Insider Trading + Prediction Markets

No Gravatar

InTrade&#8217-s John Delaney interviewed by Freakonomics:

Q. How does Intrade deal with insider trading?

A. Insider trading is one of the wicked problems, perhaps. Intrade is about providing the best predictive information. If insiders have information, then getting that information reflected in the market increases the quality of the information. I know this is not the conventional view concerning insider trading, and I am not arguing wholesale adoption or acceptance of insider trading. But we all know that, in the real world, insiders trade on inside information. We have even had markets on insider trading. Our view is to get the best information available into the market while we make sure there is some fair protection for outsiders.

I will have a third blog post on this John Delaney interview, later on, where I will make some comments.

In virtuellen Borsen wetten Web-Nutzer auf den Erfolg von Filmen, Romanen und Computerspielen. Immer mehr Unternehmen nutzen diese Prognosen, die Branche boomt. Zurecht, denn Wissenschaftler bestatigen: Niemand sagt Erfolg in Hollywood so gut voraus wie die Borsen-Spieler im Internet.

No Gravatar

Via Greek Extraordinaire George Tziralis, Der Spiegel on prediction markets (Internet-Orakel sieht Hollywoods Blockbuster voraus). Here&#8217-s a translation of the highlights (provided by George):

title: Internet Oracle foresee Hollywood Blockbusters
abstract: In virtual exchanges web users bet on the success of films, novels nd computer games. The number of applications that use such prognoses grow, the branch booms. Indeed, the scientists confirm: Nobody can make forecasts in Hollywood success as good as market games in internet.

secondary titles:
Always new prediction markets
More reliable than experts
professor Spann: The market weights the experts&#8217- opinions and attracts through incentives new knowledge
Good Forecasts in elections, Sport, Films
Justin Wolfers: Such prediction markets bring normally better predictions than experts, when election, sport results or economic development forecasts are concerned.
Base market study: Strong concept with distinctive blind marks
New concept: Idea markets
Companies test internal markets

Here&#8217-s Justin Wolfers:

Es gibt viele andere Erfolgs-Beispiele. Justin Wolfers, Wirtschaftswissenschaftler an der University of Pennsylvania, der seit Jahren Internet-Prognoseborsen erforscht, fasst seine Ergebnisse gegenuber SPIEGEL ONLINE so zusammen: &#8220-Solche Prognose-Markte bringen in der Regel bessere Prognosen als Experten hervor, wenn es um Wahlprognosen, Sportereignisse oder Vorhersagen zur Wirtschaftsentwicklung geht.&#8221-

They call that &#8220-prognosis markets&#8221-, it seems. (Or &#8220-prognostic markets&#8221-, maybe.)

Previous blog posts by Chris F. Masse:

  • Psstt… Spot that comment, on Google News, about… “bellwethers”… from a political scientist.
  • INSIDER’s STORY: The insightful strategic business report about The Evil Empire that Henry Berg does not want you to see
  • Prediction markets are about lowering transaction costs. That’s how sports come in.
  • The birth certificate of the next president of the United States of America –maybe
  • The marketing association between BetFair and TOTE Tasmania works better than expected.
  • The term “event markets” sucks —and the uncritical thinkers using this crappy term suck too.
  • CLIMBING HIS WAY TO THE TOP: Erik Snowberg is now Assistant Professor of Economics and Political Science at California Institute of Technology.

ProTrade co-founder Jeff Ma gives inklings.

No Gravatar

ProTrade&#8217-s Jeff Ma:

[…] The book Moneyball was a great inspiration for starting PROTRADE. Mike Kerns (who is the real brains behind the PROTRADE idea) and I both read Moneyball and felt it changed our lives. Conceptually PROTRADE began as a way for players to trade athlete like they were stocks. The problem with that concept is it poses the question asked above: what’s the underlying value of the athlete? We launched with a very complicated “earnings” system that took into account how much a player helped his team win (the ultimate value of a player according to Moneyball) but had to abandon that due to its complicated nature for sports fans. Instead we shifted to an “earnings” system based on traditional statistics that everyone follows. So what our market is now is a predictive market based on one year “earnings” for athletes and teams. These “earnings” are based on traditional stats that fans follow already like home runs, or touchdowns or wins. As for the larger questions that people have posed above, I wonder if sports fans really know less about athletes than they do about publicly traded companies. If you compare the amount of information I know about Tiger Woods, Barry Bonds, Tom Brady to that of Larry Page, Meg Whitman or Bill Gates, I’d have to say I know a lot more about the former group but maybe that’s because I co-founded a [company] based in sports. Finally I challenge the notion that our sports financial instrument has less underlying value than say a weather derivative. Futures and commodities are often simply “bets” about whether an instrument is over or under valued by the market.

Previous: ProTrade = The Jock Exchange, the first public stock market that trades in professional athletes??.

FT Predict = play-money InTrade by the Financial Times

No Gravatar

FT Predict – (ftpredict.com)

About FT Predict ™:

FT Predict™ is more than just a game. Predictive [*] markets collect the wisdom of the crowd* in a single dynamic price unit that can be far more sensitive to changes in the market than standard survey-based research. And a growing number of today&#8217-s leading companies embrace predictive market models in order to harness the wisdom of their own crowds to improve decision-making. Still, the fun part is playing the game and keeping up on what&#8217-s important to you at the same time. And if you like to play, we&#8217-ll keep playing.
We hope FT Predict interests you. Tell us what you think!
Looking for more information on Predictive Markets? Wikipedia&#8217-s entry is a good place to start.
* James Surroweicki&#8217-s book, The Wisdom of Crowds, offers insights into the dynamics behind predictive markets and why they work&#8230-and why sometimes they don&#8217-t.

[*] not &#8220-predictive markets&#8221-: prediction markets, please. (ALL markets are predictive.)

#1. Why not the full range of play-money contracts organized by TradeSports-InTrade (including the sports)?

#2. InTrade should unveil the purpose of its automated market maker. Here&#8217-s why. If there&#8217-s a linkage between the real-money contract prices and the play-money contract &#8220-prices&#8221-, then it might not be fun to trade on FT Predict with the hope of using the real-money InTrade prediction markets as advanced indicators. If the machine gives the right prices right away, then what would be the motivation to correct the prices?

Any comment, folks?

Addendum: What about the deal between Reuters and InTrade, as alleged by Deep Throat? Is that still on the front burner?

Addendum #2: I got Will Speck (FT&#8217-s R&amp-D director for the Americas) on the phone real quick&#8230-

a) FT Predict is a play-money prediction exchange (betting exchange) that will reward its best traders. Time period of the contest: April to June 2007. Current top prize is a cruise trip for two persons.

b) The aim of The Financial Times is really to go global with this play-money prediction exchange (betting exchange). Currently, FT Predict is only for US residents, over 21, and physically present in the U.S. They will propose the game to other countries later on.

c) There&#8217-s indeed a link between FT Predict and the real-money Intrade prices, in the form of an automated market maker. Its purpose is only to start off the exchange- the aim is to have 100% organic volume as soon as possible.

Addendum #3: Other info I found on the Official Rules page&#8230-

[…] You will receive an account with a starting balance of 10,000 FT$ to use to make trades. […] You may choose as many or as few of the approximately 250 contracts to trade each week using the available FT$ in your account. To simulate actual futures trading, a transaction fee of 0.05 FT$ per share will be charged for each trade you make. […]

To &#8220-simulate&#8221-??? To get play-money traders accustomed to the configurations of a real-money prediction exchange, rather. The idea here, maybe, is to offer a free version of a betting exchange in the hope of converting prospects into real-money traders, later on.

Addendum #4: One last thought. FT Predict gives weight to CDA in its battle against MSR.

Robin Hanson on the Sim Exchange

Robin Hanson on the Sim Exchange:

Er, it sure sounds like they dona€™t enforce any connection at all at any date between the game mentioned by the asset and anything related to sales of that game. If there are not enough traders of good will to enforce such a connection, then with learning the connection will probably be lost.

