The basic points:

Gold does well when real rates of return are low. Real rates describe the price of gold much better than inflation alone. This is because real rates reflect the opportunity cost of holding a relatively useless asset. Part of the reason gold seems irrational is that this extrinsic pricing is unintuitive and largely unappreciated.

Gold does well when liquidity, measured for example by LIBOR, is not especially tight.

Sentiment can be predictive with gold.

The “extrinsic” way of thinking is natural in the fx world where all trades are two‐sided, and the idealized one‐sided currency , e.g. Dollar Index, is a weighted average of two‐sided rates. Other examples: the Fed Model and Dividend Discount models explicitly tie together the pricing of equities and interest rates. The housing bubble was to some extent already a mispricing of money in the form of interest rates. What was the “right” price for housing given the price of money?

Do people who claim that assets exhibit “irrational” moves have a clear idea of what level of volatility would be “rational”, especially given such cross‐influences? I do not endorse the Dividend Discount Model, but no-one can deny that it is a fundamental model, and it predicts higher volatility when rates are low. Given current levels, a 10% one day drop of the market is by no means absurd. The stock market should also have more idiosyncratic volatility when it is driven by “top-down” policy, rather than averaging many “bottom-ups.”

There is perhaps a “long-termism” fallacy. Even if prices change glacially, if you want to maintain a portfolio limited to 30 stocks out of a universe of 6000, it is easy to see how a sensible person might change the “best ideas” list with some frequency. The more prices change, the more frequent portfolio changes would be in order from a valuation standpoint. Again, asset prices are not hermetically sealed, one‐sided meanings and values. There is always some discount factor or relative valuation at play.

The easiest way to achieve a shock “20 standard deviation” move is to just not mark (or mis‐mark) for a while. Deferral of pricing is much more likely than active trading to originate an explosion large enough to affect the underlying economy. Deferral of pricing, not active trading, played a large role in the corporate credit crisis. Social Security and entitlement programs are also “off balance sheet” debt. At least banks failed to predict the future. Governments failed to predict the past. The basic demographic and longevity trend has been apparent since at least the 1960s.

Demographic trends suggest lower real returns than those seen in the 20th century. Do economists have a demographic blind spot?

Jason Ruspini:

The piece is problematic insofar as it underweights proximity to areas where people work, which results in high ratings for distant neighborhoods and low ratings for central ones, on top of the effect of higher rents in central neighborhoods. True, if you work from home, it might make more sense to live in the outer boroughs. But if you have a one hour + commute every day, it doesn’t really help that you happen to live near a subway stop and thus have a relatively high “transit” rating.

For the restaurant category, he seems to be considering quantity but not quality. How else does Long Island City have a higher rating than Gramercy/Flatiron, where 9 of the top 50 Zagats restaurants are located? I don’t even think that Long Island City beats Gramercy/Flatiron in terms of quantity either.

Ultimately, of course, preferences are too subjective to give one ordinal ranking, but the distance-to-average-work-location issue seems glaring, and increases the outer borough bias.

Via the NZ economist

Prof Michael Giberson:

I think the “small, secretive committee” explanation is weak [].

Bradbury does an excellent job sifting through the shifting coalitions revealed in the three rounds of IOC voting. Neither Madrid nor Toyko showed any significant ability to attract votes as the rounds proceeded. It was going to be Rio or Chicago all along, but Chicago was weakest in the four-way vote and lost early, leaving the games to go to Brazil.

Based on Bradbury’s [analysis], I’m convinced that the decision was pretty much a toss up between Chicago and Rio. That conclusion was also implied in the prediction market prices just before the decision. Sure, the prediction markets favored Chicago, slightly, over Rio; I don’t think you can call it a miss given the closeness of the decision.

Well:

1. The voting mechanism of the IOC regarding the 2016 Olympics venue was known to the news media and the prediction market traders (like Ben Shannon) well before the vote.
2. The prediction market traders gave a surreal boost to the Chicago probability.
3. The reality check is that Chicago was the weakest candidate.
4. Hence, the prediction market traders were not informed enough about the basic facts regarding the IOC voting, for the reason that the International Olympic Committee is governed by secrecy, politics, and pork.

Next: “I have to agree with Chris. The market participants did not possess a sufficient level of information completeness to arrive at the correct prediction.”

Previously: The Chicago candidacy, which was favored by the prediction markets (and gullible bettors like Ben Shannon), is the one that fared the worst.

Previously: Chicago won’t have the Olympics in 2016.

ADDENDUM:

- BetFair’s event derivative prices:

- InTrade’s event derivative prices:

- HubDub’s event derivative prices:

Who will recieve the winning bid to host the 2016 Olympics?

- Here’s what happened to the Paris 2012 event derivative:

At first blush, it appears that we finally have a bona fide prediction market success! If we’re going to celebrate, I’d suggest Prosecco, not Champagne, however.

There are a number of reasons to be cautious. These represent only a couple of markets. We don’t know why Urban Science people appear to be so adept at forecasting GM sales in turbulent times. There is no information on the CrowdClarity web site to indicate why the markets were successful nor how their mechanism might have played a role in the PM accuracy. I’m guessing that it would have been really easy to beat GM’s forecasts in November, as they would likely have been even more biased than usual, mainly for political reasons. I’m not sure how Edmunds.com’s may have been biased or why their predictions were not accurate. Maybe they are not so good at predicting unless the market is fairly stable.

The CrowdClarity web site boasts that a few days after the markets were opened, the predictions were fairly close to the eventual outcome. This is a good thing, but, at this point it is not useful. No one knew, at that time, that those early predictions would turn out to be reasonably accurate. As a result, no one would have relied upon these early predictions to make decisions.

I’m even more skeptical of the company’s contention that markets can be operated with as few as 13 participants. Here we go again, trying to fake diversity.

It is interesting that a prediction market comprised of participants outside of the subject company did generate more accurate predictions than GM insiders (biased) and Edmunds.com (experts). The question that needs to be answered is why. Clearly, Urban Science people did have access to better information, but why?

Unless we know why the prediction markets were successful at CrowdClarity, it is hard to get excited. There are too many examples of prediction markets that are not significantly better than traditional forecasting methods. This one could be a fluke.

I’ll have more to say, soon, when I write about the prediction markets that were run at General Mills. There the authors of the study found that prediction markets were no better than the company internal forecasting process.

Paul Hewitt’s analysis is more interesting than Jed Christiansen’s naive take.

Paul Hewitt’s blog

Next: Assessing the usefulness of enterprise prediction markets

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. [...]

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. [...]

Make up your own mind by reading the whole piece.

Next: The truth about CrowdClarity’s extraordinary predictive power (which impresses Jed Christiansen so much)

Next: Assessing the usefulness of enterprise prediction markets

#2. There is indeed a lack of high quality statistical research on prediction markets. I have seen published papers doing terrible things like regressing prices from one prediction market on prices in a different prediction market and constructing standard errors for such regression as if prices represent i.i.d. sequences. Our paper was in fact an attempt to show that neither prices nor returns in a prediction market can be treated as i.i.d. sequences.

Go read the rest.