Freakonomized!

Just came back from a short trip in England and I realized that the number of visitors to the blog has gone up tremendously. After looking at the referrals, I spotted the website that sent the traffic:

http://freakonomics.blogs.nytimes.com/2007/12/13/the-freak-est-links-78/

Apparently Steven D. Levitt and Stephen J. Dubner, the authors of the well-known Freakonomics book and of the homonymous blog at The New York Times, considered interesting the posting about the efficiency of the prediction markets.

Apparently having a link from a New York Times blog results in a couple of thousand visitors within a day. Given that I was just talking about some very preliminary results in that posting, reaching that level of readership so early on, is as close as it gets to "instant gratification" in terms of research :-)

Plus, I now feel the urge to write down all the results as soon as possible, following the "authors should write to satisfy their readers" mode of doing research (via Daniel Lemire). Indeed a very good motivation!

Love, Facebook, and xkcd

Randall Munroe's interpretation of the article from Reuters "If it's Facebook, it's love":

Political Prediction Markets: Some Thoughts

Apparently, my last postings on the predictability of the political prediction markets generated some interest. Niall O'Connor decided to check how accurate our predictions are, and after a few days he checked again to see how well we have done.

Our prediction that the price for Hillary Clinton will go down proved to be correct: the price declined from 70, on Dec 2nd (the time of the original post), to 63 on Dec 9th, a 10% decline. Similarly, for Mitt Romney we predicted a decline and the price declined from 24, on Dec 4th, to 18.5 on Dec 9th, a 23% decline.

For Giuliani, we said "The analysis is more difficult in this scenario, but for the next few days we see stabilizing signals with a trend to go upwards" and we were proven wrong: the price declined from 43 on Dec 2nd, to 39.5 on Dec 9th, an 8% decline. I realized what was wrong in my reasoning. What was stabilizing was the sentiment index, not the price. And a stabilized sentiment around 50% tends to be a pretty bad adviser on how the market will move.

The fact that it is possible to predict the prediction markets brings automatically the question: "why?" What makes the markets predictable? The first answer is liquidity. The markets are not liquid, there are not enough participants, and there is a lot of momentum trading. However, I would like to list another explanation (already posted as a comment on Midas Oracle)

My understanding is that Betfair odds moved from 1.44 to 1.50 (according to the screenshot in the original posting). While indeed this corresponds to a drop from 69% to 66% (an almost 4% drop in share price) this is not as drastic as a drop from 69% to 50% within such a short period of time. Plus, the Betfair drop from 69% to 66% is comparable with the drop in Intrade (from 67% to 64%).

Also, I am not sure about the liquidity hypothesis for explaining the inefficiency. An alternative explanation is the following:

Political markets are not stock markets. They reflect the aggregate opinion of the traders about public's intention for the candidate. Notice that we have two levels of beliefs: one for what traders believe about the public's intentions, and a second for what the public actually intends to vote for.

Not every member of the voting public reads every piece of information. When the same news is being repeated over and over in the mainstream news outlets, then more voters are influenced. Hence, the longer the news about a candidate stay around, the longer the public gets influenced by the same, stale news and changes intentions. This is correspondingly reflected in the prediction markets, potentially in an efficient manner.

This may indicate that it is not that the markets are not efficient, but that the voting public is not "efficient" (i.e., voters do not incorporate all the available information in their voting decisions.)

We can test this hypothesis by testing the efficiency/predictability of political prediction markets vs. the efficiency/predictability of non-political markets.

We will work further with George Tziralis on the topic, and we will keep you posted.

Public commitment is always a good motivation to work harder :-)

Then, Bo Cowgill commented that indeed using text mining together with prediction markets is indeed a good idea. Bo's comment made me think about parallels in "prediction market trading" and "stock market trading". As Bo pointed out, in existing stock markets, there is a significant amount of algorithmic trading. This algorithmic trading makes the stock market significantly more efficient than, say, in the early 1980's where the programmatic trading was at its infancy. In fact, I have heard many stories from old-timers, saying that in the early days it was extremely easy to find inefficiencies in the markets and get healthy profits. As algorithmic trading proliferated, it became increasingly harder to spot inefficiencies in the market.

Something similar can happen today with prediction markets. If we have a prediction market platform that allows automatic/algorithmic trading, then we can improve tremendously the efficiency of today's prediction markets. Furthermore, such a tool (if done with play money) can be used as a great educational tool, similar to the now inactive Penn-Lehman Automated Trading (PLAT) Project. Allowing also for some data integration from the existing prediction markets (BetFair, Intrade, etc.) we could have a pretty realistic tool that can be used for many educational purposes that, at the same time, can generate useful and efficient prediction markets.

Now, I need to find someone willing to fund the idea. Ah, there are a couple of NSF call for proposals still open :-)

By Popular Demand: Mitt Romney

The last post generated some general interest and I got requests to post analysis for more presidential candidates. As one more data point, here is the market for Mitt Romney to be the Republican Presidential Nominee in 2008:

I checked our sentiment indicator again (in maroon), which seems to capture well the upward spikes. (If you look carefully, our indicator spikes upwards before the market.)

This market, similarly to the market of Hillary Clinton, seems to move in cycles. This cyclical behavior can be nicely visualized by plotting the 30-day moving average of our sentiment index (in black). It seems that a downward cycle has started for Romney and should continue for the next couple of weeks, until the 30-day moving average gets close to 0.3 or so. Time will tell :-)

Prediction Markets are NOT Efficient

I have been wondering in the past if prediction markets are efficient. Then, I was saying:

how long does it take for a prediction market to incorporate all the available information about an event? Liquidity seems to be an issue for the existing prediction markets, preventing them from reaching equilibrium quickly.

In fact, today's prediction markets are far from being efficient. Ari Gilder and Kevin Lerman, as part of an undergraduate project at University of Pennsylvania supervised by Fernando Pereira, have shown that by using linguistic analysis of news articles it is possible to predict the future price movements of the Iowa Electronic Markets. Therefore, the Iowa markets did not incorporate all the available information. Furthermore, the results indicated that it is possible to predict the price movement by simply using past pricing data. Therefore, the markets were not even weakly efficient. (Kevin is now a first year PhD student at Columbia University.)

One question was whether liquidity played a role in that result. The Iowa markets are thinly traded with upper limit on how much someone can bet. This imposes some artificial constraints making it difficult for information to flow freely into the market. Therefore, it is important to examine other markets with higher liquidity.

Over the last months we have been discussing this issue with George Tziralis, trying to examine how to evaluate the "Efficient Prediction Market" hypothesis. After long discussions, we came up with some techniques for extracting signals from the news about the prediction markets and see whether we can use these signals for predicting the future performance of markets in InTrade. Our sentiment indicator seems to work pretty well, even in liquid markets. Here is a preliminary result for the market on whether Hillary Clinton will be the Democratic Presidential Nominee in 2008:

Our sentiment index (in maroon) is close to 1 when we predict that the market will move higher, and it is close to 0 when we predict that the market will move down. Typically, it works pretty well for predicting long periods of price increases and declines. To put our money where our mouth is, the signal from the last few days shows that Hillary's market price will edge lower in the next few days/weeks.

The market prices for whether Giuliani will be the Republican Presidential Nominee in 2008, together with our sentiment index is displayed below.

The analysis is more difficult in this scenario, but for the next few days we see stabilizing signals with a trend to go upwards.

We will need to analyze quite a few more markets before generating the paper, but so far the results seem interesting.

Let's see what the future brings :-)