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HOW THE PROS MANAGE RISK
April 18, 2011
Every manager we look at says they manage risk, and that risk management is their number one priority – but talk is…as they say…cheap, and the real question when analyzing a managed futures program is not IF they manage risk (anyone who makes it to the point of offering a program is smart enough to know to SAY they manage risk), but HOW they manage risk.
So what are the main methods professional Commodity Trading Advisors (CTAs) use to diversify their programs and manage risk? We touched on one aspect of how managers manage risk in our blog post talking about Corn’s limit up move the other day, and here outline more of the methods and overall philosophies built into the programs we track.
Money management
The number one tool used by any and all CTAs we deal with or analyze is some sort of money management philosophy. Money management can sound daunting, but in its simplest form it boils down to position sizing – or how many contracts to trade, when to increase, when to decrease, when to take profits, and so on.
The most common form of money management is a technique some call “Risk Budgeting”, whereby the CTA trades varying numbers of contracts in different markets in an attempt to equalize risk across the markets (or separate trades) in the portfolio.
Consider a trader who gets a trade in Palladium, and then another a short time later in Sugar. If the trader correctly calls both markets correctly (capturing, say, a 5% move in each), but only does a single contract in each – the trader will have made $3,675 in Palladium, but just $1,355 in Sugar.
To combat this, instead of just trading the same number of contracts on each market they get a signal on, they look to risk the same percent of equity on each trade. To do this, a CTA will look at the risk on the trade under consideration, (which could be a function of a stop above/below a swing high/low, calculated using the ATR, or the distance from the entry to the moving average of prices) and divide that risk into their risk budget to get the number of contracts which should give them the desired total risk if their exit level is breached.
Using Palladium and Sugar from our example above; the Average True Range as a proxy for risk, and the assumption that we wish to risk 2% of our equity on each trade for a $1 million account – we can calculate the following number of contracts to be traded: Palladium = 8, Sugar = 20. Using this number of contracts with the same 5% gain example above, the account would then see $29,400 in Palladium, and $27,100 in Sugar – a much more even split of the profit between these two markets.
To see this graphically, we have listed how many contracts of each market listed below a fictitious managed futures program using ‘risk budgeting’ would trade to keep risk per trade at 2% of equity.
There is much, much more to money management, including the use of dynamic stops and profit targets, scaling into or out of positions, and limiting exposure to any one market or sector to a specific level. There are even some programs which limit the amount they are willing to lose in any one month, whereby they will shut down trading for the remainder of the month and then reinstate the following month.
But in our opinion the most important part of money management as it relates to managed futures programs is the prevalent use of dynamic risk budgeting type methodologies whereby the number of contracts traded in each market varies depending on the volatility of that market, distance to the stop level, and amount of equity in the account.
Market Selection
Another prevalent risk method used by nearly all CTAs we come across happens before they even place a trade – market selection. While most of us take it for granted that certain programs trade certain markets, the markets a managed futures program is involved in does not come about by chance.
Managers test markets to insure there is adequate liquidity, volume, and ease of execution for their type of trading. One of the easiest ways to reduce risk for a manager is to simply not trade a market. Lumber, Pork Bellies, and now defunct futures contracts like Muni Bond futures are/were simply not traded by most managed futures programs because of low volume and inadequate liquidity.
Another big risk tool used by nearly all managed futures programs (and indeed all of the CTAs we recommend), is the use of exchange traded futures. How is that a risk tool, you ask? Easy, the use of exchange traded futures removes counterparty risk, with the exchange guaranteeing the other side of any trades you put on. Think of all the money Goldman Sachs was set to lose if the government didn’t bail out AIG and you can see the importance of removing counter party risk.
And now we move on to types of diversification and risk management which aren’t common to all managed futures programs, but common enough to warrant individual mention. We have listed them in order of popularity and prevalence among the programs we track and analyze.
Market Diversification
The tool the bulk of the managed futures industry relies on more than any other is the power of diversification amongst different markets. The explanation for it is as simple as the old saying: don’t put all your eggs in one basket. And can be as advanced as complicated charts showing cross correlations amongst markets (included below).
Whether it is a program like industry bellwether Winton Capital and their reported 100+ markets traded, or a more accessible program like Clarke Capital and their 27 markets – the calling card for multi-market systematic program (formerly known as trend followers) is their access to and monitoring of a wide array of markets across numerous sectors: Energies, Grains, Softs, Metals, Meats, Currencies, Interest Rates, Stock Indices, and more.
[Source: Welton Investment Corporation]
One of the reasons most managed futures programs cover such a wide array of markets is that they don’t know where the next trend will come from; they just know it will come somewhere. By following many markets, they can be assured of being in the right place at the right time when a market decided to start trending.
