robin Hanson –- 1990-07-00
Market-Based Foresight: a Proposal –- by Robin Hanson –- 1990-10-30
Idea Futures: Encouraging an Honest Consensus –- (PDF) –- by Robin Hanson –- 1992-11-00

e) Robin Hanson godfathered the Foresight Exchange (created in 1994) and NewsFutures (created in 2000).

f) Robin Hanson invented the concepts of decision markets (PDF) and decision-aid markets.

g) Robin Hanson invented a new market design (for the 2000-2003′-s Policy Analysis Market), the Market Scoring Rules, a mix between CDA and Scoring Rules —-now in use for most enterprise prediction markets and public, play-money prediction exchanges. Note that MSR is mainly used in a one-dimension version, but many researchers are interested in its combinatorial version.

#4. Other Pioneering Public Prediction Exchanges (Betting Exchanges, Event Derivative Exchanges) and Inventors/Innovators/Entrepreneurs

a) The Foresight Exchange was founded on September 22, 1994 by Ken Kittlitz, Sean Morgan, Mark James, Greg James, David McFadzean and Duane Hewitt. The Foresight Exchange is a play-money prediction exchange (betting exchange) managed by an open group of volunteers. It pioneered user-created and user-managed, play-money prediction markets. Any person can join the Foresight Exchange and interact with the rest of the Web-based organization. An independent judge (independent from the owner of the claim) should be appointed among the volunteers. [Thus, it's not "DYI prediction markets".]

b) The Hollywood Stock Exchange was founded on April 12, 1996, by Max Keiser and Michael Burns. See the patent for the Virtual Specialist. For more info, see: Is HSX the “longest continuously operating prediction market”??? –- REDUX

c) BetFair was founded in 1999 by Andrew Black and Edward Wray, and was launched in England in June 2000. As of today, BetFair is the world’-s biggest prediction exchange (betting exchange, event derivative exchange).

d) NewsFutures was founded in March 2000 and launched in September 2000 in France and in April 2001 in the US by Emile Servan-Shreiber and Maurice Balick. See: NewsFutures Timeline. NewsFutures was the first exchange to let people buy or sell contracts for each side of a binary-outcome event. The advantage of this design is that it avoids the need for “-shorting”-, a notion that tends to confuse novice traders. NewsFutures later extend that approach to deal with n-ary outcome events while implementing automatic arbitrage.

e) TradeSports was launched in Ireland in 2002 by John Delaney. InTrade was later purchased and became a non-sports prediction exchange (betting exchange). As of today, InTrade is the biggest betting exchange on the North-American market —-where betting exchanges are still illegal. As for TradeSports, it closed at the end of 2008, alas.

#5. The Policy Analysis Market Brouhaha

a) Robin Hanson was the main economist behind the 2000–2003 US DoD’-s DARPA’-s IAO’-s FutureMAP–Policy Analysis Market project. (For this project, Robin Hanson invented a new market design, the Market Scoring Rules.) On July 28, 2003, two Democratic US Senators called for the termination of PAM, the the big media gave airtime to their arguments, and the US DOD quickly ended the IAO’-s FutureMAP program.

b) The second branch of the 2000–2003 US DoD’-s DARPA’-s IAO’-s FutureMAP program was handled by the Iowa Electronic Markets and was intended to predict the SARS pandemic. (This project later gave birth to IEM’-s Influenza Prediction Market.)

#6. James Surowiecki’-s The Wisdom Of Crowds

a) James Surowiecki’-s book, The Wisdom Of Crowds, was published in 2004.

b) Impact of The Wisdom Of Crowds.

#7. Recent Public Prediction Exchanges (Betting Exchanges, Event Derivative Exchanges) and Inventors/Innovators/Entrepreneurs

a) US-based and US-regulated HedgeStreet was launched in 2004 by John Nafeh, Russell Andersson, and Ursula Burger. A designated contract market (DCM) and a registered derivatives clearing organization (DCO), HedgeStreet is subject to regulatory oversight by the Commodity Futures Trading Commission (CFTC). In November 2006, IG Group bought HedgeStreet for $6 million.

b) Inkling Markets was launched in March 2006 and co-pioneered (with CrowdIQ, which later bellied up) the concept of DIY, play-money prediction markets.

c) In September 2006, TradeSports-InTrade was the first prediction exchange (betting exchange, event futures exchange) to apply Chris Masse’-s concept of X Groups. See: TradeSports-InTrade prediction markets on Bush approval ratings.

d) HubDub was launched in early 2008 and is the second most popular play-money prediction exchange, behind HSX.

#8. Enterprise Prediction Markets

a) Until evidence of the contrary, it seems that Robin Hanson was the first to set up and run a corporate prediction exchange —-at Xanadu, Inc., in April 1989. See: A 1990 Corporate Prediction Market + Anonymity is important for employees trading on internal prediction markets.

b) In the 1996–-1999 period, HP ran a series of internal prediction markets to forecast the sales of its printers.

c) Eli Lilly sponsored 10 public, industry-level prediction markets in April 2003 (on the NewsFutures prediction exchange).

d) Eli Lilly began using internal prediction markets in February 2004 (powered by NewsFutures).

e) Google‘-s Bo Cowgill published about their use of internal prediction markets in October 2005.

f) Since then, many companies selling software services for enterprise prediction markets have been created.

#9. Disputes Between Traders And Exchanges

a) The scandal of the North Korean Missile prediction market that erupted in July 2006 is, as of today, the biggest scandal that rocked the field of prediction markets.

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TradeSports-InTrade: United Nations > John Bolton as US Ambassador to United Nations

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I’-m curious to see who’-s going to be on the “-yes”- side of this brand-new contract. From what I heard yesterday on NBC Nightly News, this neo-con is toasted. But maybe I don’-t know the full story.

Addendum (November 15): Sacha Peter posted a comment…-

Well, at least one person out there is currently willing to lay you 999:1 odds that he will get confirmed and he’s willing to stick his neck out to the tune of $10 against your $9,990 for it. It’s too bad even if you win you’ll still have to shell out $30 in commissions and $100 in expiration fees to collect your $10 in winnings. What a deal!

The five minutes on my 15 minutes of WSJ fame.

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Coming from a politics-obsessed family, we (family and I) have been fans of predicting election outcomes for as long as I can remember. We are all conservative-libertarians of one hue or another, and I began writing “-wingnut”- screeds in eighth grade for my junior-high newspaper. What with the Florida Bush recount and 9/11, a superpoliticized era had dawned. My first forecast was 2002, and I got all the Senate races right but for one. Uncanny, I thought. Then came 2004, and I nailed that one too. Really uncanny–-but then again, every clock was right twice a day, and this being the high and higher tide of “-my side,”- my predictions didn’-t seem so uncanny in retrospect.

Fast forward to October 2006, and I was a junior in college with a mediocre academic record in finance and Chinese. An election was gearing up. I wanted to put my forecasting ability to the test. I also realized that, by blogging the rationales for my trades, it could become a valuable tool in my quest for a summer internship/job, to represent a side of me that my GPA didn’-t represent at all. Not exactly lacking confidence, I put $2500 where my mouth was, and soon afterwards plowed in another $1000. I shifted in and out of many positions, but the common denominator was (as I said on my own blog some days ago) that I believed the market was underestimating the intercorrelation between congressional races, and especially Senate races all over the country. In other words, I bet very heavily on the Democrats taking both houses.

