Archive for the 'Betting markets' Category

The Australian Election

Thoughts?  Predictions?

Prior to the leaks, I was predicting a bland victory for Labor and nominal legitimacy for Gillard as PM.  The leaks really do seem to have hurt her, though, and it now seems like a nail biter.

Betting markets are putting the probability of a Labor victory at 62% and falling fast.

Polls are (just) giving it to the Coalition.

I was really surprised by the leaks.  The content didn’t concern me, but the fact that they were leaked at all floored me.  An MP from the Labor party — and not just any MP, a cabinet member — deliberately leaked details that were damaging to Gillard in the middle of a campaign!  They either (a) thought that a Labor victory was assured and wanted to rough Gillard up a bit so she’d be rattled and less likely to lord it over the cabinet after the election; or (b) wanted so badly to hurt Gillard that they didn’t care if it lost Labor the election.

The first possibility sickens me a little (although my inner cynic asks why I was so surprised) and the second horrifies me a little (because it’s safe to assume that no other party is any better, which suggests that Australia is being led by a collective equivalent of Emperor Nero crossed with Machiavelli crossed with a two year-old child).  Should Labor still win, surely Gillard will to be tempted to find the person responsible and stomp hard on their political neck.  If she can’t find who it is, I can imagine the modern political equivalent of “nobody gets to play until the person who broke the window owns up” happening and Gillard firing two-thirds of the cabinet.

One in 20 Australians play the pokies WEEKLY

Stephen Lunn, writing at The Australian, channels the Productivity Commission’s recent report:

[The Productivity Commission] finds the legal ban in Australia on online gaming is a failure, with betting traffic heading to overseas sites that offer little in the way of consumer protection.

In its draft report on gambling, the first in-depth national look at Australia’s gambling industry in a decade, the commission finds that gamblers are losing $18 billion a year, of which $12 billion is lost on gaming machines.

It estimates that around 5 per cent of adults play weekly or more on gaming machines, and 15 per cent of those, or around 125,000 people, are problem gamblers.

Productivity commissioner Gary Banks says “a large number of people have problems with their gambling (and) it is vital that they are given a tool to achieve greater control”.

The commission recommends the reduction in the amount that can be lost on a gaming machine from its current upper limit of $1200 an hour to $120 per hour, and giving people a choice when they sit down on how much they spend, using the latest technologies.

[Emphasis added by John Barrdear]

If we assume that state governments and pubs don’t want to get rid of pokies because they’re so dependent on the revenues, then surely the only serious hope for enacting this would be for it to be a federal law.

Lifting the ban on online gambling and permitting pokies but limiting the loss rate seem sensible ideas to me – they leave people with the freedom to gamble if they wish, but limit the loss to largely one of time rather than having the option of putting the house down.

Of course, the softest still-ultimately-effective policy would be to simply hold the upper limit on loss rates constant while letting the minimum wage and welfare benefits rise with inflation so that the limit falls both in real terms (relative to the cost of living) and relative to household income.

The Nobel committee can’t lose in their decision over Fama

Alex Tabarrok points out the amusing truth:

Ladbrokes gives Eugene Fama the best odds for winning the economics Nobel.  Thus, if Fama wins he will have deserved to have won and if Fama loses he will not have deserved to have won.  The Nobel committee cannot go wrong no matter what it does!  Think about it.

New Hampshire and the prediction markets

Plenty of people, Paul Krugman among them, are pointing out that just like the polls (which, on average, had Obama ahead of Clinton by over 8 points), the prediction markets were plainly wrong in forecasting the outcome of the Democratic New Hampshire primary. They’ve got a point.

These are the daily closing prices on the Clinton and Obama contracts to win the New Hampshire primary from InTrade:



Up until Iowa, they were fairly steady at ~60% for Clinton and ~40% for Obama, but from the 3rd of January onwards, there was a clear movement towards Obama. On the day before the primary, the markets had Obama 97.8% likely to win. On the day, Clinton won with 39.07% of the vote, while Obama received 36.47%. So why did the market get it wrong?

