Archive for the 'General' Category

A new competitor in prediction markets, and their brilliant case study

Monday, September 28th, 2009

I recently found out about a new competitor in corporate prediction markets: CrowdClarity.

I’m a little partial to these guys, mainly because they come from my alma mater, the University of Michigan. The key people look to be a mix of entrepreneurial students and professionals. The company itself was started a year and a half ago, but has had success with early pilot projects.

In fact, three slides included in their “Learn more” online slideshow are quite powerful statements as to why prediction markets can be useful. They were predicting car sales in the winter of 2008/2009, during what was one of the most volatile months the industry has seen in many, many years. And the prediction market beat the internal forecast made at the beginning of the month, and the expert forecast made at the end of the month.

Here are the slides:




To recap, the prediction market beat the official GM forecast (made at the beginning of the month) easily, which isn’t hugely surprising considering the myopic nature of internal forecasting. But the prediction market also beat the forecast. This is particularly interesting, as Edmunds would have had the opportunity to review almost the entire month’s news and data before making their forecast at the end of the month.

Examining the numbers

Let’s quickly quantify this error. Assume an average Chevrolet sells for $18,000. After dealer markup, assume that GM/Chevrolet receives $16,000 per vehicle.

Within the first week of November 2008, the prediction market would have warned Chevrolet that they were going to miss their revenue targets by $800 million in the Chevrolet division alone. And depending upon the exact product mix, this could have easily exceeded $1billion.

Now Chris can blather on about corporate prediction markets, but he’s simply wrong. Assume that even with three weeks’ early warning Chevrolet was only able to save 10% of that gap, it’s still $80million in savings. Even if a corporate prediction market for a giant company like GM cost $200,000 a year, that would still be a return on investment of 40,000 %. And again, that’s in the Chevrolet division alone. (Note: It would be a rare prediction market that cost $200k/year to run.)

Now not every problem should be solved by a prediction market. This is where management expertise comes in: are the errors large enough to warrant the cost of reducing those errors? But big problems with big numbers are often very suitable to address with a prediction market.


I’d like to wish good luck to the CrowdClarity team. It’s great to see Wolverine entrepreneurs working on prediction markets. There are more and more players each year in the corporate prediction market scene, but with case studies like this behind their belts, they’ll be well-placed to pick up some business.

What I’ve been working on recently…

Monday, September 21st, 2009

I know it’s been a bit quiet here on Mercury’s Blog for a few months. Over the past year I’ve been studying at Cambridge University for my MBA, and this summer I undertook a research project around Y Combinator.

Y Combinator is a very unique seed accelerator program. Startup companies get ~$15-20k in funding in return for ~6% equity, three months of intensive business and product advice, connections to mentors and potential advisors. Inkling Markets was in the second cohort of Y Combinator funded companies, for example.

My hypothesis was that a lot of people/organizations are starting seed accelerators without really examining the full scope of innovations they need to think about in order to achieve their goals. I wanted to take the opportunity to look into why entrepreneurs choose to go into a seed accelerator, why individuals choose to start a seed accelerator, and then propose a framework for designing new programs.

You can click here to view the post on my personal blog that has a bit longer executive summary. But I’ve embedded the paper and data sources below. I hope you find it interesting.

Copying Y Combinator

Appendix A – List of Seed Accelerators

Click here to view the list of seed accelerators. Only seed accelerator programs are listed; see main paper for details.

Appendix B – Example Seed Accelerator financial model

Appendix C – List of all companies founded by Seed Accelerators


Click here to view the list via Google Docs.

Starting from the wrong metaphor – Prediction Markets and Ideas

Sunday, August 30th, 2009

Chris Masse over at Midas Oracle has recently generated a fair number comments on his post “Are IBM Smarter Cities prediction markets too smart for people?” As the commenters rightly point out, what IBM was doing was NOT a prediction market, but instead a polling system.

This is something I’ve been talking about since May 2008. When you try to use a prediction market to forecast the “best” ideas, you get a “Keynesian beauty contest.” To quote (via wikipedia):

It is not a case of choosing those [faces] that, to the best of one’s judgment, are really the prettiest, nor even those that average opinion genuinely thinks the prettiest. We have reached the third degree where we devote our intelligences to anticipating what average opinion expects the average opinion to be. (Keynes, General Theory of Employment Interest and Money, 1936).

Starting from the wrong metaphor

Ideas in an organization are a long-term investment, where significant work and development is required before you can even get to the prototype stage of development. This is a very different concept than regular, known markets, such as the number of customers in the next quarter or “will me meet sales targets next year” which are asked in prediction markets. Prediction markets work well for the regular, known markets, because prediction markets involve liquid, easily-traded contracts.

Idea markets should be seen through the metaphor of venture capital. An individual sees lots of good ideas, but none of them are well developed. Just as a venture capitalist puts resources into ideas and those resources are locked-up and illiquid until the idea has proven its success or died, an individual in an idea market should see their votes as an illiquid, sunk cost.