Previous: The structure of simExchange game stocks

Previous: An invitation to join the simExchange beta + Since November 9, 2006, the Sim Exchange has attracted over 2,400 registered players. + Sim Exchange &#8211- How to earn additional money? + The Sim Exchange: Basic Trading vs. Advanced Trading + BetFair, Sim Exchange = Vertical Prediction Exchanges, First

simExchange a Keynesian Beauty Contest

There&#8217-s an important difference between shares of ownership in real companies and these game shares. Shares of ownership in real companies have intrinsic value. Even for stocks that don&#8217-t pay dividends, shares of a real company represent ownership of the company&#8217-s assets. Thus, a stock&#8217-s price can&#8217-t fall too far below the company&#8217-s liquidation value because a smart trader could buyout the company and sell off its assets for more than the share price. Doing this makes money. I don&#8217-t think this property applies to the game shares since they don&#8217-t seem to be claims on anything but the ability to sell off the shares to someone else.

The simExchange seems like an excellent example of Keynes&#8217- beauty contest view of speculative markets. If there are naive traders who believe that shares have value based on actual game sales, then strategic traders will try to anticipate what naive traders will believe. Even though strategic traders know the shares have no intrinsic value (no dividends and no way to liquidate based on actual sales), they will trade to anticipate what naive traders will believe about sales. Thus, even though game shares have no intrinsic value (even in play money terms), as long as there is some level of belief that prices do correspond to sales, strategic traders will enforce this view.

I would be interested in a test of Shiau&#8217-s claim that &#8220-A stocka€™s price on the simExchange corresponds to the lifetime worldwide sales of a game, in which 1 DKP corresponds to 10,000 copies sold.&#8221- I could see this statement being basically correct if traders perceive that prices actually work this way and perceive that others perceive that prices actually work this way. Do the market makers try to enforce this connection? How do market makers on the exchange set their prices?

Previous: Robin Hanson on the Sim Exchage and The structure of simExchange game stocks

The structure of simExchange game stocks

No Gravatar

Brian Shiau (draft):

Contracts on most prediction markets are often binary contracts that pay depending on whether the event described by the contract occurs or does not occur. This structure is often referred as a binary option [1]. However, a prediction market is not restricted in solving yes or no questions. Contracts can be created to pay a scaling amount so that a prediction market can attempt to ascertain a quantity, such as how much a movie will gross in box office receipts.

Prediction markets have used contracts similar to futures to answer such questions. These contracts have some value that corresponds to a prediction and expire at a certain point, such as four weekends after a movie is released. At expiration, the contract holder cashes out the contract at the spot price (or the sum of box office receipts after four weekends) [2]. However, the problem with video games is that a game can continue to sell for years to come so any arbitrary expiration is not indicative of how well a game will sell. In creating a prediction market for video games, the simExchange required a structure that would accommodate the nature of the video game industry.

There are other quirks to the video game business. One particular problem that has been the ire of many analysts is the lack of comprehensive sales data [3]. Unlike Hollywood movies, video games do not have official sales figures every weekend. Instead, the industry relies on point-of-sale studies, surveys, and intelligent extrapolations from companies like NPD to get an estimate of how many copies a game has sold. The number of copies a game has sold will vary from source to source, although NPD is considered the standard by many in North America as it is the most comprehensive for North American sales.

Given these two problems, video games can continue to sell for years and the lack of data with absolute truth, the simExchange could not easily adopt the structure of most prediction markets already in existence. Instead, it sought a time-tested structure that has been used to answer a similarly mirky question: how much is a company that may last for decades really worth?

No one knows with absolute certainty how much a company is actually worth. That is one reason why the stock market exists for people to trade shares of a company. The stock market aggregates the information of all the traders to hopefully ascertain an accurate valuation for the company (this concept is known as the Efficient Market Hypothesis [4]). Due to the similarity of the issues, stocks on the simExchange function very similarly.