Paul Krugman contends that the prediction markets were just reflecting the polls and talking heads, presumably because that was all the information there was to be had. This naturally raises the question of why they were wrong (e.g. did we just witness the Bradley effect in action?), but that is not the point here. A prediction market, according to the theory, is meant to be superior to the polls in predicting outcomes because it combines information contained in the polls with information from other sources. So perhaps Krugman is right. But if he is, why did the market go so far towards Obama?

My guesses:

  • Perhaps Krugman is partially right, but the talking heads provided a positive feedback loop. The polls predicted Obama, which the markets saw. The talking heads saw the polls too (perhaps in more detail) and then spoke about it on television, but added effectively no extra information. The markets saw the talking heads and believed it to be extra information in support of the polls.
  • Like any market, the prediction markets are susceptible to bubbles. Perhaps we saw one here in the days between Iowa and New Hampshire.
  • A lack of “true” liquidity. There was plenty of nominal liquidity in these markets leading up to and during the counting, but how much of the trading was arbitrage, how much was momentum (i.e. bubble) trading and how much was “true,” changing-belief-based trading? As the counting occurred, I was watching both the leaked figures on the Drudge Report and the movement on InTrade. It seemed that the prediction market was moving steadily towards Clinton, but nowhere near as quickly as one would have expected. For example, at 9:40pm, with 46% of the vote counted, Clinton was leading 49,719 (40%) to 45,383 (36%), from which one would conclude with extremely high confidence that Clinton would win, but the market was still only putting her at 65%.
  • Perhaps – and I’m by no means certain of this last point – in order for a prediction market to work perfectly, we also need people to set the size of their position in proportion to their confidence in that prediction. So perhaps there were traders who, looking at Drudge or some other source were extremely confident that Clinton would win from quite early in the counting, but since they did not take large enough positions, they did not move the market. In other words, liquidity requirements for a successful prediction market are not just on the number of trades, but on the volume traded.

Update: Justin Wolfers, a long-time researcher in prediction markets, has an article in the WSJ highlighting how surprising the result was given the market predictions.

We were led to this research by an age-old racetrack puzzle economists call the “favorite-long shot bias“: Horse bettors historically have overbet long shots, and they win less often than their odds suggest. Our research suggests that similar biases hold in political prediction markets.

As such, Sen. Clinton’s comeback is even more stunning, as political underdogs have historically won even less often than suggested by their prediction market odds.

Historical comparisons are already being drawn between the New Hampshire primary and the famous 1948 presidential race in which President Harry S. Truman beat Republican challenger Thomas Dewey, despite the infamous “Dewey Defeats Truman” headline in the Chicago Tribune.

Yet the magnitude of the Clinton surprise is arguably even greater. Indeed, historical research by Professors Paul Rhode of the University of Arizona and Koleman Strumpf of Kansas University has shown that in the Truman-Dewey race, prediction markets had seen hope for President Truman despite his dreadful polling numbers, and he was rated an 11% chance of winning the election by election-eve. Thus, Sen. Clinton’s victory on Tuesday was more surprising than President Truman’s in 1948.

Personally, I seem to be thinking of this the other way around. Assuming that prediction markets are generally better than other forms of forecasting, I find it surprising that they got it so wrong on this occasion. Rather than thinking of the result as the equivalent of a 6-sigma event given the prediction market, I wonder what was different this time that so disturbed the market’s ability to predict?

Update 2: Okay, okay a 3-sigma event 🙂  Justin in an email:

For the polls, this was about a 3-sigma event.  For the market, which had Hillary priced at about a 7% chance [JB: Justin is referring to the WSJ market], it is about a 1.7 sigma event.  They aren’t that unusual.  Indeed, they probably happen about 7% of the time

Pr{US recession in 2008} = 47.5%?

According to betting on InTrade, the current market estimate for the probability that the US will experience a recession during 2008 is 47.5%.

BTW … InTrade really don’t like the idea of people directly linking to their contract pages like that.  I had to hunt for ages to find a direct link.  I guess it’s because they don’t include advertising on the contract page, but do through the main pages.