Or more correctly put when considering it from the risk side, they can have comfort in knowing that the same catalyst is highly unlikely to cause loss making trend reversals and/or false breakouts in markets as diverse as diverse as Japanese Govt. Bonds and Natural Gas (again, per the ultra scientific – ‘don’t put all your eggs in one basket – property).
Why this works is a bit more advanced, but boils down to the fact that markets do different things at different times, and don’t all go up and down together (most of the time). The statistic for measuring how closely tied these up and down movements are is called correlation, and we have included a chart showing the cross correlations between a broad mix of markets below.
As a refresher, correlation is a statistical figure with values which range between -1.00 and +1.00, meant to show how inter-related two sets of data (in this case monthly % returns) are. If they have a correlation of 1.00, they are perfectly correlated, meaning when one market rises 1%, the other will do the exact same, and when one loses -1%, so will the other. If they are at -1.00, they are exactly opposite; with one making the exact opposite amount the other loses each day, and vice versa.
Market diversification seems to work for managed futures because the predominant color of the chart below is not dark green or dark red, but a shade of yellow – meaning that most cross correlations between markets are somewhere in between -0.25 and +0.25, which are readings in the range better known to statisticians as non correlated. Indeed, the average correlation coefficient across the 930 separate readings in the correlation matrix is a low 0.14.
Other methods utilized within market diversification include Sector Diversification, by requiring a certain number of markets in each sector be included and/or restricting exposure to a certain sector to only so much risk. And some managers (especially larger ones) will use a unique type of market diversification where they spread out the large number of contracts they need to execute amongst several different contract months for the same commodity (buying June, July, August, and September Crude Oil for example).
Nearly all managed futures programs, with the exception of single market stock index programs and specialty programs focusing on markets like Gold use market diversification, and it’s easy to see why. The ability of different markets to zig while others zag, creates the potential for increased returns and lower volatility and drawdowns. The only con…that correlation is dynamic and ever changing, meaning that markets and sectors previously non correlated can become highly correlated over short periods of time (see 2009), removing the benefit of diversification and actually making a broad portfolio more risky.
Model Diversification
Next up is strategy, system, or model diversification – which are all ways of describing the practice of utilizing several different trading models within a single program. This can be as simple as having two trend following models, for example: one which uses a 200 day look back for entries and exits, and one which uses a shorter 80 day look-back. Or as advanced as billion dollar manager QIM who has 100-500 models active at any one time.
While model diversification seems to have started out with the practice of slightly different themes on the same model (different moving averages as parameters for the same model, for example); we have seen more and more of seemingly conflicting models such as trend following and counter trend working on the same markets in the same account.
The appeal for the use of different strategy types is simple enough to understand, when considering that the market is not always trending, nor always oscillating, and so on – meaning that there will be times when a model meant to do well in a trending or oscillating environment, for example, will not do well. The obvious fix for many managers as a way to reduce the risk of a poor environment for one of their models, is to add a model which can do well in that environment.
It is yet another example of not putting all your eggs in one basket, yet this time your eggs are not the ‘what’ you are trading, but the ‘how’ you are trading. Advanced stuff, to be sure.
The great thing about this strategy is it allows you to make money in all kinds of markets. Looking back at performance, you’re likely to see a smoother equity curve overall [past performance is not necessarily indicative of future results]. However, the strategy, potentially, can suffer from over-optimization. It also may not be as diverse as you think. At times, you’ll notice that these models are really just varying timeframes of trend following.
Programs Using This Strategy:
APA Modified Program, APA Strategic Diversification Program, 2100 Xenon Managed Futures Program, 2100 Xenon Fixed Income Program, Accela Capital Management Global Diversified, Accela Capital Management Global Diversified, Dominion Capital Management, Futures Truth Company MS4, Futures Truth Company SAM 101, III Associates III Futures Neural Network, Integrated Managed Futures Corp. Global Investment Program, Integrated Managed Futures Corp. IMFC Global Concentrated, James River Capital Corp Navigator Program
Time Frame Divesification
Time frame diversification is exactly what it sounds like – mixing up trading by running the same models on different time frames (10 minutes, versus hourly, versus daily, versus weekly – for example). In addition to the length of time the model considers (10mins, daily, etc), time frame diversification also shows up in how long the average trade is for a model. A model run on 10 minute data may have an average hold time of less than one day, for example, while a model run on weekly data would likely have an average hold time measured in months.
The table below shows different performance statistics of the exact same trend following model run on the same market (2yr Notes) as run on four different time frames, to highlight just how different the results can be when just changing a single input – time.
[The following is an example of the topic discussed and does not represent trading in an actual account]
Because of these different possible hold times for a trade, time frame diversification also allows for a type of hedging, whereby the program may be long a market on a longer time frame, but see a shorter time frame initiate a short trade (thereby hedging the long position). This would seem like a no win situation, as you are both long and short, which net out to flat – but it is possible to make money (and lose) on both sides of the trade, because they can exit their respective trades at different times (after the market has sold off, ideally, in the case of the short; and after it has rallied back, in the case of the long).