A stock&#8217-s price on the simExchange corresponds to the lifetime worldwide sales of a game, in which 1 DKP corresponds to 10,000 copies sold. These stock prices will climb or fall with monthly sales reports, just like a company&#8217-s stock price will climb or fall with quarterly earnings reports. A stock on the simExchange will also increase or decrease as a result of news on the product, just as a company&#8217-s stock will increase or decrease as a result of news on their products. If people believe a stock is underpriced given the data, people will bid it up and vice versa [5]. There is no automated function by the New York Stock Exchange to cash out a stock and pay shareholders a lump sum of cash depending on how the quarterly earnings for the company fared.

Eventually, a game will stop selling, just like eventually a company will stop growing. In this case the stock price will merely stagnate. Investors of game stocks can cash out just like they would with company stocks by selling their shares (or covering if they are short the stocks). The simExchange market makers will supply the liquidity to close those positions.

Due to this structure, in an efficient state where a diverse pool of traders are participating in the simExchange, game stock prices should become a strong predictor of the lifetime worldwide sales of video game titles [5].

References:
[1] Wolfers, Justin &amp- Zitzewitz, Eric. &#8220-Prediction Markets in Theory and Practice.&#8221- March 2006. (PDF)
[2] Hollywood Stock Exchange Frequently Asked Questions.
[3] Electronic Gaming Business, October 6, 2004.
[4] Shleifer, Andrei. Inefficient Markets: An Introduction to Behavioral Finance. New York: Oxford University Press, Inc. 2000.
[5] Chen, Kay-Yut &amp- Plott, Charles R. &#8220-Information Aggregation Mechanisms: Concept, Design, and Implementation for a Sales Forecasting Problem.&#8221- Hewlett Packard Laboratories and California Institute of Technology. March 2002.

Originally published on the Sim Exchange website. Republished on Midas Oracle .ORG with Brian Shiau&#8217-s permission. ((( Appreciated. :) )))

Related: Keith Jacks Gamble: simExchange is somewhat OK, but will remained confined in play-money land. + Brian Shiau: The Sim Exchange Works Fine, Thanks. + Robin Hanson on the Sim Exchage + simExchange a Keynesian Beauty Contest + The structure of simExchange game stocks + An invitation to join the simExchange beta + Since November 9, 2006, the Sim Exchange has attracted over 2,400 registered players. + Sim Exchange – How to earn additional money? + The Sim Exchange: Basic Trading vs. Advanced Trading + BetFair, Sim Exchange = Vertical Prediction Exchanges, First

Trading on Markets vs. WeatherBill, Market Scoring Rule, BetFair Multiples

No Gravatar

I will try to be clearer.

PROBLEM: Other than on popular prediction markets at TradeSports-InTrade and BetFair, liquidity is a problem.

HANSON&#8217-S SOLUTION [*]: Market Scoring Rule. (See it in action: I’m betting the farm on AL Gore for the Oscars.) Cool, but not really customizable. In my example, I had the choice between three options, only. Quite poor.

WEATHER BILL SOLUTION: Forget trading on markets, forget automated market makers, forget mixing betting and trading (MSR), forget all this &#8212-impossible to go retail with these mechanisms. Weather Bill = a sophisticated way to take client orders, very precisely. Customers get what they really want. Weather Bill offers a highly customized hedging service. (Instead of going for re-insurance money, they pass the risks to some hedge funds via a CFTC-acknowledged EBOT, but that&#8217-s a back-office detail.)

HEDGESTREET CASE: HedgeStreet is a hedging/speculating service. (Note that they are regulated by the CFTC, not just acknowledged.) Let&#8217-s do a thought experiment. HedgeStreet (just like BetFair did recently) becomes a bookmaker, in addition to being an exchange. They adopt a WeatherBill-like user interface to take very precise client orders &#8212-as opposed to asking traders to fill in ask-and-bid orders and making use of an automated market makers (as HedgeStreet did, if I am correct). Of course, you would have two different user interfaces: one for the speculators and one for the hedgers. Then, do you see an algorithm/mechanism that would balance the two? And whom would they pass the risk to (if any)? And is there a way to have a mix exchange&#8211-bookmaker business model while satisfying the CFTC at the same time?

