Ben Shannon on his misguided SELL stock market call delivered just before the stock market rally

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Ben Shannon on his &#8220-SELL&#8221- market call

Previously: Wiser Than The Stock Market &#8212- NOT

UPDATE: Andrew Page + Henry Blodget

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Wiser Than The Stock Market – NOT

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Ben Shannon (alias &#8220-Jesse Livermore&#8221-, who blogs at &#8220-Wiser Than The Crowd&#8221-) claims on his blog to have an uncanny ability at forecasting the future and profiting from it, whether it is speculating on InTrade&#8217-s prediction markets or on the US stock market. Here is his stock market call from July 10, 2009:

SELL SELL SELL

REALITY CHECK:

The stock market is up about 12% since Ben Shannon&#8217-s &#8220-sell sell sell&#8221- call on July 10th.

Spot the 10th on the chart&#8230- Ben Shannon sold the exact bottom immediately before the rally.

ben-shannon-stock-market

Thanks to Deep Throat for the tip.

UPDATE: Ben Shannon on his &#8220-SELL&#8221- market call

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Robin Hanson: My best idea was prediction markets.

Robin Hanson&#8216-s auto-biography (i.e., how Our Master Of All Universes views HimSelf):

robin-hanson-drink

Robin Hanson:

Do you find it hard to summarize yourself in a few words? Me too.

But I love the above quote. I have a passion, a sacred quest, to understand everything, and to save the world. I am addicted to a€?viewquakesa€?, insights which dramatically change my world view. I loved science fiction as a child, and have studied physics, philosophy, artificial intelligence, economics, and political science a€” all fields full of such insights. Unfortunately, this also tempted me to leave subjects after mastering their major insights.

I also have a rather critical style. I beat hard on new ideas, seek out critics, and then pledge my allegiance only to those still left standing. In conversation, I prefer to identify a claim at issue, and then focus on analyzing it, rather than the usual quick tours past hundreds of issues. I have always asked questions, even when I was very young.

I have little patience with those whose thinking is sloppy, small, or devoid of abstraction. And Ia€™m not a joiner– I rebel against groups with a€?our beliefsa€?, especially when members must keep criticisms private, so as not to give ammunition to a€?them.a€?A  I love to argue one on one, and common beliefs are not important for friendship a€” instead I value honesty and passion.

In a€?77 I began college (UCI) in engineering, but switched to physics to really understand the equations.A  Two years in, when physics repeated the same concepts with more math,A  I studied physics on my own, skipping the homework but acing the exams.A  To dig deeper, I did philosophy of science grad school (U Chicago), switched back to physics, and was then seduced to Silicon Valley.

By day I did artificial intelligence (Lockheed, NASA), and by night I studied on my own (Stanford) and hung with Xanadua€™s libertarian web pioneers and futurists.A  I had a hobby of institution designmy best idea was idea futures, now know as prediction markets. Feeling stuck without contacts and credentials, I went for a Ph.D. in social science (Caltech).

The physicist in me respected only econ experiments at first, but I was soon persuaded econ theory was full of insight, and did a theory thesis, and a bit of futurism on the side.A  I landed a health policy postdoc, where I was shocked to learn of medicinea€™s impotency.A  I finally landed a tenure-track job (GMU), and also found the wide-ranging intellectual conversations Ia€™d lacked since Xanadu.

My Policy Analysis Market project hit the press shit fan in a€?03, burying me in media attention for a while, and helping to kickstart the prediction market industry, which continues to grow and for which I continue to consult.A  The press flap also tipped me over the tenure edge in a€?05- my colleagues liked my being denounced by Senators. :)Tenure allowed me to maintain my diverse research agenda, and to start blogging at Overcoming Bias in November a€?06, about the same time I became a research associate at Oxforda€™s Future of Humanity Institute.

My more professional bio is here.

Robin Hanson is an associate professor of economics at George Mason University, and a research associate at the Future of Humanity Institute of Oxford University. After receiving his Ph.D. in social science from the California Institute of Technology in 1997, Robin was a Robert Wood Johnson Foundation health policy scholar at the University of California at Berkeley. In 1984, Robin received a masters in physics and a masters in the philosophy of science from the University of Chicago, and afterward spent nine years researching artificial intelligence, Bayesian statistics, and hypertext publishing at Lockheed, NASA, and independently.

Robin has over 70 publications, including articles in Applied Optics, Business Week, CATO Journal, Communications of the ACM, Economics Letters, Econometrica, Economics of Governance, Extropy, Forbes, Foundations of Physics, IEEE Intelligent Systems, Information Systems Frontiers, Innovations, International Joint Conference on Artificial Intelligence, Journal of Economic Behavior and Organization, Journal of Evolution and Technology, Journal of Law Economics and Policy, Journal of Political Philosophy, Journal of Prediction Markets, Journal of Public Economics, Medical Hypotheses, Proceedings of the Royal Society, Public Choice, Social Epistemology, Social Philosophy and Policy, Theory and Decision, and Wired.

