[The following article is part of a larger commentary, available here.]
After monitoring this calculation for a long time, I’m happy to announce this new and unusual arbitrage.
Pennies are composed of 97.6% zinc and 2.4% copper, with a total weight of 2.5 grams. After several years of rapid appreciation, copper prices have been stagnant for about 6 months, but zinc has been rising toward $4,000 per metric ton (or about 4/10 of a cent per gram). That places the value of the zinc at 0.994¢, and the value of the copper at 0.045¢, bringing to total cost of the raw metals to 1.04¢.
So if you collect pennies, melt them down, separate and purify the metals, then sell the metal on the public exchange, you make 4%. This is a new phenomenon, and may not last. I would expect to hear an announcement that the penny will be modified, replacing zinc with aluminum. This would bring the value of the metals down to less than 7/10 of a cent, and gives the government another couple years before they are forced to drop the penny as a unit of currency.
The new aluminum pennies will still be clad in copper, but will feel much lighter. You heard it here first.
Cointegration typically uses the price information for two related securities, and provides relative value signals. As with traditional technical trading strategies, changes to the fundamentals create a risk of bad relative value signals. With 2-security cointegration, this risk is doubled because changes to the fundamentals of either company can skew the relative value signal. However, this problem can be cut in half by creating baskets (portfolios) of securities and running the cointegration analysis with each security against the basket. This effectively generates signals which are skewed only by changes to the fundamentals of the individual security. Additionally, the required correlation matrix for the permuted set of security combinations can be replaced by a single vector of correlations – greatly improving calculation efficiency and extending the analysis processing potential.
To improve upon normalizing data to % changes, factors typically associated with beta may also provide better signalling data. For example, as the size of a company grows over the course of a few years, its price volitility may fall. Similarly, as market cap grows, the price change correlations may increase relative to larger cap baskets and decrease relative to smaller cap baskets.
By backtesting, optimal trigger strengths and bet sizes can be measured, however, given the correlation coefficients, volitilities, number of positions, and risk preferences, probabalistically optimal bet sizes may provide better results.
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