&#8212-

My point is that there is room for innovation, out there. BetFair has shown that you can go retail &#8212-providing that you cater to a population of sophisticated bettors. The fact that they now offer multiples show that the betting exchange business model has some limits. Sticking with it like a fundamentalist prevents you to servicing fully your customers.

As always, innovation is the key to customer satisfaction &#8212-and to profitability, mister Jason Ruspini.

Addendum: [*] Robin Hanson actually devised two versions of Market Scoring Rule. His combinatorial version might render this blog post totally pointless. Alas, no prediction exchange (betting exchange) is using his combinatorial Market Scoring Rule.

Predictocracy: Market Mechanisms for Public and Private Decisionmaking – THE MARKET WEB

Predictocracy: Market Mechanisms for Public and Private Decisionmaking &#8211- by Michael Abramowicz &#8211- 2007-xx-xx &#8211- (fall)

Chapter: The Market Web &#8211- (towards the end of the book)

&#8212-

Michael Abramowicz:

If prediction markets should become commonplace, decisionmakers might link to them in their own analyses.

Will trading play-money and/or real-money event derivative contracts become commonplace? It&#8217-s likely, at the contrary, that trading will remain an elite occupation and that prediction markets with appropriate liquidity will remain scarce. Unless Google, Yahoo! (with Yootopia) and/or MicroSoft has/have a secret plan to popularize betting exchanges &#8212-which could well be since Bo Cowgill, David Pennock and Todd Proebsting are ambitious guys.

&#8212-

Michael Abramowicz:

For example, suppose that a corporation is deciding whether to build a new factory in a particular area. That decision might depend on variables like future interest rates and geographic patterns. And so, a decisionmaker might build a spreadsheet containing live links to prediction markets assessing these issues.

Interest rate prediction markets would help, for sure. As for geographic forecasting, maybe non-trading mechanisms could help &#8212-for real estate, I&#8217-m thinking of Zillow, or some improved mechanisms derived on Zillow.

&#8212-

Michael Abramowicz:

The Market Web

If prediction markets should become commonplace, decisionmakers might link to them in their own analyses. For example, suppose that a corporation is deciding whether to build a new factory in a particular area. That decision might depend on variables like future interest rates and geographic patterns. And so, a decisionmaker might build a spreadsheet containing live links to prediction markets assessing these issues. That way, as the market predictions change, the spreadsheet&#8217-s bottom line would change as well. Predictions in many prediction markets may be interrelated, and so market participants in one prediction market will often have incentives to take into account developments in other prediction markets. Prediction markets thus can affect one another indirectly, as participants in one update their models based on developments in another.

Sometimes, however, it might be desirable to construct links among prediction markets so that changes in one automatically lead to changes in another. Consider, for example, the possibility of a market-based alternative to class action litigation. In Chapter 8, each adjudicated case represented a separate prediction market, but often there will be issues in common across cases. Many thousands of cases may depend in part on some common factual issues, as well as on some distinct issues. Legal issues also may be the same or different across cases. Someone who improves the analysis of any common factual or legal issue can thus profit on that only by changing predictions in a very large number of cases. A better system might allow someone to make a change across a single market and have that change propagate automatically to individual cases.

The critical step needed to facilitate creation of the market web is to allow a market participant to propose a mathematical formula to be used for some particular prediction market. Some of the variables in that formula could be references to other, sometimes new, prediction markets. For example, a market participant might propose in a market determining how much amages the plaintiff should receive a formula dependent on variables such as the probability that the plaintiff states a cause of action, the probability that the plaintiff was in fact injured, the probability given injury that the defendant caused the injury, the probability given a cause of action that the defendant is subject to strict liability, the probability given no strict liability that the defendant was negligent, and the damages that the plaintiff should be awarded if liability is proved. This formula, for example, presumably would allow for no damages where the plaintiff probably does not state a cause of action. Each of the components of this formula might be assessed with a separate prediction market. We can easily build the market web by combining three existing tools. The first tool is a text-authoring market. The relevant text would be the formula itself, including specifications of other prediction markets that would be used to calculate specific variables. As with any text-authoring market, a timing market would determine when a proposal to change the text should be resolved. Other markets might become live only once proposals to take them into account were approved. Ex post decisionmakers would assess the wisdom of these markets&#8217- recommendations in some fraction of cases to discipline the market&#8217-s functioning.

