Mercury's Blog

How did 2009 turn out?

Early this year I posted my predictions for 2009. In the best spirit of Robin Hanson (getting better predictions by simply tracking how close predictions matched reality), I want to see how I did.

• “Prediction markets in 2009 are going to become even more well-known and wide-spread, but there will be no single event that brings them to the attention of the public. It’s going to be a slow, but steady, growth.” – This was spot on. All of the vendors appear to be doing well, but there was no big “event” that brought everyone attention.
• “All of the prediction market vendors will mature their business offering/proposition.” – Not having been on the receiving end of any of their sales pitches, I can’t say this one is true for sure. But from their blog posts and public statements I would assess this as likely.
• “HubDub will continue to only be the only strongly popular play-money prediction market.” – Put me down as wrong on this one. Nigel and the creators of HubDub have focused their time and effort on FanDuel instead. (Rightly, for revenue reasons!) So while HubDub is still active, it’s not the hive of activity it was for a while.

That said, I’m still a fan of niche, public, popular sites like HSX. They managed to turn play-money prediction markets into a real-money revenue stream by analyzing trader behaviour (which can only be seen by administrators) and selling that business intelligence to the studios. This could be replicated in many industries, such as video games or television.

• “While a couple additional software vendors may appear, I get the feeling that the market for prediction market software is largely saturated.” – This was also spot on.
• “I’m looking forward to see how the CantorExchange develops.” – Not so much a prediction, but a hope that it proved interesting. It’s taken a long time to get up and running apparently, and won’t be widely launched for real-money contracts until 2010. (If I read the website correctly.)

A great development from InTrade

Just yesterday John Delaney of InTrade posted on his blog that InTrade will soon be offering some historical market data to the public for free. (As he notes, this means they’re losing a source of potential revenue, as historical data can be quite valuable.) This is a great development, and should be a solid source of data for people to dig in and get interested in how traders operate in a prediction market. Kudos to John for doing this.

While Wordpress is great software, its ubiquity means that a lot of script-kiddies and general hackers like to attack it. All of the different settings, options, plugins and the rest mean that it takes quite a bit of work to balance letting people participate (through comments, postings) while keeping spammers and hackers out.

About a year and a half ago, my blog was hacked. I was notified of it by Google’s webmaster tools, and it took quite a while to go through all the different files to find the offending code and strip it out. It ended up being located in a number of different places, so it took a few go-through’s re-submitting the site to Google before the hack-detection software declared it clean.

I was always a little worried that I hadn’t gotten it all. Recently, I came across a great couple of blog posts that I highly recommend:

• This is a good general description of why you need to be worried and links to a couple of tools you can use.
• This is a link to a Wordpress plugin called “Exploit Scanner.” I uploaded it to this blog and found a number of files that were clearly hacker-installed. I’ve since un-installed them, but want to post some things to help anyone else that might face the same situation in the future.

———————-

Files that were uploaded:
fx_akismet.php
fx_blogger.php
fx_I10n.php
fx_menu.php
fx_wp-config.php
fx_wp-db-backup.php
… and a folder of 70 html files and a javascript file meant to steal Google PageRank

All the php files were nearly identical. Here’s the code:

I don’t code in php, so I don’t really know what this says, but hopefully it might be useful to anyone afflicted by the same script.

I highly recommend if any of you have Wordpress blogs to take these same steps to see if you’ve been hacked.

It might not be the most ground-breaking innovation out there, but I really like what Inkling has done recently with their trading widgets. They recently demonstrated how your employees and partners can predict project management milestones within Basecamp, a very popular web application for project management.

Basecamp is used a lot in small and medium businesses, and I’ve heard that it’s becoming more common in niches of larger businesses. (It’s easy to put the relatively small monthly bills on corporate purchase cards.) It’s popularity means there’s a great population of potential customers that can start taking advantage of this new source of information.

(Inkling’s full post on how to implement prediction markets within Basecamp can be seen by clicking here.)

I’ve written before that I think there is a LOT of value in using prediction markets to forecast project milestones. (It’s generally done poorly, if it’s done at all.) One of my more popular posts according to Google Analytics is “RAG status – people lie, prediction markets don’t“. I dreamt of a day where prediction markets would be run on all project milestones, and the results of those markets would drive a project’s “RAG status”. That is now easily done with Inkling’s Basecamp widget.

There are probably still a few details to be ironed out with the actual implementation, but this is a great step forward for businesses that want better intelligence from their employees and partners. I look forward to hearing more from Inkling about how customers have taken advantage of this and if/how it’s worked for them.

From their blog post, here are two screenshots of what the screens look like before and after you trade:

The “trading” screen:

The results screen:

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 Edmunds.com 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.

Summary

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.

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

OR

Click here to view the list via Google Docs.