Managed futures: a bright spot amid the gloom

For investors surveying the wreckage and gloom that was 2008, a few shining beacons stand out. One positive sparkler is the alternative investment strategy of managed futures. This was one of the very few investment styles to prosper and, generally, its managers did very well.

The BarclayHedge CTA Index, an accepted industry bellwether for managed futures managers (known as CTAs), made a double digit return in 2008, with some managers producing 20 per cent plus for investors. Here, we examine the philosophies and techniques that underpin a trading style that has been around for more than a quarter of a century but is little understood.

There are several aspects of managed futures trading that combine to produce a potent mix. Trading is done with contracts costing a small fraction of their face value, thus allowing for the use of leverage without the need for outside borrowing.

Futures and forward contracts are available for an extremely broad range of underlying assets, from currencies to commodities and equities traded in hundreds of markets worldwide. This allows, simultaneously, for increased opportunities and, through diversification, a potential reduction of risk.

The highly liquid nature of most of the contracts means that efficient and transparent trading can take place, mostly on exchanges and the interbank markets.

More than one approach

They rely on fundamental analysis by studying factors that affect the supply and demand of a particular asset. These may include factors such as economic strength, governmental policies, domestic and foreign political events and even — if it’s an agricultural asset such as wheat — the weather.

Fundamental analysts believe that markets have a fair value with which they must converge over the medium term, and they seek to identify and profit from periods when they think a valuation has fallen out of line. As discretionary traders rely on human input, the number of opportunities that can be traded is smaller compared to systematic trading strategies.

Nevertheless, this can be very successful, but it does depend not only on the manager’s skills, but also on keeping up those skills in all circumstances, including moments of market or personal stress, into the indefinite future.

This is a tall order. What if a manager is under considerable pressure? What if they leave their current firm and move to another one, or retire? If they have a string of losses, will they start to doubt their core beliefs and lose confidence? In contrast, computer-based models don’t suffer from these potential weaknesses. Computers act without emotion, don’t go to work for someone else and don’t get tired or stressed.

‘Quantitative’ traders use statistical analyses of data relevant to a market to look for patterns that might be replicated in the future. The most important type of data used is the market’s price history. In particular, this type of analysis is based on the belief that markets do not always move randomly, and from time to time they will exhibit strong trends in one direction or another. This has given rise to a highly popular and successful technique-trend following.

Quantitative trading has been in the ascendancy for some years and dominates the managed futures arena. This has come about for several historical reasons, but it was the emergence of comparatively cheap computing power in the 1980s that really gave impetus to the quants, as they are known in the trade.

Some traders saw the opportunity to harness this powerful new tool. They developed the idea that the history of an asset’s price or value — whether a commodity such as oil or an exchange rate between two currencies, or even a country’s short-term interest rate — could be viewed simply as a series of numbers.

Numbers are, of course, eminently suited to mathematical and statistical analysis, and this is most efficiently carried out by computer. Coupled with the increasing use of mathematics in the world of finance generally, this was a potent combination.

Developing a trading strategy

A very simple example of a hypothesis might be: if a market trends (moves in the same direction) strongly for a given length of time, this is likely to be followed by a sharp reversal. Using historical data, the manager runs tests to see whether this hypothesis has been true or false in the past.

It’s vital to make an assessment of how persistent this situation is likely to be in the future and, in particular, a manager has to be very careful not to draw incorrect conclusions. Sometimes, while there seems to be a statistical connection between two events, it is just a coincidence.

For instance, it might be historically true that every time it rains for more than two hours in Sydney the price of gold drops the following day, but it is clear that the first does not cause the second. There has to be a plausible and logical link between two events.

This is where a manager’s experience comes in, and it is one reason why the initial hypothesis needs to be extremely well thought out.

Once a trading system — which is essentially a program for making buying and selling decisions — has been designed, there are a number of steps that have to be taken before a research idea can be used to manage client money.

Different managers will use different approaches, but they might include researching in a full portfolio context, paper trading and proprietary trading.

Typically starting with a single market (although there can be exceptions to this as some strategies by their very nature involve trading in more than one instrument simultaneously), the research process entails analysing the statistical profitability and reliability of the proposed idea. If the proposed system or strategy had been put into practice at some time in the past, how would the returns and risks have looked? This is a complex question. It’s important to consider factors such as how often it would have been successful, how large the average winning and losing trades are, and so on.

Once a strategy has been thoroughly tested on a single market, the tests can be broadened to include a range of markets. If this is successful, it, in principle, enables a manager to construct a portfolio that will, almost without exception, be less risky than a single market.

At this stage, a hypothetical program can be run using real-time price data to see how the strategy behaves in practice. Theory and practice can then be compared. Do they match? Can any lessons be learned and fed back into the research?

When the system has been, if necessary, remodelled and retested, many managers will carry out proprietary trading, using their own money. This is a crucial stage, as something developed on computers needs to stand up in the real world.

Research is one thing, trading is another; a successful manager needs to be an expert in both and use lessons learned in one area to reinforce the other.

The size and efficiency of markets, usually collectively known as liquidity, can play a major part in the success of a trading strategy. A complicating factor is that liquidity changes in response to overall market conditions.

This was particularly acute towards the end of 2008, when the burgeoning economic crisis meant that liquidity in many important markets almost completely disappeared, making it hard for some types of strategies to function. One of the advantages of managed futures is that, with trading usually carried out on highly liquid exchanges, these effects tend to be less pronounced than for many other styles.

A good CTA will understand these effects well, and will feed back market knowledge and experience into the development of its strategies.

The rationale behind managed futures is strong and clear. Markets trend and other inefficiencies persistently occur and can be exploited. A very broad range of markets exist and can be easily traded. Non-traditional trading techniques provide many more opportunities than can usually be found in a traditional fund, particularly as managed futures aren’t dependent on rising markets in order to do well.

While developing a successful trading strategy is complicated and requires intensive research and development — which needs to be constantly refined to take into account new markets and conditions — a successful managed futures fund has many advantages.

Over the years, the ability of managed futures funds to produce good medium to long-term returns has been clearly demonstrated, as the chart shows. Equally important is the potential ability of such funds to reduce the overall riskiness of a diversified portfolio.

This effect occurs because the factors that allow managed futures funds to make a positive return are often different to those for funds based on traditional assets such as equities and bonds.

Traditional and non-traditional funds therefore frequently behave differently to each other and produce profits at different times. This acts to smooth out the performance of a diversified portfolio that contains both types, and hence lessens the impact of downturns.

New markets are opening up and being developed worldwide in response to the new factors driving the global economy — climate change, demographics, and energy, to name just a few. If anything, the investment world is more complex than it has ever been and, consequently, the challenges and opportunities are greater.

One thing remains constant: the best prepared and most committed managers will be those who adapt most successfully to this new world. Managed futures looks set to build on its successes and to remain an important part of the investment management world. That, it seems, is definitely a trend worth following.

John Andrews is chief writer at Man Investments, based in Pfaeffikon, Switzerland.