Tag Archives: prediction markets

MBAs on Enterprise Prediction Markets

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Alan H.:

During the class, Adam Siegel, the founder of Inkling Markets, a prediction markets consulting firm, spoke about his experiences. Of the benefits, he said that prediction markets bring clarity around information, prevent political fudging and backstabbing regarding information. Nobody is the whistleblower for challenging optimistic assumptions, rather, &#8220-it&#8217-s the market.&#8221- […]

Given that many executives and managers want to hide their poor performance, I asked Adam about who typically approaches his firm. He responded that he is usually approached by either third parties who have no P&amp-L responsibility, such as strategic planning groups, or forward thinking managers who are sick of bad forecasts being submitted. […]

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Read Adam Siegel&#8217-s post about his intervention.

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Obstacles to Prediction Market Adoption

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BusinessWeek:

Harrah&#8217-s is setting up a pilot prediction market to forecast customer activity in one of its domestic casino operations. […]

Since the power of prediction markets hinges on effectively tapping into cognitive diversity throughout an organization, Page also argues convincingly that if members of a group do not have enough diversity in their perspectives, prediction markets can actually produce dismal results. […]

Until now, few of the companies sponsoring successful pilots or tests have deployed prediction markets on a broad or sustained basis. Why not? One explanation is that prediction markets are deeply subversive. After all, lots of midlevel executives are consumed with the task of forecasting. If prediction markets do a better job of it, doesn&#8217-t that discredit the efforts (and perhaps even the motives) of these executives? But as prediction markets shift their focus toward new knowledge creation, they may become less threatening within corporations. […]

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I don&#8217-t buy this explanation &#8212-nor do I buy that other one.

My view is that we haven&#8217-t yet demonstrated clearly when and how prediction markets can be useful.

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Robin Hanson has convinced Concensus Point to support combinatorial prediction markets.

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Robin Hanson:

I&#8217-ve developed a combinatorial betting tech that lets a few or many users edit an always-coherent joint probability distribution over all value combinations of some set of base variables. Far futures base variables might include the years of important tech milestones, population, wealth, or mortality values at particular future dates, etc. Each user edit would be backed by a bet, a bet invested in assets paying competitive interest/returns. This combo bet tech worked well in published lab tests, several firms have used it, and I&#8217-m now working with Consensus Point to deliver a robust commercial implementation. More on the tech here, here, and here.

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See the explainer from David Pennock, which we will link to, again, later on.

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HubDub CEO on Max Keisers The Oracle (BBC World News)

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Cory Doctorow likes Max Keiser&#8217-s TV show &#8212- I do too.

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  1. Although I don&#8217-t agree with them politically, Max Keiser is exceptionally charismatic and funny, and Stacy Herbert is very lively and competent.
  2. Max needs to invite a guest who is as lively and as literate in finance than he is. Otherwise, &#8220-The Oracle&#8221- will remain his show, as opposed to a good show.
  3. The TV format is a winner. Max is on a path to stardom.
  4. Nigel managed to plug his prediction exchange. Good.

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Prediction markets feed on facts and expertise.

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Via Yahoo! research scientist David Pennock of Odd Head and YooPick, the dear honorable Duncan Watts:

In part because of disappointing findings such as this, an increasingly popular substitute for expert opinions are so-called &#8220-prediction markets,&#8221- in which individuals buy and sell contracts on various outcomes, such as football game point spreads or presidential elections. The market prices for these contracts then effectively aggregate the knowledge and judgment of the many into a single prediction, which often turns out to be more accurate than all but the best individual guesses.

But even if these markets do perform better than experts, they don&#8217-t necessarily do a good enough job to rely on. Recently, my colleagues have started tracking the performance of one popular prediction market, at forecasting the outcome of weekly NFL games. So far, what they&#8217-re finding is that the market predictions are better than the simple rule of always betting on the home team, but only slightly so &#8212- which, oddly, is very similar to what Tetlock found regarding his experts. Some outcomes, in other words, and possibly the outcomes we care about the most, simply aren&#8217-t &#8220-predictable&#8221- in the way we would like.

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  1. Prediction markets are not &#8220-a substitute for expert opinions&#8221-. They are a substitute for the averaged probabilistic predictions of a large group of experts polled the traditional way (by phone or by e-mail). In prediction markets, traders (who are not experts, most of the times) collect and aggregate facts and expertise at a lower cost than a poll or survey of experts.
  2. In the research cited by Ducan Watts, the prediction markets are slightly more accurate than the competitive forecasting mechanism. Well, that&#8217-s something we are used to.
  3. What Ducan Watts doesn&#8217-t say is that prediction markets integrate facts and expertise faster than the group of experts polled by his researching colleagues &#8212-for the very crude reason that it takes a certain time to survey a group of experts (be it by e-mail or by phone).