Three or so weeks into my blog, I was getting scattered, but very positive feedback about my material. People in Tradesports threads began quoting it, and a major Tradesports speculator asked me for some further opinions regarding the direction of the 2008 US presidential nominations markets. (I told him not to do anything other than short Hillary, but to especially not short Barack Obama, until after the election, and I couldn’-t have any “-gut feeling”- until I had gauged what a Dem election victory would mean.) So I knew my material was good, but I was still pretty surprised, not to mention ecstatic, when WSJ reporter Jim Browning contacted me for information about election prediction markets. So I happily gave him everything he asked, and the superb WSJ article was the result. (That’-s the non-$$, Pittsburgh P-G version.) But as our conversations continued into election night, I begain to despair about my positions…-

In Virginia, George Allen had about a 12,000-vote lead with about fifteen counties remaining to vote. I knew they were in the pro-Webb counties (Fairfax, Loudoun, Richmond City…-) but those precincts ranged from barely better than even to 72-28 (Richmond). Webb would have required statistically…-.unlikely turnout and/or margins in order to win. A lot of people on DailyKos and other communities emotionally invested in a Webb victory, processing new updates literally seconds after they came, had given up on Webb. Harry Reid came on TV and his body language screamed, “-I don’-t think the Senate is in play anymore, even though I thought it was a couple of hours ago…-but that was too much to ask, anyway.”- Without Virginia, the calculations for the Democrats’- taking the Senate became very grim, very fast.

Final polls (which I had spent the previous weekend trashing) showed VA breaking for Webb, comporting well with my own intuition. As the returns came in, however, Allen seemed to have an insurmountable lead with about 96% of precints reported. I concluded that Allen would win re-election, barely. Michael Barone’-s forecast to the contrary, I noticed that the remaining precincts to report were healthy-majority Democrat (about 60-40, 65-35), but I didn’-t think that Webb would be able to cut Allen’-s lead in half–-well, maybe half, but not zero it out. (I learned only later that when Virginia says “-precincts reporting,”- it apparently does not include absentee ballots when it says that. Or it reported them before it started tallying up the actual votes from the voting booths on that day. Or something. But a bunch of absentee ballots flowed into Fairfax later that netted about 7k more votes for Webb.)
However, rewinding to that despairing moment, the Democratic machines in Richmond City, Fairfax and Loudoun had waited until all other precincts had reported before reporting. Now, I don’-t know about Virginia, but I know that in Missouri, the urban Democratic machines in STL and KC have a certain notoriety (in some circles, anyway) of waiting until every other precint has reported, and then releasing surprisingly high results (that usually imply incredible voter turnout–-certain STL precincts reporting 97%, 100+% turnout isn’-t unheard of), magically pushing the Democratic candidate over the top by a fraction of a percentage point. (I don’-t want to start a flame war here–-I think American politics is a blood sport, both sides have their different ways of playing dirty, and this was just something I didn’-t factor in.) And six to twelve months after the election, some low level Democrats get a year in jail for voting fraud. It happens like clockwork, except that this time around I don’-t think the MO Dems will need to resort to that. But I digress…-

So I figured VA was lost when it wasn’-t, and I puked up all the SENATE.GOP shorts. At one point, over 50% of my entire principal was gone. Then, after despairing for about ten minutes, I went back to the TS markets intending to try and make back what I could. Then I realized that Webb had magically jumped into a 2200 vote lead in VA with 100% of precincts counted. I had already looked at the counties and their turnout/margin statistics and figured that couldn’-t have happened, but it had. So after losing over a couple grand…-not to mention feeling like a complete idiot for throwing away $8000 by buckling at the last possible second, I hopped back on the SENATE.GOP train and rode it down to zero, and made back that entire original investment, plus about $150 left over.

So a lot of lost hair, Wheat Thins, NoDoz, bad grades and exhaustive political analysis later, I felt pretty vindicated, even though I had managed to squander 90% of the potential compensation. (I did indeed lose hair.) I had staked 1,000 shorts against SENATE.GOP.2006, well against the majority view of the market. As I recall, total volume going into the election was 35k or 40k trades, but because some significant fraction of that was the same positions being flipped back and forth between a stagnant pool of traders before I’-d arrived, it was probably closer to 5% of outstanding positions. If my money hadn’-t buttressed the market-minority’-s view, the price would have been even more inaccurate–-before election day, the price hovered around 70 percent, and without my heavy position on the “-minority”- side, it would have been closer to 80-20 in favor of the GOP holding onto the Senate. Plus, going into the election, I had stuck by my calculations even as the market had continued to erode my investment. Having that kind of confidence and analytical precision vindicated meant much more to me than $8-9000 in potential winnings lost.

–-Alex Forshaw

P.S. On that non-mercenary note, I’-m seeking an internship this summer involving event derivatives trading/research or options trading, either academia- or finance-based. If you’-re interested, please e-mail to: alex.forshaw@gmail.com

Speculating (and hedging?) on US presidential prediction markets would have social utility. Dixit Robin Hanson.

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If Deep Throat is right and the CFTC has indeed already given its stamp of approval to presidential prediction markets, then HedgeStreet and USFE would be well advised to listen to professor Robin Hanson’-s idea with great attention:

Using data from a site like Tradesports.com to forecast who will win an election is just scratching the surface, said Robin Hanson, associate professor of economics at George Mason University in Fairfax, VA, and one of the founders of the field of prediction markets. Although the economic incentive is high for picking a winner, Hanson would like to see prediction markets forecast the consequences of a candidate getting into office. Will unemployment go up or down? Will we have more or less trouble in Iraq? Will we decrease or increase the deficit? “-The social value of telling people who’-s likely to win is questionable. The social value of telling people the consequences is arguably far higher,”- said Hanson.

My Question To Professor Robin Hanson: The prediction market that would be interesting would be the one featuring the elected candidate (the so-called “-President-Elect”-). But the expiry of the other prediction markets, featuring the defeated presidential candidates, would be impossible to judge, since these presidential candidates by definition won’-t take office and have any power on the US government. And if the game is murky, you won’-t find any traders willing to risk his/her shirt on those kinds of US presidential prediction markets.

Addendum: Robin Hanson has posted a comment, and I republish it here for everyone to see…-

Let U = the unemployment rate, D = Democrats win, and R = Republicans win. An exchange rate between “Pays $U if D” and “Pays $1 if D” gives an estimate of E[U|D]. Similarly, an exchange rate between “Pays $U if R” and “Pays $1 if R” gives an estimate of E[U|R]. We can compare E[U|D] and E[U|R] to see which candidate is expected to have a lower unemployment rate. And we know how to pay off all of these assets, no matter what happens.

Robin Hanson would like to see prediction markets forecast the consequences of a candidate getting into office. – REDUX

TEN CEO John Delaney finally admits that the new law will cut off TradeSports-InTrades revenues.

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He writes:

Our entire team are very busy with a bunch of things right now
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** Offering new and easier ways for members to deposit funds to the exchange.

As for trader “-Todd73NJ”-, he found out that Web-based bookmaker/sportsbook BetCris is testing a workaround for the Internet Gambling Prohibition and Enforcement Act:

1) They mail you the card
2) You fund it via Western Union – either online from a credit card or at a location via credit card or cash
3) You will be charged by Western Union approx $40 per $1,000 transfered
4) They refund the charges you are charged once you deposit to their site and roll over the money.
5) Withdrawing is free to send the money back to your debit card.
6) Normal ATM transaction fees when you take out cash from any ATM. Or you can use it at places that [accept] the certain purchase options they mentioned.

Remember: Neteller (the main financial source of TradeSports-InTrade) is out of the US market.

Accuracy of futures prices as predictors of the fed funds rate

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I’-m just finishing writing a new research paper whose goal is to come up with a better measure and understanding of the lagged effect of monetary policy on the economy. One of my claims is that the public’-s expectations of what the Fed is going to do next play a key role in that process. In this, the first of several posts based on that paper, I describe some of the properties I’-ve found for fed funds futures prices as predictors of subsequent Fed policy changes.

The primary policy tool of the U.S. Federal Reserve is manipulation of the federal funds rate, an overnight interest rate on interbank loans that is quite sensitive to the total quantity of reserve deposits that are created by the Fed. The Chicago Board of Trade offers a futures contract whose payoff is based on the average value for the effective fed funds rate over all of the calendar days of a specified month.If this were a pure forward contract, no money would change hands until the first-of-month settlement day. The actual futures contracts are a little more complicated, since the exchange will require you to commit collateral to prove you can honor the contract, and these margin requirements will increase if the market moves against you. However, a recent paper by Monika Piazzesi and Eric Swanson demonstrates that the impact of these margin calculations on the value of the contracts should be quite small, and I will discuss here the simpler case of how to evaluate a pure forward contract.

Consider first how a contract that specified a 5.25% value for the current month’-s fed funds rate would be valued at the start of the last day of the month (the day before settlement). If the actual rate turns out to be lower than 5.25%, the next day the seller of the contract will have to compensate the buyer for the difference (paying $41.67 per basis point in the standard contract). If you were the buyer of the contract, this would for you be a pure profit. The primary consideration that might prevent you from taking this bet is a concern that perhaps the rate would end up above 5.25%, in which case you’-ll owe money. If speculators are risk neutral, the contract price will be bid up or down to the point at which its implied interest rate just equals traders’- expectations of what the settlement rate will turn out to be.

On the next-to-last day of the month, similar logic would again imply that the price reflects the market expectation at that time. New information could well come in after this, causing the price to move up or down before settlement. But if it were possible to anticipate, say, a price increase between the penultimate and last day of the month, there is a pure profit opportunity from buying on October 30 and selling on October 31. A statistical principle known as the Law of Iterated Expectations implies that the October 30 price should not only equal the expected settlement value, it should also equal the expected October 31 price. As time goes on and new information comes in, of course we know that the price is likely to change. But none of us can predict the direction. In other words, this simple theory suggests that the futures price should follow a martingale, in which the best forecast of where the price is going to be tomorrow is always just today’-s price.In my statistical analysis I looked at daily changes in the interest rate implied by the current month’-s fed funds contract (denoted f1d), the following month’-s contract (f2d), and the month after that (f3d)- for example, for d = October 31 we could consider the change in the October contract (f1d), the November contract (f2d), or the December contract (f3d). The graph below plots daily changes in the interest rate implied by the current month contract from October 1988 through June 2006.

f1d.gif

On average, the values of f1d, f2d, and f3d all turn out to be negative over this sample period, with t-statistics around -4. This represents strong evidence against the martingale hypothesis, and some researchers have interpreted this bias as evidence of some kind of average risk or hedging premium reflected in the futures prices.

However, if you look at the graph above, you will see that it is a pretty wild series. Forty-six percent of the observations are identically zero, while 25 observations exceed 5 standard deviations. The variance is considerably larger at the beginning of the sample or the start of a month, with the volatility appearing in clusters and particularly on days of major monetary policy announcements. If one models all these volatility dynamics and departures from a Gaussian distribution, the maximum likelihood estimate of the population mean of f1d, f2d, or f3d all turn out to be positive rather than negative, and far from statistically significant. The sample median of all three series is also exactly zero. I therefore see the nonzero sample mean not as an indication of bias on the part of futures markets, but rather as reflecting the fact that there were a few big moves down in interest rates over this period

that caught traders by surprise.

I also looked for whether changes could be predicted on the basis of lagged changes, by regressing fid on a constant and five of its own lagged changes. OLS coefficient estimates along with their 95% confidence intervals are shown below.

fid_autoregressions.gif

The first lag is always highly statistically significant. Its value, however, is only around 0.15, which gives the regression an R2 of less than 0.03 and essentially zero predictability looking more than one day ahead. It is quite likely that this very modest degree of predictability could be attributed to measurement error in resolving daily bid-ask factors rather than systematic errors or risk factors in futures markets.