Robin has pioneered prediction markets, also known as information markets or idea futures, since 1988. He was the first to write in detail about people creating and subsidizing markets in order to gain better estimates on those topics. Robin was a principal architect of the first internal corporate markets, at Xanadu in 1990, of the first web markets, the Foresight Exchange since 1994, and of DARPA&#8217-s Policy Analysis Market, from 2001 to 2003. Robin has developed new technologies for conditional, combinatorial, and intermediated trading, and has studied insider trading, manipulation, and other foul play. Robin has written and spoken widely on the application of idea futures to business and policy, being mentioned in over one hundred press articles on the subject, and advising many ventures, including Consensus Point, GuessNow, Newsfutures, Particle Financial, Prophet Street, Trilogy Advisors, XPree, YooNew, and undisclosable defense research projects.

Robin has diverse research interests, with papers on spatial product competition, health incentive contracts, group insurance, product bans, evolutionary psychology and bioethics of health care, voter information incentives, incentives to fake expertize, Bayesian classification, agreeing to disagree, self-deception in disagreement, probability elicitation, wiretaps, image reconstruction, the history of science prizes, reversible computation, the origin of life, the survival of humanity, very long term economic growth, growth given machine intelligence, and interstellar colonization.

Robin Hanson: My best idea was prediction markets.

Robin Hanson‘s auto-biography (i.e., how Our Master Of All Universes views HimSelf):

robin-hanson-drink

Robin Hanson:

Do you find it hard to summarize yourself in a few words? Me too.

But I love the above quote. I have a passion, a sacred quest, to understand everything, and to save the world. I am addicted to “viewquakes”, insights which dramatically change my world view. I loved science fiction as a child, and have studied physics, philosophy, artificial intelligence, economics, and political science — all fields full of such insights. Unfortunately, this also tempted me to leave subjects after mastering their major insights.

I also have a rather critical style. I beat hard on new ideas, seek out critics, and then pledge my allegiance only to those still left standing. In conversation, I prefer to identify a claim at issue, and then focus on analyzing it, rather than the usual quick tours past hundreds of issues. I have always asked questions, even when I was very young.

I have little patience with those whose thinking is sloppy, small, or devoid of abstraction. And I’m not a joiner; I rebel against groups with “our beliefs”, especially when members must keep criticisms private, so as not to give ammunition to “them.”  I love to argue one on one, and common beliefs are not important for friendship — instead I value honesty and passion.

In ‘77 I began college (UCI) in engineering, but switched to physics to really understand the equations.  Two years in, when physics repeated the same concepts with more math,  I studied physics on my own, skipping the homework but acing the exams.  To dig deeper, I did philosophy of science grad school (U Chicago), switched back to physics, and was then seduced to Silicon Valley.

By day I did artificial intelligence (Lockheed, NASA), and by night I studied on my own (Stanford) and hung with Xanadu’s libertarian web pioneers and futurists.  I had a hobby of institution design; my best idea was idea futures, now know as prediction markets. Feeling stuck without contacts and credentials, I went for a Ph.D. in social science (Caltech).

The physicist in me respected only econ experiments at first, but I was soon persuaded econ theory was full of insight, and did a theory thesis, and a bit of futurism on the side.  I landed a health policy postdoc, where I was shocked to learn of medicine’s impotency.  I finally landed a tenure-track job (GMU), and also found the wide-ranging intellectual conversations I’d lacked since Xanadu.

My Policy Analysis Market project hit the press shit fan in ‘03, burying me in media attention for a while, and helping to kickstart the prediction market industry, which continues to grow and for which I continue to consult.  The press flap also tipped me over the tenure edge in ‘05; my colleagues liked my being denounced by Senators. :)   Tenure allowed me to maintain my diverse research agenda, and to start blogging at Overcoming Bias in November ‘06, about the same time I became a research associate at Oxford’s Future of Humanity Institute.

My more professional bio is here.

Robin Hanson is an associate professor of economics at George Mason University, and a research associate at the Future of Humanity Institute of Oxford University. After receiving his Ph.D. in social science from the California Institute of Technology in 1997, Robin was a Robert Wood Johnson Foundation health policy scholar at the University of California at Berkeley. In 1984, Robin received a masters in physics and a masters in the philosophy of science from the University of Chicago, and afterward spent nine years researching artificial intelligence, Bayesian statistics, and hypertext publishing at Lockheed, NASA, and independently.

