Category Archives: prediction markets

finance, prediction markets

Even the stock market doesn't know how to report prices

The debate over how to report prediction market prices may seem to arise only because so many of the markets have low liquidity. If prediction markets were more liquid, the logic goes, then it wouldn’t matter if observers reported the last trade price, the average of the last several prices, or the bid-ask spread: they’d all be roughly the same. Indeed, in the extreme case of an “infinite liquidity” automated market maker, they are all the same.

Lance encountered the problem when a few rogue trades caused the colors on our electoral markets map to swing in what would seem to be irrational ways, briefly painting California red for McCain, for example.

However yesterday proved that even one of the most traded stocks on one of the largest volume financial exchanges in the world can suffer from bizarre trading oddities that make reporting meaningful prices an exercise in ad hockery.

It seems that Google’s stock gyrated wildly near the close of trading in entirely inexplicable ways that seem impossible to rationalize, and all this despite enormous volume traded. From SeekingAlpha:

[Here are] the official [NASDAQ] datapoints: share volume of 12 million shares (that’s about $5 billion), more than double the normal amount; an intraday high of $489, and — most improbable of all — an intraday low of just 1 cent per share.

What I found most incredible is that NASDAQ actually rewrote history in response:

Sep 30, 2008 17:01:02 ET Pursuant to Rule 11890(b) NASDAQ, on its own motion, has determined to cancel all trades in security Google Inc Cl – A “GOOG” at or above $425.29 and at or below $400.52 that were executed in NASDAQ between 15:57:00 and 16:02:00 ET. In addition, NASDAQ will be adjusting the NASDAQ Official Closing Cross (NOCP) and all trades executed in the cross to $400.52. This decision cannot be appealed. MarketWatch has coordinated this decision to break trades with other UTP Exchanges. NASDAQ will be canceling trades on the participant’s behalf.

I had no idea that stock exchanges canceled trades “just because”. Barring system error, this seems just plain wrong — certainly worthy of serious outrage from traders. If someone agrees to trade at a wildly irrational price that’s their own problem and they should have to live with it.

Apparently it’s not only possible, but common. On the same day NASDAQ canceled trades in ROH deemed out of bounds:

Sep 30, 2008 17:14:37 ET Pursuant to Rule 11890(b) NASDAQ, on its own motion, has determined to cancel all trades in security Rohm and Haas Company (ROH) at or above $73.20 and at or below $68.93 that were executed in NASDAQ between 15:57:00 and 16:02:00 ET. This decision cannot be appealed.

Suddenly our hack fix to the electoral markets map* and the various controversial revisions at intrade and betfair [1, 2] don’t seem quite so crazarbitrary in comparison.

*We now report the last trade price only if it falls between the current bid-ask spread, otherwise we report the bid or ask price, whichever is closer to the last price. After all, if the last price falls outside the bid-ask boundaries, it no longer reflects current market sentiment.
advertising, insurance, prediction markets

Political ads: Insuring your message gets across. Literally.

1 Comment

Centrist Messenger How It Works SnippetHere’s a brilliant idea: Centrist Messenger let’s you buy political ads with a money-back guarantee. You pay only if your preferred candidate wins. If the other candidate wins, you get your money back.

Centrist Messenger backs the guarantee with contracts purchased from intrade, in the same way that Priceline backs its “Sunshine Guarantee” with contracts from WeatherBill. (So presumably fully insured ads cost about twice as much as uninsured ads.)

In addition, the ads you buy can’t be too partisan:

Centrist Messages can … make strong advocacy of a position and candidate. However, this advocacy cannot demonize the other side, focus solely on personality, or make false representations of the candidates’ positions.

I’ll add Centrist Messenger to WeatherBill, Priceline, Yoonew, and FirstDIBZ (was TicketReserve) as companies fashioning creative ways to package and sell “markets in uncertainty” in the US amid a challenging legal and regulatory landscape.

What other useful and/or fun ways can you imagine re-packaging gambles as either insurance or contingent goods? Here are some of my own brainstorms:

  • Buy a ticket to a sporting event whose cost is refunded if your team loses.
  • Buy a “streak ticket”: entitles you to a ticket to the next game as long as your team keeps winning. (Variant: “K-loss ticket” entitles you to tickets until your team loses K times.)
  • Buy a “playoff run ticket” which gives you tickets, flights, hotel, etc. for the duration of your team’s playoff run. In other words, as long as your team keeps winning, you keep getting tickets, hotel, and flight to the next game. You may be able to buy this at the beginning of the season cheaply since it’s worth nothing if your team does not make the playoffs.
  • Buy “price drop” insurance: If that precious electronic gadget you just bought (read: iPhone) drops in price within N days, get K times your money back.
politics, prediction markets, yahoo

New Yahoo! News election dashboard

Cross-posted on midasoracle.org

The Yahoo! News Political Dashboard has re-launched for the general election stretch run of the 2008 US Presidential election.

Yahoo! News political dashboard for the 2008 US general Presidential election

From the main map you can see the status of the election in every state according to either polls or Intrade prediction market odds. Hover your mouse over a state to see current numbers or click on a state to see historical trends. On the side, help you can see search trends, price blogs, story news, and demographic breakdowns at national and state levels.

You can also “create your own scenario” by picking who will win in every state. You can save and share your prediction and compare against markets, polls, history, or celebrities. More on ycorpblog.

In the markets view, states are colored either bright red or bright blue, regardless of how close the race is in that state. To see a visualization that blends colors to reflect the tightness of the race, see electoralmarkets.com.

Yahoo! News also offers a candidate badge that you can display on your blog declaring your choice. The badge features national-level polls, prediction markets, search buzz, and money raised.

economics, finance, insurance, prediction markets, probability

WeatherBill shows the way toward usable combinatorial prediction markets

8 Comments

WeatherBill let’s you construct an enormous variety of insurance contracts related to weather. For example, the screenshot embedded below shows how I might have insured my vacation at the New Jersey shore:

Read this document on Scribd: WeatherBill Example Contract

For $42.62 I could have arranged to be paid $100 per day of rain during my vacation.

(I didn’t actually purchase this mainly because the US government insists that I am a menace to myself and should not be allowed to enter into such a dangerous gamble — more on this later. And as Dan Reeves pointed out to me, it’s probably not rational to do for small sums.)

WeatherBill is an example of the evolution of financial exchanges as they embrace technology.

WeatherBill can be thought of as expressive insurance, a financial category no doubt poised for growth and a wonderful example of how computer science algorithms are finally supplanting the centuries-old exchange logic designed for humans (CombineNet is another great example).

WeatherBill can also be thought of as a combinatorial prediction market with an automated market maker, a viewpoint I’ll expand on now.

On WeatherBill, you piece together contracts by specifying a series of attributes: date range, place, type of weather, threshold temperature or participation level, minimum and maximum number of bad-weather days, etc. The user interface is extremely well done: a straightforward series of adaptive menu choices and text entry fields guide the customer through the selection process.

This flexibility quickly leads to a combinatorial explosion: given the choices on the site I’m sure the number of possible contracts you can construct runs into the millions.

Once you’ve defined when you want to be paid — according to whatever definition of bad weather makes sense for you or your business — you choose how much you want to be paid.

Finally, given all this information, WeatherBill quotes a price for your custom insurance contract, in effect the maximum amount you will lose if bad weather doesn’t materialize. Quotes are instantaneous — essentially WeatherBill is an automated market maker always willing to trade at some price on any of millions of contracts.

Side note: On WeatherBill, you control the magnitude of your bet by choosing how much you want to be paid. In a typical prediction market, you control magnitude by choosing how many shares to trade. In our own prediction market Yoopick, you control magnitude by choosing the maximum amount you are willing to lose. All three approaches are equivalent, and what’s best depends on context. I would argue that the WeatherBill and Yoopick approaches are simpler to understand, requiring less indirection. The WeatherBill approach seems most natural in an insurance context and the Yoopick approach in a gambling context.

How does the WeatherBill market maker determine prices? I don’t know the details, but their FAQ says that prices change “due to a number of factors, including WeatherBill forecast data, weather simulation, and recent Contract sales”. Certainly historical data plays an important role — in fact, with every price quote WeatherBill tells you what you would have been paid in years past. They allow contracts as few as four days into the future, so I imagine they incorporate current weather forecasts. And the FAQ implies that some form of market feedback occurs, raising prices on contract terms that are in high demand.

Interface is important. WeatherBill shows that a very complicated combinatorial market can be presented in a natural and intuitive way. Though greater expressiveness can mean greater complexity and confusion, Tuomas Sandholm is fond of pointing out that, when done right, expressiveness actually simplifies things by allowing users to speak in terms they are familiar with. WeatherBill — and to an extent Yoopick IMHO — are examples of this somewhat counterintuitive principle at work.