The paper by Piazzesi and Swanson mentioned above documents some predictability using monthly data of longer-horizon fed funds futures prices based on a number of interest rate spreads. However, consistent with their findings, I find these spreads do not predict the daily movements in the prices associated with the near-term fed funds futures contracts that I am studying, as summarized in the table below:

Explanatory variable Dependent variable
xd-1-1 f1d f2d f3d
10-year minus 5-year
Treasury spread
0.058
(0.086)
-0.036
(0.117)
-0.070
(0.138)
5-year minus 2-year
Treasury spread
-0.009
(0.058)
-0.085
(0.079)
-0.126
(0.093)
2-year minus 1-year
Treasury spread
-0.072
(0.112)
-0.136
(0.153)
-0.172
(0.181)
1-year minus 6-month
Treasury spread
0.006
(0.173)
0.302
(0.236)
0.439
(0.279)
Baa minus 10-year
Treasury spread
-0.035
(0.058)
-0.126
(0.079)
-0.184*
(0.094)
12-month job growth
(revised data)
0.017
(0.023)
0.089**
(0.031)
0.125**
(0.036)
12-month job growth
(real-time data)
0.016
(0.024)
0.093**
(0.033)
0.121**
(0.039)

I also replicate with these data Piazzesi and Swanson’-s observation that employment growth helps predict futures prices, though again for my data the R2 is only 2%, and the results I will describe in my next post turn out to be insensitive to whether one includes this conditioning variable. Overall, I conclude that although these data do not appear to follow an exact martingale, that is really an excellent approximation to their behavior.

A separate question from whether changes in futures prices are possible to predict is the question of how far in advance they give a useful estimate. One standard of comparison is the mean squared error, or the average squared difference between the implied futures forecast at a given date and what the actual fed funds rate turns out to be. A benchmark for comparison is the assumption that the fed funds rate itself follows a martingale, so that one’-s forecast for the future value of the fed funds rate is always its current value. Such “-no-change”- forecasts have often proven to be very difficult to beat out-of-sample with financial data. The table below shows that, if you simply predicted that the fed funds rate isn’-t going to change, you’-d have a mean squared error of 389 basis points (that is, a standard deviation of about 20 basis points or 0.2%) predicting one month ahead and 2,522 basis points (50 basis-point standard deviation) predicting 3-months ahead. For comparison, the MSEs of the futures-derived forecasts are only a third as large.

Forecast horizon No-change
MSE
Futures
MSE
Percent MSE
improvement
Futures
MAE
1 month ahead 389 128 67% 6.90
2 months ahead 1248 392 69% 12.76
3 months ahead 2522 914 64% 20.03

Futures prices have become even better predictors over the last three years, with an incredible 97% improvement over the “-no-change”- forecast:

Forecast horizon No-change
MSE
Futures
MSE
Percent MSE
improvement
Futures
MAE
1 month ahead 183 5 97% 1.50
2 months ahead 665 19 97% 3.18
3 months ahead 1484 48 97% 5.40

The moral is, if you think the fed funds rate is going to do something over the next few months that differs from what is predicted by the futures prices, then think again.

And what the futures prices say right now is, no change in December.

MIT Center for Collective Intelligence – Play-money prediction exchange

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Yesterday, I blogged about the MIT CCI’-s collective book project, “-We Are Smarter Than Me“-, which will be presented today at a live web cast (at lunch time, EST).

I completely overlooked that the MIT CCI is launching a play-money prediction exchange. The topics are CCI self-centric and thus totally uninteresting.

PREDICTION TOOL FAQs

What is a “-Prediction Tool”-?

The Prediction Tool on this site is based on the idea of prediction markets. “-Prediction markets are speculative markets created for the purpose of making predictions. Assets are created whose final cash value is tied to a particular event or parameter (e.g., Will there be at least 10,000 registered community members by March 31, 2007?). The current market prices can then be interpreted as predictions of the probability of the event or the expected value of the parameter. Other names for prediction markets include information markets, decision markets, idea futures, and virtual markets.”- (Source: Wikipedia)

OK I get it, sort of, but what does that mean?

We have made a set of predictions about the success of the “-We”- community. You get to buy and sell stock in these predictions based on how likely you think they are to come true. If the prediction turns out to be true, the stock will pay out $100 per share. If it turns out not to be true, the stock will pay out $0 per share.

The hope is that through trading stocks back and forth, the market value of the stocks will eventually closely match the probability of each event coming true.

My Question: Does anybody know which software/design the MIT CCI is using here?

The Answer (added October 25): Shared Insights runs the MIT CCI’-s play-money prediction exchange with the software provided by Consensus Point.