Robin has over 70 publications, including articles in Applied Optics, Business Week, CATO Journal, Communications of the ACM, Economics Letters, Econometrica, Economics of Governance, Extropy, Forbes, Foundations of Physics, IEEE Intelligent Systems, Information Systems Frontiers, Innovations, International Joint Conference on Artificial Intelligence, Journal of Economic Behavior and Organization, Journal of Evolution and Technology, Journal of Law Economics and Policy, Journal of Political Philosophy, Journal of Prediction Markets, Journal of Public Economics, Medical Hypotheses, Proceedings of the Royal Society, Public Choice, Social Epistemology, Social Philosophy and Policy, Theory and Decision, and Wired.

Robin has pioneered prediction markets, also known as information markets or idea futures, since 1988. He was the first to write in detail about people creating and subsidizing markets in order to gain better estimates on those topics. Robin was a principal architect of the first internal corporate markets, at Xanadu in 1990, of the first web markets, the Foresight Exchange since 1994, and of DARPA’s Policy Analysis Market, from 2001 to 2003. Robin has developed new technologies for conditional, combinatorial, and intermediated trading, and has studied insider trading, manipulation, and other foul play. Robin has written and spoken widely on the application of idea futures to business and policy, being mentioned in over one hundred press articles on the subject, and advising many ventures, including Consensus Point, GuessNow, Newsfutures, Particle Financial, Prophet Street, Trilogy Advisors, XPree, YooNew, and undisclosable defense research projects.

Robin has diverse research interests, with papers on spatial product competition, health incentive contracts, group insurance, product bans, evolutionary psychology and bioethics of health care, voter information incentives, incentives to fake expertize, Bayesian classification, agreeing to disagree, self-deception in disagreement, probability elicitation, wiretaps, image reconstruction, the history of science prizes, reversible computation, the origin of life, the survival of humanity, very long term economic growth, growth given machine intelligence, and interstellar colonization.

Can prediction markets help improve economic forecasts?

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At VOX, David Hendry and James Reade examine the question, &#8220-How should we make economic forecasts?&#8221- Among the ideas discussed is whether prediction markets could be used to improve economic forecasting. Interesting suggestion and seeming to be worthy of additional exploration, but the authors don&#8217-t go too deep here.  Instead, they assert that &#8220-prediction markets can be viewed as a form of &#8230- model averaging,&#8221- and then drift into a discussion of forecast averaging. I&#8217-m not sure that forecast averaging is a good way to look at prediction markets.

Here is what they say:

Prediction markets can be viewed as a form of forecast pooling or model averaging, a common forecast technique (Bates and Granger 1969, Hoeting et al 1999 and Stock and Watson 2004). That is, forecasts from different models are combined to produce a single forecast. In prediction markets, each market participant makes a forecast based on his or her own forecasting model, and the market price is some function of each of these individual forecasts.

Since the &#8220-prediction&#8221- implied by a prediction market is set by the marginal transaction, it depends not at all on the distribution of earlier trades, nor on the valuations of parties priced out of the market at the current price.

For example, consider two event markets: in the first 999 contracts trade at $0.50 and the 1000th and final trade is at $0.75- in the second 999 contracts trade at $0.76 and the 1000th and final trade is at $0.75.  In the typical interpretation of prediction markets, the event is &#8220-predicted&#8221- to result with a 75 percent probability in both cases.  However, averaging among the different predictions doesn&#8217-t get you that result.

(Well, strictly speaking the market price is &#8220-some function&#8221- of the prices &#8211- namely, one in which all trades but the last are weighted zero and the last trade is weighted one. You can call this &#8220-averaging,&#8221- but that isn&#8217-t the most useful explanation of the function.)

I&#8217-m not arguing that forecast averaging might not be a good idea in many situations, just that averaging doesn&#8217-t seem like a good way to explain what a prediction market is doing.

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The Accuracy Of Prediction Markets

A Lesson in Prediction Markets from the Game of Craps &#8211- by Paul Hewitt

Why Public Prediction Markets Fail &#8211- by Paul Hewitt

Both articles are required reading for Jed Christiansen and Panos Ipeirotis (alias &#8220-Prof Panos&#8221-). :-D

The truth about (enterprise) prediction markets

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Paul Hewitt:

[…] In virtually every case, the prediction market forecast is closer to the official HP forecast than it is to the actual outcome. Perhaps these markets are better at forecasting the forecast than they are at forecasting the outcome! Looking further into the results, while most of the predictions have a smaller error than the HP official forecasts, the differences are, in most cases, quite small. For example, in Event 3, the HP forecast error was 59.549% vs. 53.333% for the prediction market. They’re both really poor forecasts. To the decision-maker, the difference between these forecasts is not material.