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If I can count, that&#8217-s 3 reasons why prediction markets can bring in business value:

  1. lower cost-
  2. better accuracy (relatively, and, overall)-
  3. velocity.

That said, it should be repeated that prediction markets feed on facts and expertise &#8212-so the experts remain indispensable in the general forecasting process.

No facts (e.g., political polls) &#8211-&gt- No prediction markets.

No experts (e.g., NFL prognosticators) &#8211-&gt- No prediction markets.

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Are they afraid?

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Bo Cowgill and Midas Oracle are the only media to have published about the Lee&#8211-Moretti paper. We are awaiting insightful takes from the following prediction market bloggers:

– Freakonomics @ New York Times

– Overcoming Bias – (&#8221-the future of humanity&#8221-)

– Odd Head

– Computational Complexity

– Caveat Bettor

– Mike Linksvayer Blog

– NewsFutures Blog

– Inkling Markets Blog

– Consensus Point Blog

– Xpree Blog

– George Tziralis Blog

– Chris Hibbert Blog

– Jason Ruspini Blog

– John Delaney Blog

– James Surowiecki Blog @ New Yorker

– Felix Salmon @ Portfolio – Market Movers

– Zubin Jelveh @ Portfolio – Odd Numbers

If you are a reader of one of the blogs listed above, do e-mail their owners to demand that they feature a piece on the Lee&#8211-Moretti paper.

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Learning in Investment Decisions: Evidence from Prediction Markets and Polls – (PDF file) – David S. Lee and Enrico Moretti – 2008-12-XX

In this paper, we explore how polls and prediction markets interact in the context of the 2008 U.S. Presidential election. We begin by presenting some evidence on the relative predictive power of polls and prediction markers. If almost all of the information that is relevant for predicting electoral outcomes is not captured in polling, then there is little reason to believe that prediction market prices should co-move with contemporaneous polling. If, at the other extreme, there is no useful information beyond what is already summarized by the current polls, then market prices should react to new polling information in a particular way. Using both a random walk and a simple autoregressive model, we find that the latter view appears more consistent with the data. Rather than anticipating significant changes in voter sentiment, the market price appears to be reacting to the release of the polling information.

We then outline and test a more formal model of investor learning. In the model, investors have a prior on the probability of victory of each candidate, and in each period they update this probability after receiving a noisy signal in the form of a poll. This Bayesian model indicates that the market price should be a function of the prior and each of the available signals, with weights reflecting their relative precision. It also indicates that more precise polls (i.e. polls with larger sample size) and earlier polls should have more effect on market prices, everything else constant. The empirical evidence is generally, although not completely, supportive of the predictions of the Bayesian model.

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polls-prediction-markets

Prediction markets react to polls.

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Learning in Investment Decisions: Evidence from Prediction Markets and Polls – (PDF file) – David S. Lee and Enrico Moretti – 2008-12-XX

In this paper, we explore how polls and prediction markets interact in the context of the 2008 U.S. Presidential election. We begin by presenting some evidence on the relative predictive power of polls and prediction markers. If almost all of the information that is relevant for predicting electoral outcomes is not captured in polling, then there is little reason to believe that prediction market prices should co-move with contemporaneous polling. If, at the other extreme, there is no useful information beyond what is already summarized by the current polls, then market prices should react to new polling information in a particular way. Using both a random walk and a simple autoregressive model, we find that the latter view appears more consistent with the data. Rather than anticipating significant changes in voter sentiment, the market price appears to be reacting to the release of the polling information.

We then outline and test a more formal model of investor learning. In the model, investors have a prior on the probability of victory of each candidate, and in each period they update this probability after receiving a noisy signal in the form of a poll. This Bayesian model indicates that the market price should be a function of the prior and each of the available signals, with weights reflecting their relative precision. It also indicates that more precise polls (i.e. polls with larger sample size) and earlier polls should have more effect on market prices, everything else constant. The empirical evidence is generally, although not completely, supportive of the predictions of the Bayesian model.

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polls-prediction-markets

The Open Institute Of Prediction Markets

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I am (finally) finished writing up the mission statement of The Open Institute Of Prediction Markets.

I have asked Mike Giberson, Mike Linksvayer, and (of course) David Pennock, to give me feedback, so I can see whether I am on the right track or not. If it&#8217-s the case, and once I have integrated their feedback, I will show it to 3 other prediction market luminaries, and so forth, until an ethereal sense of perfection emerges out of it. (Could take weeks.)

Stay tuned.

PS: Google is forbidden to snatch that &#8220-mission&#8221- webpage, don&#8217-t ever think of trying to read the cached webpage.

UPDATE: Got feedback. Need to work on it.

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