The organization running the idea market then concentrates their resources where individuals concentrate their votes. But just like venture capital, this cannot be a one-time market. The organization needs to regularly run similar markets, say every quarter or every six months. If an idea gets resources but doesn’t make enough progress (or doesn’t look like it will be as effective as originally thought) the market will stop voting for it. As an idea makes good progress, it continues to receive resources for development. Finally, when an idea is implemented and made successful, all of the people that voted for it during its development will be rewarded. And just like venture capital, the earlier you contribute the more you are rewarded.

The key is that idea selection and development is long-term work, and thus when markets are used to forecast and help allocate resources, the market structure must match that long-term approach.

Recent prediction market news

Monday, July 20th, 2009

I’ve recently come across a couple of interesting notes in the prediction market industry.

#1 – Trading UK Housing Prices

CityOdds, based in London, has recently opened a prediction market (currently marketed as a competition) to predict a UK housing market index. While this is a play-money market, there is a £10,000 first-place prize.

CityOdds runs both fantasy markets, as well as real-money markets on currencies and other commodities. Mike Chadney, the founder, is a good guy and experienced “City” man. Check out the housing market here:

#2 – Predictify shuts down

Predictify made a bit of a splash when Scott Adams used them to forecast how many sales of his (then) new book were going to be sold.

It seemed a bit of an odd company; venture-funded, but with people who had no background in prediction markets at all. They offered a small number of markets where accurate forecasting would win cash, while most markets were just for leaderboard position. (My trading was perhaps typical: I’d log it and only scan the markets where I could win cash and ignore the rest.)

Well, they’ve died. According to an announcement on their website:

Due to the tough economic climate, we are planning to cease operations and shut down the company in the near future. If you have an account balance of $20 or more, please visit your account page and enter your withdrawal information by 11:59pm on August 31, 2009 to receive payment.

We sincerely enjoyed building and operating Predictify, and we’re glad that you could be a part of it.

The Predictify Team

My question is this: what does this mean for Crowdcast?

Crowdcast has an absolutely fantastic team who have great experience in prediction markets. But can they thrive as a venture-funded company? I’m hopeful, but perhaps am more skeptical that there are simply enough customers truly interested in their level of solution. Particularly as so many companies need hand-holding and thus a lot of expensive people-time through the early stages of implementation, are the profits sufficient to satisfy investors?

I don’t know, but I do believe they’ve at least got the people to give it a go. (Strangely enough, it was Crowdcast’s founder Mat Fogarty who originally told me about Predictify well before their launched.)

Stock market metaphor with ideas

Tuesday, June 30th, 2009

I’ve been writing about ideas and innovation for about a year now. More and more evidence has, in my opinion, built up to show that the stock market metaphor is not appropriate for finding the best ideas from a prospective pool.

From Emile of NewsFutures, here are three links and a quote from each:

The imagination market, Information Systems Frontiers (July 2007)

Participants were able to trade shares of technology ideas over the course of 3 weeks, resulting in the market identifying the “best” idea as the highest priced security. Our findings suggest that information markets for idea generation result in more ideas and more participants than traditional idea generation techniques; however, using markets to rank ideas may be no better than other methods of idea ranking.

Examining Trader Behavior in Idea Markets: An Implementation of GE’s Imagination Markets , Journal of Prediction Markets (April 2009)

In this experiment, we examine the behavior of traders that have submitted the ideas on the market and their influence on the market’s outcome. An idea’s submitter is clearly motivated to have his idea valued highly by the market, both by the funding given to the top idea as well as smaller prizes given to the top three ideas. In general, founders tended to buy their suggested ideas at prices above the volume-weighted-average-price (VWAP) in significant volumes. We discuss the implications and mitigation strategies. A survey of market participants yielded mixed results regarding the market’s effectiveness at ranking ideas but very positive results regarding the quality of ideas proposed.

GE Global Research blog (link here)

From the comments,
“GE Healthcare IT attempted an imagination market a few months ago to bring forth some new ideas for the company’s future. It left me with very strong mixed impressions: on one hand, it’s wonderful that we’re leveraging the power of technology for mass collaboration and idea sharing. On the other hand, I felt that the tool obfuscates the very opinions it seeks to gather by due to the inherent complexities of market behavior.

My primary objection is the use of the stock market paradigm to evaluate these ideas. Simply, I find it too abstract to be useful in gathering feedback about the quality of an idea. Stock investment is done by trying to predict *the change in collectively perceived value of something over time*. However, when dealing with ideas, neither the collectively perceived value, nor the change in this value over time are valuable metrics; you want people evaluating ideas based on *their opinions*, not based on their attempts to predict changes in the investment decisions of others over the course of a few weeks. These are static ideas isolated from one another, not evolving companies that interact. I think a stock market is an unnecessarily abstract, and distracting way to retrieve simple information: what do people think of these ideas?”

I’d be interested in hearing from people that disagree with this…