There were eight markets that had HP official forecasts. In four of these (50%), the forecast error was greater than 25%. Even though, only three of the prediction market forecast errors were greater than 25%, this can hardly be a ringing endorsement for the accuracy of prediction markets (at least in this study). […]

To the despair of the Nashville imbecile, Paul&#8217-s analysis is quite similar to mine (circa February 14, 2009):

The prediction market technology is not a disruptive technology, and the social utility of the prediction markets is marginal. Number one, the aggregated information has value only for the totally uninformed people (a group that comprises those who overly obsess with prediction markets and have a narrow cultural universe). Number two, the added accuracy (if any) is minute, and, anyway, doesn’t fill up the gap between expectations and omniscience (which is how people judge forecasters). In our view, the social utility of the prediction markets lays in efficiency, not in accuracy. In complicated situations, the prediction markets integrate expectations (informed by facts and expertise) much faster than the mass media do. Their accuracy/efficiency is their uniqueness. It is their velocity that we should put to work.

Prediction markets are not a disruptive technology, but merely another means of forecasting.

Go reading Paul&#8217-s analysis in full.

I would like to add 2 things to Paul&#8217-s conclusion:

  1. We have been lied to about the real value of the prediction markets. Part of the &#8220-field of prediction markets&#8221- (which is a terminology that encompasses more people and organizations than just the prediction market industry) is made up of liars who live by the hype and will die by the hype.
  2. Prediction markets have value in specific cases where it could be demonstrated that an information aggregation mechanism is the appropriate method that should be put at work in those cases (and not in others). Neither the Ivory Tower economic canaries nor the self-described prediction market &#8220-practitioners&#8221- have done this job.

Nostradamical.com – 3 months later, they are doing fine.

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Nostradamical is now 3 months into launch and building a loyal following. Its model is different from our prediction exchanges. It contains more social elements. Nostradamical looks like a multi-user blog with the prediction algorithm(s) as a background feature. Question creators can express themselves on the predictions they are interested in while inviting other users to forecast the predictions &#8212-this is the DIY model pioneered by Inkling Markets in 2006. Users have fun&#8230- Twitter, FaceBook and FriendFeed are integrated, now.

http://www.nostradamical.com/predictions

How vendors are scuttling the field of enterprise prediction markets -and the prediction market industry, as a whole

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The danger of vendor conferences without any editorial line: It backfires against the whole prediction markets industry &#8212-big time.

sawing

I warned my readers many times against the vendor conferences organized by the San Francisco man. He is so desperate that he invites anybody who will pronounce the word &#8220-prediction&#8221- and &#8220-markets&#8221- in the same paragraph. Many of the invited speakers haven&#8217-t the slightest knowledge of the field of prediction markets. As for the vendors, they are incapable of producing one single case study featuring a success in the use of enterprise prediction markets. Not a single one. (And I won&#8217-t mention the &#8220-flow of information&#8221- &#8212-the worst research ever published on prediction markets.) Their vendor websites publish lists of clients, which, at first glance, look impressive, but many of those so-called customers are in fact ancient clients who have ended pilot programs years ago. To add insult to injury, this fake conference is sold $400 to gullible attendees. It is not even worth 4 cents.

The Economist reporter who attended the San Francisco conference realized what I [*] realized long ago: The field of enterprise prediction markets is all smokes and mirrors. The more the prediction market vendors will participate in such crappy conferences, the more the media will realize that the prediction market vendors are all hat and no cattle, and the more they will publish news stories bursting the prediction market bubble. And in the end of 2009, we will end up with 10 news articles in major media telling the world that prediction markets were a fad. Live by the hype- die by the hype.

The only way to get out of this debacle is to come back to basics: Do the research right, do discover the real value of enterprise prediction markets (velocity), and, then, only when you have something to show for it, go out in postings and conferences.

[*] I follow the field of prediction markets since 2003. I saw it in all shapes and stripes. You can fool your mother, but you can&#8217-t fool me.

APPENDIX:

An uncertain future – A novel way of generating forecasts has yet to take off. – by The Economist – 2009-02-26

– But although they have spread beyond early-adopting companies in the technology industry, they have still not become mainstream management tools. Even fervent advocates admit much remains to be done to convince sceptical managers of their value.

– Koch says the results so far have been pretty accurate compared to actual outcomes, but stresses that markets are complementary to other forecasting techniques, not a substitute for them.

– A big hurdle facing managers using prediction markets is getting enough people to keep trading after the novelty has worn off.

– Another reason prediction markets flop is that employees cannot see how the results are used, so they lose interest.

Bosses may also be wary of relying on the judgments of non-experts.