Programs Using This Strategy:
APA Modified Program, APA Strategic Diversification Program, Dominion Capital Management, Futures Truth Company MS4, III Associates III Futures Neural Network
Trade Structure (Loss defined by trade parameters)
This is a specific type of risk protocol used almost exclusively by option trading managed futures programs, and is a direct function of the products they choose to trade in their programs.
The easiest example is a trade in which you buy a Put. Because of the mechanics of option trading, which we don’t have the space to get into here, buying a put has a defined risk equal to the price you pay for the put. You can’t lose anything more than the price you paid for that put.
Likewise, if you set up more advanced option spread strategies such as an iron condor, bull call spread, straddle or the like – the trade structure itself can be the method of controlling risk. This ability to define the risk of a trade before entering it, to define it by the very order placed – is likely the very reason so many traders, novice and professional alike, are drawn to options.
The benefit to this kind of strategy is its defined risk, but that also limits the potential return – as if you are buying insurance to define your risk.
Programs Using Strategy:
Cervino Capital Management Llc Diversified Options 1x, Cervino Capital Management Llc Diversified Options 2x, Clarity Capital Management, Crescent Bay Capital Management Balanced Volatility
Delta Neutral (Spread)
While this is more of a trading strategy than risk methodology, there are some managers who will initiate spreads to reduce risk. The logic for using spreads as a risk tool usually stems from a feeling that a position in the opposite direction in the same market in a different contract month (calendar spread) or a position in the opposite direction in a highly correlated market (intermarket spread) gives the manager greater flexibility in handling the trade.
The logic behind using a spread to diversify risk is that the directional risk of the market can be replaced by the relational risk, with the thought that if you have a long June Crude Oil contract and Short December Crude Oil, they aren’t likely to diverge greatly (with both going down or up in tandem, although not by the same amount).
In addition, for markets which are locked limit up or down, spreads in highly correlated markets not also locked limit may be initiated, as well as spreads using synthetic positions initiated via options trades, which don’t lock limit when the underlying futures of the options market do (not sure who snuck that one past).
There are two advantages to this strategy. First and foremost, it can be far less volatile than a directional program, as spreads generally move just a fraction of the amount the underlying market moves. Second, the use of spreads to control risk can also lead to profits when traditional managed futures strategies struggle, as spreads have their own trends. However, there is also the somewhat hidden potential to lose on both sides of the trade.
Programs Using Strategy:
Emil Van Essen Spread Trading-High Minimum, Emil Van Essen Spread Trading-Low Minimum, Emil Van Essen Spread/Index Program, Rosetta Capital Management, Rosetta Capital Management Macro
Manager Skill/Experience
When all else fails – go with your gut. This is a dangerous game to be sure, but some discretionary programs do at times rely solely on the manager’s skill to manage risk. A CTA using this method may get out at a loss of -3% some times, then hold on much longer another time if they ‘feel’ a market is unlikely to go any further, etc. They may sometimes load up on a single trade, and sometimes spread their risk between several trades or markets.
The allure of this method, even if it is merely an overlay over automated risk controls, is the human touch. The world doesn’t quite look like the futuristic vision from the movie Terminator yet, where the computers have taken over; and it is extremely difficult to program into a model every eventuality that may pop up in the markets. Indeed, there will be ones you were unable to foresee even if you had the time and skill to program all the known risks into your models. So at some level, it makes sense to have some risk control be in the managers hands, and have the manager be able to take risk off the table even if it is based purely on a ‘gut’ feeling.
The advantage here is that the program isn’t tied to one strategy. The manager can be flexible with his trading and turn a profit in any environment… theoretically. The main problem is that it becomes very difficult to identify strategy drift unless it’s blatantly obvious, and by that point, it may be too late to prevent severe losses. You may also see the manager get married to a trade, as we saw with Dighton do with crude in late 2008/ early 2009.
Programs Using Strategy:
Dighton Capital CTA Ltd (Aggressive Trading Program), Dighton Balanced, Mesirow Financial Commodities Absolute Return Strategy, Mesirow Financial Commodities Low Volatility Absolute Return Strategy, Global Ag
Conclusion
Turns out there are more than a few ways professional commodity trading advisors manage risk and diversify their portfolios, with everything from the what, how, and when to trade structure and gut feelings.
The best part – there will be more in the future as manager’s learn more about their programs, the markets, and risk in general. One of the biggest advantages managed futures have over other types of investments, in our opinion, is this evolutionary property, where you aren’t just investing in what the manager knows and how they control risk now, but also investing in what the manager will find out and how they will diversify risk in the future.