There is another quote from WeatherBill’s FAQ that alludes to an even higher degree of combinatorics coming soon:

Currently you can only price contracts based on one weather measurement. We’re working on making it possible to use more than one measurement, and hope to make it available soon.

If so, I can imagine the number of possible insurance contracts quickly growing into the billions or more with prices hinging on interdependencies among weather events.

Finally, back to the US government treating me like a child. It turns out that only a very limited set of people can buy contracts on WeatherBill, mainly businesses and multi-millionaires who aren’t speculators. In fact, the rules of who can play are a convoluted jumble that I believe are based on regulations from the US Commodity Futures Trading Commission.

Luckily, WeatherBill provides a nice “choose your own adventure” style navigation flow to determine whether you are allowed to participate. Most people will quickly find they are not eligible. (I don’t officially endorse the CYOA standard of re-starting over and over again until you pass.)

Even if red tape locks the average consumer out of direct access, clever companies are stepping in to mediate. In a nice intro piece on WeatherBill, Newsweek mentions that Priceline used WeatherBill to back a “Sunshine Guaranteed” promotion offering refunds to customers whose trips were rained out.

Can you think of other end-arounds to bring WeatherBill functionality to the masses? What other forms of expressive insurance would you like to see?

gambling, prediction markets, yahoo

Predict Olympic medal counts on Yoopick

2 Comments

We just added a new feature to Yoopick designed especially for Frenchmen Chris and Emile and citizens of nineteen other countries to place their swagor* on how many Olympic medals they think their country will win.

We’ve argued that the Yoopick interface is useful for predicting almost any kind of number, and since medal count is indeed a number, we thought we’d give it a try.

Besides, Lance told us it would be a good idea.

Sign up, play, enjoy, and don’t forget to tell us what you think!

Thanks,
Sharad Goel
David Pennock
Dan Reeves

* Scientific wild-ass guess, on record

Yoopick: Olympic medal count: Select

Yoopick: Olympics medal count: France: Make pick

prediction markets

Some recent news and notes on prediction markets

2 Comments

A collection of (relatively) recent yellow bricks on the road to widespread use of prediction markets:

Plus a Murphy’s Law Alert:

Mr. Murphy may be hard at work orchestrating one of his signature ironies. Picture this: Prediction market proponents (including me) aren’t careful and get what they wish for: The CFTC takes prediction markets under its regulatory purview. Then, efforts to legalize Internet gambling in the US succeed, opening up an enormous and fabulously lucrative business that the “socially good” prediction market operators are legislated out of, mired instead in a separate regulatory goop of their own making.

games, prediction markets, research, yahoo

Yoopick: A sports prediction contest on Facebook with a research twist

19 Comments

I’m happy to announce the public beta launch of Yoopick, a sports prediction contest with a twist.

You pick any range you think the score difference or point spread of the game will fall into, for example you might pick Pittsburgh wins by between 2 and 11 points.

Yoopick make your pick slider interface screenshot

The more your prediction is viewed as unlikely by others, and the more you’re willing to stake on your prediction, the more you stand to gain. Of course it’s all for fun: you win and lose bragging rights only.

You can play with and against your friends on Facebook.

You can settle a pick even before the game is over, much like selling a stock in the stock market. Depending on what other players have done in the interim, you may be left with a gain or loss. You gain if you were one of the first to pick a popular outcome.

If you run out of credit, you can “work off your debt” by helping to digitize old books via the recaptcha project.

Those are the highlights if you want to go play the game. If you’re interested in more details, read on…

Motivation, Design, and Research Goals

There are a great many sources of sports predictions, including expert communities, statistical number crunchers, bookmakers, and betting exchanges. Many of these sources are highly accurate, however they typically focus on predicting the outright or spread-adjusted winner of the game. Our goal is to obtain more information about the final score, including the relative likelihood of each point spread. For example, if our system is working, on average there should be more weight put on point spreads of 3 and 7 in NFL games than on 2,4,6, or 8.

We chose sports as a test domain to tap into the avid fan base and the armies of arm chair (and Aeron chair) prognosticators out there. However, the same approach should translate well to any situation where you’d like to predict a number, for example, the vote share of a politician or the volume of sales of your company’s widget. In addition to giving you the expected value of the number, our approach gives you the confidence or variance of the prediction — in fact, it gives you the entire probability distribution, or the likelihood of every possible value of the number.

