A commodities fund manager's comments on gold vs gold-miners arbitrage

John Netto, a principal in a commodities fund that focuses heavily on gold, wrote me the following concerning my article on arbitrage between gold and gold-miners: "... there is a paradox that exists in many instances with gold companies and the underlying metal, which could potentially unwind most pairs traders. This is the dynamic of non-recourse loans that companies take on when doing a project. This would never show up in a quantitative model but can put companies in a position that when gold rises, they can get hurt to some degree. Banks that do non-recourse loans require the companies to sell futures to guarantee payment for the project in case the price of gold falls. This way, they will not lose if the project no longer becomes a viable business endeavour. If gold rises, these companies must show massive mark-to-market losses on their books based on new accounting rules. So the theory that gold companies can trade correlated to the price of the underlying is correct, however a dynamic exists that has the potential on a per company basis to materially affect that."

I find Mr. Netto's comments very insightful. I would make one further point: if the mark-to-market accounting losses are temporary and will recover next quarter, we can expect their stock prices to revert. This is exactly the cointegration scenario that I talked about -- a price reversion after some period of time, but not a day-to-day or week-to-week correlation.

Are political futures markets really predictive?

Today I will take a brief break from quantitative trading in the financial markets. Instead, I will take a critical look at political futures markets. There has been a lot of enthusiasm lately for such markets (e.g. www.tradesports.com, based in Ireland, is the most popular one.) Media pundits and scholars alike have often said that these markets offer a better prediction of election outcomes than opinion polls, sometimes claiming that they beat polls three-quarters of the time. I have been an avid participant in these markets, but I would like to offer a contrarian view: I believe that these markets often follow, rather than predict, events. The so-called “predictability” of these markets is often ill-defined. The prediction changes constantly over time, and so depending on when you take a snapshot of the markets, you can always find an instant when, retrospectively, the prediction matches the actual election outcomes very closely.

As an example, I watched with amusement the tradesports.com futures market prediction of the Virginia Senate race between Democrat Jim Webb and Republican George Allen. This is one of the two close races that will determine the control of the Senate. For months, the market predicts that the Democrat will lose (the probability of winning, which is the same as the price divided by 100, is always below 50% until the beginning of November). Then in November, the market began to see the light, and started to predict a Democratic win. See the chart below.

But look what happened on the night of the election:

As the vote counts started to be released, the market first thought the Republican was going to win, driving the prices down to the teens. That was due to the votes from the conservative southern Virginia, which were the first to come in. Then, as the vote counts from the more liberal northern Virginia were published at around 11:30 pm, the prices shot up to above $60, and continued on to over $80. Clearly, the market does not know more about the future than your average news anchor.

As someone interested in the predictability of election outcomes based on futures markets, this raises a serious question. What is the proper time to take a snapshot of the market? Should it be 1 month before the election (in which case this market prediction failed, presuming a Democratic win after the recount)? Or should it be 1 week before the election, in which case this market prediction succeeded? And without an answer to this question, how can one claim whether the prediction is accurate or inaccurate?

Cointegration is not the same as correlation

A reader asked me recently why I believe that energy stock prices (e.g. XLE) are correlated with crude oil futures front-month contract (QM). Actually I don’t believe they are necessarily correlated – I only think they are “cointegrated”.

What is the difference between correlation and cointegration? If XLE and QM were really correlated, when XLE goes up one day, QM would likely go up also on the same day, and vice versa. Their daily (or weekly, or monthly) returns would have risen or fallen in synchrony. But that’s not what my analysis was about. I claim that XLE and QM are cointegrated, meaning that the two price series cannot wander off in opposite directions for very long without coming back to a mean distance eventually. But it doesn’t mean that on a daily basis the two prices have to move in synchrony at all.

Two hypothetical graphs illustrate the differences. In the first graph, stock A and stock B are correlated. You can see that their prices move in the same direction almost everyday.

Now consider stock A and stock C.

Stock C clearly doesn’t move in any correlated fashion with stock A: some days they move in same direction, other days opposite. Most days stock C doesn’t move at all! But notice that the spread in stock prices between C and A always return to about $1 after a while. This is a manifestation of cointegration between A and C. In this instance, a profitable trade would be to buy A and short C at around day 10, then exit both positions at around day 19. Another profitable trade would be to buy C and short A at around day 31, then closing out the positions around day 40.

Cointegration is the foundation upon which pair trading (“statistical arbitrage”) is built. If two stocks simply move in a correlated manner, there may never be any widening of the spread. Without a temporary widening of the spread in either direction, there is no opportunity to short (or buy) the spread, and no reason to expect the spread to revert to the mean either.

For further reading:

Alexander, Carol (2001). Market Models: A Guide to Financial Data Analysis. John Wiley & Sons.