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 Guiliani, 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, bring 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 are 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 :-)
