Tag Archives: prediction markets

Niall OConnor, the one-data-point analyst

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Here is what Niall O&#8217-Connor has published on his site:

Prediction Markets Tombstone

Read the previous blog posts by Chris. F. Masse:

  • Davos – World Economic Forum
  • CME Group = Chicago Mercantile Exchange + Chicago Board Of Trade
  • Democratic and Republican caucuses in Nevada + Republican primary in South Carolina
  • The BetFair blog is not a serious publication.
  • MICHIGAN PRIMARY @ BETFAIR: Niall O’Connor asks the very pertinent question.
  • One thing John Delaney and his Irish employees at InTrade-TradeSports can learn from the BetFair-TradeFair folks at HammerSmith.
  • BetFair compound chart on the Michigan primary

Prediction Markets vs. Bookmakers – The Ultimate Argument

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No GravatarLas Vegas Sun:

“The bookie’s odds will be influenced by his appetite for risk, the action he’s got on his side and his own bias,” said John Delaney, chief executive officer of Dublin-based Intrade.com, the world’s largest prediction market. “If I were to ask you where you would find the expected value of IBM, would you ask a broker or go to the stock exchange? The aggregation of information that happens on an exchange typically provides better information than if you had several buyers and just one seller.”

Excellent.

Read the previous blog posts by Chris F. Masse:

  • BetFair-TradeFair (slightly) improve their blog, finally (it was about time) —and open 2 new sections: “prediction markets” and “financials”.
  • Control in Distributed Networks (Decisions 2.0: Distributed Decision-making)
  • What are enterprise prediction markets for?
  • BetFair sponsor medical doctor for jockeys.
  • Freakonomics @ Predictify
  • MyPronostic
  • Can you correctly forecast sales of music CDs?

Enterprise prediction markets will be the next big thing when…

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Tom Davenport:

  • Confidence that executives have valuable roles to play even if they don’t always have the right answer-
  • A high level of trust in the intelligence and capabilities of employees-
  • The willingness to follow numbers and analysis wherever they lead (as long as they are more-or-less consistent with common sense)-
  • A pretty strong analytical and financial orientation (since futures markets aren’t something that every Joe or Jane understands).

Read the previous blog posts by Chris. F. Masse:

  • We regret to inform you of the passing of BettingMarket.com.
  • Niall O’Connor, the one-data-point analyst
  • The best headline of the day –post Michigan
  • Enterprise prediction markets will be the next big thing when… hierarchies are flat.
  • Prediction Markets vs. Bookmakers — The Ultimate Argument
  • The Michigan primary as seen thru the prism of the InTrade prediction markets
  • BitGravity = video distribution network

Why collecting and synthesizing the dispersed available information?

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Sean Park (after a long, boring introduction to the subject):

[…] The ‘failure’ of New Hampshire was the result of primarily two factors:

  1. It wasn’t a failure. No market is always right. More importantly markets reflect the information available to and the interests of their participants. Basically markets are very efficient mechanisms (I would claim the most efficient) for processing information. No more, no less.
  2. In this particular instance, the probability of the market producing an erroneous forecast was high due to the lack of liquidity. This is a problem of all political markets in the US. Show me a market on the New Hampshire primaries with tens of thousands of participants and millions of dollars traded and I will show you a market that creates more valuable information. BUT it would still on occasion be ’surprised.’

Basically I guess what I’m trying to say is the expectations seem to be set all wrong by many inside the community. I think “prediction markets” – creating markets in information and outcomes is a wonderfully important and valuable thing to do. Equally however I think that anyone that represents such markets as being able to predict the future is a charlatan. What they can do is collect and synthesize powerfully and efficiently all the dispersed available information – using money as the relevance filter. This is very valuable in its own right and is defensible. Promoting prediction markets to true sceptics (ie mainstream American politicians) on the basis that they are a Delphic Oracle is surely a path to certain tears and ultimately is almost guaranteed to fail. [*]

Markets don’t compute unknown unknowns. That doesn’t mean they are useless, just that they have to be understood in context.

[*] How to promote the prediction markets, then? As information collecting tools? Who should use these tools, then? Experts or ignorants? Sean Park does not elaborate further. None of the questions I have asked are answered.

InTrade is no psychic -but what if that bit of truth is systematically said BEFORE, as opposed to AFTER.

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David Leonhart in his New York Times blog, last week:

The political prediction markets just went through their version of the dot-com bubble. […]

Intrade’s odds have had a very good forecasting record over the last few years, having correctly called every Senate race in 2006, every state in the 2004 presidential election and all but one state in the 2004 Senate races. The odds also correctly called New Hampshire for John McCain this week and now make him the favorite for the Republican nomination- he is given a 38 percent chance, while Rudolph W. Giuliani is given a 29 percent chance.

Intrade’s executives, as well as the academic researchers who study the site, are careful to point out that its contracts provide only odds, not certainties. An outcome that’s given a 20 percent chance of happening should happen 20 percent of the time — not never. […]

The question I asked yesterday was: What would happen if that warning label were to be sticked on InTrade before each election, as opposed to after each predictive debacle? My bet is that, if you suppress the mention of InTrade&#8217-s magical touch, the Irish real-money prediction markets will be far less appealing to people. They want magic. All of the sudden, InTrade is not a psychic anymore, but simply a forecasting tool of convenience for busy people who don&#8217-t want to check the polls in details. This issue is crucial if we want to be able to define what is the &#8220-prediction market approach&#8221- &#8212-as opposed to the &#8220-betting exchange approach&#8221-.

Give me one reason why the political analysts should follow the US primaries thru the prism of the InTrade prediction markets instead of thru the polls. [My question is still unanswered, you will notice. Which shows to you the embarrassment of the prediction market luminaries (or so they think they are).]

Once the true nature of the prediction markets appears more clearly, it becomes evident that they are not tools for the experts, but tools for the ignorants, rather. Which is great, provided that this is said clearly from the start.

Prediction Market Efficiency vs. Prediction Market Accuracy

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Panos Ipeirotis in a comment here:

[W]e should try to separate two things: Market efficiency and market accuracy. Efficiency is the rate in which the market incorporates new information and prevents any arbitrage opportunities. Accuracy is the probability in which the market predicts the correct outcome of an event. The main claim to fame for the [prediction] markets is that they self-report their accuracy, and that “the prices are probabilities”.

We can measure the effectiveness of the market by following the outline discussed above. One axis is the price of the contract at time t before the expiration of the contract and the other axis is the rate in which this event happens. (…60% of the cases the event that trades at 0.6 happens, 30% of the cases the event that trades at 0.3 happens, and so on…). A perfectly accurate market should have a straight line as an outcome when time t gets close to 0. Any deviation of the experimental results indicates an accuracy bias. There are many papers that indicate the favorite-longshot biases in the market (underprice the favorite, overprice the longshots) so there is no need to really repeat this here. An interesting thing is to see how big it can be and still have reasonable accuracy. Furthermore, if we have systematic and robust biases, then we can use a calibration function that will adjust the market prices, compensating for the biases, to reflect real-life probabilities.

Measuring efficiency is a trickier concept. The general definition of efficiency is that “the market immediately incorporates all available information”. Being able to predict price movements indicates inefficiency. Having prices for an event summing up to anything other than 1, indicates inefficiency. However, it is difficult to have a definite proof that the market is efficient. We can only say that “we were not able to spot inefficiencies”. It is very difficult to prove that “the market is efficient”.

The two metrics are, of course, highly connected close to the expiration of the contract. If the market is not efficient, then it will not be accurate, as it will not have had incorporated all the available information, if any material information becomes available just before the expiration of the contract.

Panos Ipeirotis

ROBIN HANSON TELLS THE TRUTH ON GOOGLES ENTERPRISE PREDICTION MARKETS.

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Robin Hanson:

Yes prediction markets are cool, Google is cool, and it is cool that Google had location data to show how location influences trading. But cool need not be useful. People are not asking the hard questions here: what value exactly is Google getting out of these markets, aside from helping them look cool?

Robin Hanson is a modern-day hero. Speaks the truth. Has a clear vision. Doesn&#8217-t mind to act as a contrarian, now and then. Like Winston Churchill. Is a real leader.

Related Links: Using Prediction Markets to Track Information Flows: Evidence From Google – (PDF file – PDF file) – by Bo Cowgill (Google economic analyst), Justin Wolfers (University of Pennsylvania) and Eric Zitzewitz (Dartmouth College)

Robin Hanson is not convinced by the Google experiment with enterprise prediction markets -to say the least.

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Robin Hanson in a comment on Marginal Revolution:

This is important work for organizational sociology, but not for prediction markets, as this does little to help us find and field high value markets.

Finally, somebody who speaks the truth.

See also the comment of economist Michael Giberson.

Related Links: Using Prediction Markets to Track Information Flows: Evidence From Google – (PDF file – PDF file) – by Bo Cowgill (Google economic analyst), Justin Wolfers (University of Pennsylvania) and Eric Zitzewitz (Dartmouth College)

Have Googles enterprise prediction markets been accurate?

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Justin Wolfers:

So we decided to move beyond asking, “Do prediction markets work?” and instead use them as a tool for better understanding how information flows within a (very cool) corporation.

I am more interested in the accuracy of the enterprise prediction markets than in corporate micro-geography issues.

Related Links: Using Prediction Markets to Track Information Flows: Evidence From Google – (PDF file – PDF file) – by Bo Cowgill (Google economic analyst), Justin Wolfers (University of Pennsylvania) and Eric Zitzewitz (Dartmouth College)

Can the prediction markets survive without the over-selling from John Delaney and his little fanboys?

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Emile Servan-Schreiber:

[…] The classic first line of defense in these cases is to remind people that market “predictions” are really just probabilities, so any one outcome cannot invalidate the approach. The argument is sound and backed up by loads of data. But it would of course be much more convincing if we, as an industry, would remember to show at least as much humility when our market “predictions” appear correct instead. If you’re going to spread the idea that your market called all 50 states in the last U.S. presidential election because each correct outcome was predicted with over 50% chance, then you can’t hide behind probabilities when an 80% prediction comes to naught, as in Obama’s NH collapse. […]

Emile Servan-Schreiber makes a good point &#8212-see also Panos Ipeirotis, in the same vein.

But the over-selling is the reason [*] why InTrade (and not NewsFutures) has managed to infiltrate so many US media. If you suppress the magical touch, then InTrade is just a forecasting tool of convenience &#8212-for those too busy to look at the polls.

Give me one reason why the political analysts should follow InTrade instead of the polls, then?

What is the true nature of the prediction markets? How to use the prediction markets? Who should use the prediction markets? For what benefits? Once you have the answer to these 4 questions, you can tackle the next two problematics: How to market the prediction markets without over-selling them. How to report news thru the prism of the prediction markets while respecting their true probabilistic nature.

Welcome to the version #2 of the prediction market industry. Quite a horse of another color, now.

&#8212-

[*] UPDATE: The over-selling aspect is the topping over the real-money and the liquidity dimensions. The over-selling aspect wraps all that.