The second tool would be a simple normative prediction market corresponding to the text-authoring market. It might also be possible to have computer software that automatically parses the formula and consults various sources, but the market sponsor need not build this tool. Rather, ex post decisionmakers will assess the appropriate value for the normative prediction market based on the formula. An advantage of this approach is that it would make it easy to use complicated formulas, as well as formulas that depend in part on numbers from sources other than prediction markets, or from prediction markets of other types. In addition, this approach makes it easy to collapse a formula into a single prediction market, if that should prove desirable. The formula text simply would be changed to a description of the market to be created, such as &#8220-adjudication of plaintiff&#8217-s liability in a particular case.&#8221-

Finally, the third tool necessary is a mechanism for determining the market subsidy. A separate subsidy would be needed for the text-authoring market and the normative prediction market. Each of these subsidies could be determined by additional normative prediction markets, perhaps with fixed subsidies. The subsidy for the text-authoring market in turn would be distributed by the text-authoring market to individuals who have proposed particular amendments, and individuals who have participated in the assessment of particular amendments. The text-authoring market also could allocate a subsidy to the first individual who creates the market and proposes some text for it. When the text-authoring market produces a new formula reflecting additional prediction markets, the subsidy for the main prediction market would fall (since calculating a formula based on other prediction markets will often be relatively easy).

A single node in the market web would thus consist of a text-authoring market describing the node and providing a formula for calculating it, a normative prediction market, and a set of additional prediction markets for determining how to distribute a subsidy to the different components of the node. The nodes collectively create a web because the formulas link to other nodes- software, of course, could easily make these links clickable. At the same time, a mechanism is needed to determine what portion of the market subsidy each node should receive. A simple approach would be for a prediction market to be used for every link, to determine the portion of the subsidy for each node that should be allocated to each node linked to it. The total should add up to less than 1, leaving some portion of the subsidy for the node itself.

With these markets established, software could easily distribute a single subsidy for the market as a whole to market participants who have traded on individual nodes when the market closes. Market participants working on one portion of the web, meanwhile, would not have to assess the relative importance of one node to nodes that are only distantly related. It would also be straightforward to have a continuously open market, periodically collecting and distributing money in accordance with individual participants&#8217- success on the market.

This assumes that the market web would be arranged on a single server. It is possible, though, that a node on one market web might link to a node on another market web. If market sponsors allowed such links, it could promote competition among prediction market providers. It also partially answers one potential criticism of using prediction markets for decisionmaking, that a software engineer might hijack the government by faking some prediction market results. Market participants at least will have incentives to identify fake prediction markets and not link to them. In principle, it is possible to have government decisions based entirely on decentralized prediction markets. A caveat is that the government might want to subsidized individual market web providers, and it might use centralized prediction markets to accomplish that.

Whether or not the markets themselves are decentralized, they would allow market participants to make it easier to assess the basis for market predictions. Indeed, the market web is in some ways a substitute for deliberative prediction markets, because both provide means of helping observers understand the basis for the market&#8217-s predictions. An observer could look at any individual node of the market web and understand how it has been calculated, though inevitably there must be some &#8220-leaf&#8221- nodes that themselves do not contain any formulas. At the same time, software might allow an observer to find all of the nodes that link to a particular node. So a market participant addressing a factual issue relevant to many cases could link to all of the cases represented by that factual issue. As a particular issue becomes increasingly important, the subsidy for that node should rise, and market participants can profit on their analysis of the issues relevant to that node without worrying about details of individual cases.

[…]

Brainy stuff. I&#8217-ll mind this for a while. I&#8217-m sure that the Midas Oracle readers will find this idea original &#8212-and maybe, interesting.