Underneath the hood, Yoopick is a type of combinatorial prediction market where the possible outcomes are the values of the point spread, and each pick is a purchase of a bundle of outcomes in a given interval. We use Hanson’s logarithmic market scoring rules market maker to price the picks — that is, to set the risk/reward ratio. This pricing mechanism also determines the gain or loss when picks are settled early.

Wins and losses on Yoopick are measured in milliyootles, a social currency useful for expressing thanks.

Our market maker can — and we expect will — lose yootles on average. Stated another way, we expect players as a whole to gain on average. At the same time, we actively work to improve our market maker to limit its losses to control inflation in the game.

Because the outcomes of a game are tied together in a unified market, picks in one region automatically affect the price of picks in other regions in a logically consistent way. Players have considerable flexibility in how and what information they can inject into the market. In particular, players can replicate the standard picks like outright winner and spread-adjusted winner if they want, or they can go beyond to pick any interval of the point spread. No matter the form of the pick, all the information flows into a single market that aggregates everything in a unified prediction. In contrast, at venues from Wall Street to Churchill Downs to High Street to Las Vegas Boulevard, markets with many outcomes are usually split into independent one-dimensional markets.

Our goal is to test whether our market design is indeed able to elicit more information than traditional methods. We hope you have fun playing in our Petri dish.

Sharad Goel
David Pennock
Daniel Reeves
Prasenjit Sarkar
Cong Yu

economics, events, prediction markets, research

Call for Papers and Participation: Workshop on Prediction Markets: Chicago, July 9 2008

I am happy to announce the following prediction market workshop and solicit submissions and participants.


=======================================================================
Call for Contributions and Participation

Third Workshop on Prediction Markets

http://betforgood.com/events/pm2008/index.html

Afternoon of July 9, 2008
Chicago, Illinois

In conjunction with the
ACM Conference on Electronic Commerce (EC’08)

SUBMISSIONS DUE May 23, 2008
=======================================================================

We solicit research contributions, system demonstrations, and
participants for the Third Workshop on Prediction Markets, to be held
in conjunction with the Ninth ACM Conference on Electronic Commerce
(EC’08). The workshop will bring together researchers and
practitioners from a variety of relevant fields, including economics,
finance, computer science, and statistics, in both academia and
industry, to discuss the state of the art today, and the challenges
and prospects for tomorrow in the field of prediction markets.

A prediction market is a financial market designed to elicit a
forecast. For example, suppose a policymaker seeks a forecast of the
likelihood of an avian flu outbreak in 2009. She may float a security
paying $1 if and only if an outbreak actually occurs in 2008, hoping
to attract traders willing to speculate on the outcome. With
sufficient liquidity, traders will converge to a consensus price
reflecting their collective information about the value of the
security, which in this case directly corresponds to the probability
of outbreak. Empirically, prediction markets often yield better
forecasts than other methods across a diverse array of settings.

The past decade has seen a healthy growth in the field, including a
sharp rise in publications and events, and the creation of the Journal
of Prediction Markets. Academic work includes mechanism design,
experimental (laboratory) studies, field studies, and empirical
analyses. In industry, several companies including Eli Lilly, Corning,
HP, Microsoft, and Google have piloted internal prediction
markets. Other companies, including ConsensusPoint, InklingMarkets,
InTrade, and NewsFutures, base their business on providing public
prediction markets, prediction market software solutions, or
consulting services. The growth of the field is reflected and fueled
by a wave of popular press articles and books on the topic, most
prominently Surowiecki’s “The Wisdom of Crowds”.

Workshop topics
===============

The area of prediction markets faces challenges regarding how best
to design, deploy, analyze, implement, and understand prediction
markets. One important research direction is designing mechanisms for
prediction markets, especially for events with a combinatorial outcome
space. Another notable issue is manipulation in prediction
markets. Understanding the effect of manipulation is especially
important for prediction markets to find their way to assist
individuals and organizations in making critical decisions. Moreover,
how to implement market mechanisms that not only are easy to use but
also facilitate information aggregation has been an important problem
for practitioners. Prediction markets face social and political
obstacles including antigambling laws and moral and ethical concerns,
both real and constructed.

