by Daniel Edwards
Read the previous blog posts by Chris. F. Masse:
Odds that Hillary Clinton gets the 2008 Democratic nomination = 1.56 (digital odds taken at 9:15 AM EST)
To get the implied probability expressed in percentage:
BetFair-generated implied probability is not far away from InTrade’-s 62.1%.
Psstt…- This present post was prompted by Niall O’-Connor, who puts all his faith in the BetFair instant “-over-round”- —-which indeed doesn’-t add up to the virgin and perfect “-100%”- that Niall is seeking (like the Monthy Python were seeking the Holy Grail). Good luck for your quest, Niall.
Your mother was a hamster and your father smelt of elderberries!
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External Resource: Interpreting Prediction Market Prices as Probabilities – (PDF file) – by Justin Wolfers and Eric Zitzewitz
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NEXT: Implied Probability of an Outcome –-BetFair Edition
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Via Steve Roman, Michael Abramowicz.
Read the previous blog posts by Chris. F. Masse:
Justin Wolfers investigated the Rudy Giuliani free-fall thru the prism of the InTrade prediction markets in the Wall Street Journal (PDF file).
All told, prediction market data tend to confirm that Mr. Giuliani’-s recent decline is due to a poor campaign, rather than a poor strategy.
I have just read the 3-page New York Times story on Rudy Giuliani’-s “-dizzying free-fall”- (who has not yet dropped out and endorsed John McCain, as I write this). And, as I understand the two NYT writers, they attribute his failure to both a strategic mistake and a poor campaign.
As of today, I am not yet convinced that Justin Wolfers saw something in the prediction markets that the political analysts didn’-t see in the polls and in the votes. Thus, I will find other Giuliani reports. I’-ll see. What is needed is an independent judgment on this.
Read the previous blog posts by Chris. F. Masse:
It’-s for Rositsa Popcheva’-s thesis, “-Information Markets and Knowledge Management: An Empirical Study of the Advantages and Limitations in Organizational Settings.”-
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Read the previous blog posts by Chris. F. Masse:
Read the previous blog posts by Chris. F. Masse:
“-Does prediction market guru [= Chris Masse] understand probabilities?“-, asks our good friend Niall O’-Connor.
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Let’-s ask economics PhD Michael Giberson:
Yes, I think you are right. I just looked at your exchange with Niall and Niall’-s post, and haven’-t thought through just how the over-round may affect things.
But it seems okay to do it just the way you say, because the digital odds implies a precise numerical prediction and that prediction can be stated in the form of a probability. Call the calculated number an implied probability of the event, and then you don’-t have to worry that a complete group of related market prices don’-t add to 100 percent.
If a trader believes that event X should be trading at 70 percent and sees current digital odds of 1.56 at Betfair ( =>- 64.1 percent), he should buy (considering fees, etc.). If the digital odds move to 1.4 ( =>- 71.4 ) then sell or at least don’-t buy.
Niall may be hung up on using a pure concept of probability. The purity is not useful- your explanation is useful. You win.
(Feel free to quote from this email, should you wish.)
-Mike
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UPDATE: Michael Giberson precises his comment…-
Niall, I agree that Professor Sauer’-s presentation explains how to estimate true probabilities from odds that do not sum to one. I was taking Chris Masse to be explaining a related, but slightly different task: the conversion of the digital odds that Betfair quotes to an implied probability.
The point of my slightly snide comment concerning purity reflects the pragmatic view that a trader could use the method Chris describes to convert from digital odds to an implied probability (which may be easier for some traders to think with and trade on). A single quote of digital odds implies a particular probability estimate. Chris’-s math gets the trader from the one number to the other. (=useful to traders)
To get to the estimate of true probabilities, as you have explained, a trader must have a complete set of odds for all possible outcomes for an event. This additional information requirement would completely stymie a trader wishing to arrive at the true probability estimates in cases in which some of the data is unavailable. (= not as useful to traders)
Read the previous blog posts by Chris. F. Masse:
Preparing a Negotiated R&-D Portfolio with a Prediction Market – by Cedric Gaspoz and Yves Pigneur
http://www.hec.unil.ch/cgaspoz/files/pub/Gaspoz08a_Preparing_a_Negotiated_R_D_Portfolio_with_a_Prediction_Market.pdf
Multi-Criteria Decision-Making versus Prediction Markets – by Cedric Gaspoz and Yves Pigneur
http://www.hec.unil.ch/cgaspoz/files/pub/Gaspoz07c_Technology_Foresight_for_IT_Investment.pdf
Read the previous blog posts by Chris. F. Masse:
The Journal of Prediction Markets – Volume 1, Number 3, December 2007
Thanks to George Tziralis of AskMarkets
Read the previous blog posts by Chris. F. Masse:
Previous blog posts by Chris F. Masse: