NG Spreads returns, a reliable earner.

I was reminded recently it has been a while since my last posting.  So here is a quick analysis of a strategy that works pretty well.

Much has been written on the changes to the NG market structure over the past few years; the influence of financial investors and the impact of new technologies.  One thing that hasn’t changed is the reliable earning power of spread trading.

The complexity in modeling returns from futures trading, particularly spread trading means that the best way to characterize the profitability of these types of strategies is in terms of $/contract.  This can then be directly compared to the $/contract of margin requirements for a reasonable perspective on the investment return.  For this analysis, I used recent data from the CME website for initial margin by strategy type for the NG contract:

http://www.cmegroup.com/clearing/margins/intras.html#e=NYM&a=NATURAL+GAS&p=NG

But first I present the $/contract return side of the equation for some standard spread strategies.  These analyses are all based on rolling a X-Y position in NG futures, with the contract roll being on the penultimate day available.  In the usual convention X is the nearby contract and Y is the further out contract.  The ratio is 1:1 in contracts.  Other roll alternatives may make more sense but this is conveniently tradeable given the TAS contracts available.  Other hedge ratios may be more statistically defensible; 1:1 has the advantage of being available as a directly tradable spread (also via TAS), and the analytics is trivial.

Looking at the 5 year historical performance of N-(N+1) spreads we see the following results:

spread	Daily avg	Daily stdev	Annualized avg/stdev
1-2	13.27	232.51	0.90
2-3	-4.89	172.79	-0.45
3-4	-10.81	128.42	-1.33
4-5	-3.90	126.33	-0.49
5-6	-1.30	97.10	-0.21
6-7	-5.48	108.67	-0.80
7-8	-9.22	123.99	-1.18
8-9	-17.55	179.98	-1.54
9-10	-2.78	129.50	-0.34
10-11	0.96	123.55	0.12
11-12	-7.32	755.13	-0.15
1-3	18.16	193.36	1.48
1-2 -(3-4)	24.08	232.20	1.64

This table shows the average daily return and standard deviation in $/contract.  So for example, holding a long prompt contract and short 2^nd month contract since January 1, 2007 made an average of $13.27 per day with a standard deviation of $232.51 per day.  However on an annualized basis this translates into $3,317 and $3,676 respectively or a profit to risk ratio of about 0.9.  Not great, but a nice diversified addition to an existing strategy. 

The highest individual 1 month spread contract return is in the 3-4 contract.  Specifically being short this contract, which yields a profit to risk ratio for the year of 1.33.

By extension, these strategies can be combined relatively easily: 

Long 1-2 plus short 2-3 (which is equivalent to long 1-3) has a 1.48 profit to risk ratio. 

Long 1-2 plus short 3-4 has a 1.64 profit to risk ratio. 

What is nice about these strategies is that the capital (margin) commitment is not immense.  An individual spread requires less than $1000 per contract of initial margin.  Drawdowns have been limited in the past 5 years as the cumulative profit chart shows:

Overall, this is a nice uncorrelated strategy to add to a portfolio.

Retail Commodity Investors - A Bleeding Indicator?

Retail Commodity Investors – A bleeding indicator?

With the recent “flash crash” in commodities it seemed like a good time to delve into the question of how retail investors react to commodity prices and their investment flows into and out of commodities.  Some of the single commodity investment pools (a.k.a. ETFs) publish their daily issuances and redemptions data.  This allows a perspective into how retail investors are reacting to commodity prices on a daily basis.

Taking UNG as an example, we can observe daily on their website the following information, for example today on 5/10/2011 the report shows:

As of 05/09/2011, subject to change

Pending Trades Shares Created/Redeemed

2,700,000

Units Outstanding 

 172,497,828

If we gather this data over time we can create a time series of redemptions and issuances which can be lined up with prices.  Hypothetically treating the entire pool of issuance as a single retail investor, this investor is at the margin buying and selling shares each day in reaction to prices.  The question is: does this hypothetical investor do better or worse than holding their position?  Also is there informational value in the funds flow?

The “as of” date for the pricing of daily holdings suggests that today’s report shows transactions that were done on the prior day 5/9/11.  So the incremental purchase of $2.7m shares was done at yesterday’s closing price for the fund, effectively the last trade price of $10.77.  This would have cost the investor a notional value of $29.079m.

When we track these incremental in/out flows cumulatively over time we can see that the hypothetical incremental investor has had pretty bad timing over most of the last year or so.  The incremental flows cost the investor about $1.4b over 2010, spread pretty steadily over the year.  In other words, investors consistently bought high and sold low in their redemptions and issuances of this ETF.  Intriguingly, this investor, (whether lucky or smart,) has recovered roughly half of this amount over 2011. 

Further when overlaid with the fund’s NAV, we can see that investors have made money exchanging shares when the NAV was falling  in early 2010, lost money while it was relatively stable throughout 2010, and then made money as it has fallen in 2011.

(Note that all these results have been adjusted for the reverse split that took place earlier this year.)

This also helps answer the second question of whether the retail investor flows have informational content regarding the market.  The annualized Sharpe ratio of the investor’s activity above is -0.9.  Not great as an investment strategy, but maybe there is informational content to the investor flow information?

Turns out that if you could know investor flows today and act on it there is a 0.8 Sharpe strategy in a 1 day hold long / short position based on investor flows.  Not a great stand alone strategy but useful as an overlay to a commodity strategy.

Commodities vs. commodity stocks

Just got back from a couple of months vacation in NZ so brief posting this month.

Another common field of mean reversion analysis relates to commodities and commodity related stocks.  In this analysis, I compare the prompt month crude oil price, CL1 to the price of XOP, an ETF that invests in oil & gas exploration & production companies.

As expected, these two prices track each other fairly closely over time:

 A similar analysis as before, performing an ADF test suggests that over its history the ratio of price series has exhibited mean reversion characteristics with a half life of 37 trading days or approximately two months.  This isn’t ideal for trading; it would be preferable if the relationship exerted itself more strongly.  Nevertheless with an ADF test statistic of 4% the evidence seems strong.

This relationship can be generalized to other commodities and commodity equity groupings, providing a guide to over/under valuation of commodity equities in relationship to their corresponding commodity prices.