Submissions of abstracts for research contributions from a rich set
of empirical, experimental, and theoretical perspectives are
invited. Topics of interest at the workshop include, but are not
limited to:

* Mechanism design
* Game-theoretic analysis of mechanisms, behaviors, and dynamics
* Decision markets
* Combinatorial prediction markets
* Market makers for prediction markets
* Manipulation and prediction markets
* Order matching algorithms
* Computational issues of prediction markets
* Liquidity and thin markets
* Laboratory experiments
* Empirical analysis
* Prediction market modeling
* Industry and field experience
* Simulations
* Policy applications and implications
* Internal corporate applications
* Legal and ethical issues

Submissions of summaries for demonstrations on prediction market
systems are invited. Systems of interest at the workshop include, but
are not limited to:

* Implemented combinatorial prediction markets
* Mature systems and commercial products of market mechanisms
* Research prototypes on prediction markets
* Other collective prediction systems

Submission instructions
=======================

Research contributions should report new (unpublished) research
results or ongoing research. We request an abstract not exceeding one
page for every research contribution.

For system demonstrations, a summary of up to two pages including
technical content to be demonstrated is requested. Please indicate if
the demonstration requires network access.

Research contributions and system demonstrations should be submitted
electronically to the organizing committee at pm2008@umich.edu no
later than midnight Hawaii time May 23, 2008.

At least one author of each accepted research contribution and
system demonstration will be expected to attend and present or
demonstrate their work at the workshop.

Important dates
===============

May 23, 2008: Submissions due midnight Hawaii Time

May 30, 2008: Notification of accepted research contributions and
system demonstrations

July 9, 2008: Workshop date

Organizing committee
====================

Yiling Chen, Yahoo! Inc
David Pennock, Yahoo! Inc
Rahul Sami, University of Michigan
Adam Siegel, Inkling Markets

More information
================

For more information or questions, visit the workshop website:
http://betforgood.com/events/pm2008/index.html

or email the organizing committee: pm2008@umich.edu

commentary, prediction markets

A historic MayDay: The US government’s call for help on regulating prediction markets

6 Comments

May 1, 2008 could signal a turning point for the prediction markets industry.*

Yesterday, the US Commodity Futures Trading Commission (CFTC) issued a request for public comments as they mull over the legal and regulatory status of prediction markets.

I read the Concept Release in detail, and I am happy to report that it is a careful, thoughtful, even scholarly document that reflects a solid understanding of the goals of prediction markets, and that appears to signal a real willingness on the part of the CFTC to consider reasonable options and arguments.

In short, this development leaves the optimist in me dreaming of a day in the not so distant future when US companies can try out some truly innovative products.

It’s not often that an industry in its infancy cries out for more government oversight. But the CFTC is certainly preferable to the gambling Gestapo.

Anyone who desires to see more prediction markets in the US, please let the CFTC know what you think!

*Or not.
commentary, prediction markets, probability

Reporting prediction market prices

3 Comments

Reuters recently ran a story on political prediction markets, quoting prices from intrade and IEM. (Apparently the story was buzzed up to the Yahoo! homepage and made the Drudge Report.)

The reporter phrased prices in terms of the candidates’ percent chance of winning:

Traders … gave Democratic front-runner Barack Obama an 86 percent chance of being the Democratic presidential nominee, versus a 12.8 percent for Clinton…

…traders were betting the Democratic nominee would ultimately become president. They gave the Democrat a 59.1 percent chance of winning, versus a 48.8 percent chance for the Republican.

The latter numbers imply an embarrassingly incoherent market, giving the Democrats and Republicans together a 107.9% chance of winning. This is almost certainly the result of a typo, since the Republican candidate on intrade has not been much above 40 since mid 2007.

Still, typos aside, we know that the last-trade prices of candidates on intrade and IEM often don’t sum to exactly 100. So how should journalists report prediction market prices?

Byrne Hobart suggests they should stick to something strictly factual like "For $4.00, an investor could purchase a contract which would yield $10.00" if the Republican wins.

I disagree. I believe that phrasing prices as probabilities is desirable. The general public understands “percent chance” without further explanation, and interpreting prices in this way directly aligns with the prediction market industry’s message.

When converting prices to probabilities, is a journalist obligated to normalize them so they sum to 100? Should journalists report last-trade prices or bid-ask spreads or something else?

My inclination is that bid-ask spreads are better. Something like "traders gave the Democrats between a 22 and 30 percent chance of winning the state of Arkansas". These will rarely be inconsistent (otherwise arbitrage is sitting on the table) and the phrasing is still relatively easy to understand.

Avoiding this (admittedly nitpicky) dilemma is another advantage of automated market makers like Hanson’s. The market maker’s prices always sum to exactly 100, and the bid, ask, and last-trade prices are one and the same. Auction-type mechanisms like intrade’s can also be designed better so that prices are automatically kept consistent.