Love, Facebook, and xkcd
Randall Munroe's interpretation of the article from Reuters "If it's
Facebook, it's love":
Posted by Panos Ipeirotis at 1:15 AM
1 comments Links to this post
Sunday, December 9, 2007
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 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 :-)
Labels: academia, efficient markets, Hillary Clinton, Mitt Romney,
prediction markets, presidential elections 2008, research, Rudy
Giuliani
Posted by Panos Ipeirotis at 11:23 PM
0 comments Links to this post
Tuesday, December 4, 2007
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 again our sentiment indicator (in maroon), which seems to
capture well the upward spikes. (If you see 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 should
continue for the next couple of weeks, until the 30-day moving average
gets close to 0.3 or so. Time will tell :-)
Labels: Mitt Romney, prediction markets, presidential elections 2008
Posted by Panos Ipeirotis at 7:53 PM
0 comments Links to this post
Sunday, December 2, 2007
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 undegraduate 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 :-)
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