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Opalesque Futures Intelligence

Futures Lab: Within the managed futures industry there are many approaches and techniques, with different strengths and weaknesses. A study from Man Investments highlights some key differences.

Tuesday, April 21, 2009

Understanding Managed Futures Sub-Strategies

Managed futures covers a wide range of trading techniques and approaches. These perform differently in varying market environments and have different advantages/disadvantages. Understanding the differences should help investors make better investment decisions.

The discussion of sub-strategies below comes from a longer and more detailed study by Man Investments, an arm of Man Group. The edited excerpts highlight the main distinctions. A previous issue of OFI presented a review of risk controls from the same study (February 24, 2009).

The terms managed futures and commodity trading advisors are used interchangeably here.

The first managed futures managers used classical technical trading patterns that were fairly simple, including “head and shoulders”, “support and resistance” and “break-out”. Head and shoulders, for instance, is a chart formation that rises to a peak and subsequently declines, then rises above the former peak and declines again and finally rises once more.

By the early 1990s new analytical software and the technological revolution had brought entire libraries of technical indicators to the trading floor. Many indicators had become readily available and were offered by most financial data vendors. This sparked a wave of strategy testing and development among aspiring traders.

Computerization allowed traders to sample more data in a far shorter time period. This increased the scope of their trading, allowing them to perform such tasks as finding the most profitable moving average length for a price series.

By 2000, the success of systematically driven CTAs was attracting researchers from a variety of scientific fields. They started to research continuous trading, price forecasting and portfolio optimization, which contributed to the continuing success and sophistication of managed futures traders.

As a result, the technical trading concepts that dominated the earlier stage of the industry's development have become less important and are now used in combination with more scientific approaches.

In the past decade, technological and scientific progress have massively increased the spectrum of strategies available to managed futures traders. Investors can build portfolios containing diverse sub-strategies.

Trading Methods

The managed futures universe can be broadly divided into systematic and discretionary strategies. Systematic strategies make use of historical price data and/or historical relationships that can be tested and which may help to anticipate future price movements. These strategies rely heavily on computer generated trading signals.

Conversely, discretionary traders rely on the judgment of the managers and their expertise in a particular market to make investment decisions. Both methods have their advantages and disadvantages; however, they are complementary as they tend to experience losses at different points of a market cycle.

The more prevalent systematic approach relies on the application of statistical analysis to evaluate the movements of markets. Such information may include daily, weekly or monthly price fluctuations, volume variations and changes in open interest. System-driven trading represents the lion's share of futures trading volume.

CTA managers use a vast range of different trading techniques. The table below is not intended to be exhaustive but gives a snapshot of some of the techniques used today.

A sample of CTA trading methods

Serial correlation analysis Correlation of a variable with itself over successive time intervals. Managed futures traders use serial correlation to check to what degree past prices predict future prices.
Trading of volatility break-outWhen the percentage price move of an asset exceeds a certain threshold.
Position measuring based on volatilityPositions are sized as a function of volatility. In a high volatility market, positions are scaled down and vice versa.
Conditional executionTrading signals are placed in the market with pre-defined conditions attached to them, i.e. “buy at market” if “volatility is below x and price above 100”.
Term structure tradingAnalysis of interest rate differentials as well as term structure premia in the markets. One implementation of this is the popular carry trade in the currency markets.
Reversal pattern TradingPredictive strategy that tries to time significant market reversals.
Probability signals: position weightingIf statistically favorable probabilities of a directional move are measured, then position sizes are increased.
Algorithmic trading/high frequency trading/execution robotsTraders are replaced by computers which execute the trades automatically, often generating very short-term (intra-day) trading.
Non-parametric approachesReduce the reliance on a particular time frame in order to derive more stable performance. Trading is also spread out to a larger time frame in order to reduce market footprint.
Dynamic sector allocationAllocation to different market sectors such as commodities or currencies are adjusted in size, depending on opportunities and/or trends.
Behavioral financeStrategies that rely on persistent errors in the marketplace driven by biases of the human behavior.
Fundamental methodsEconometric models that value certain markets in relationship to the economic cycle.

Systematic trading is based on computerized quantitative models that use moving average prices, break-outs of price ranges or other technical rules to generate buy and sell signals for a set of markets. The approach relies heavily on computer generated trading signals to maintain a disciplined approach. With the emergence of electronic trading, the execution of these strategies is becoming increasingly automated.

A number of sub-strategies can be identified within the systematic category. Here we focus on just three:

  • Trend following – seeks to capitalize on medium to long-term trends in a variety of markets.
  • Trend reversal – seeks to capitalize on key turning points in liquid futures markets.
  • Contrarian (counter-trend) – aims to sell near market tops and buy at market bottoms.

The figure below shows the entry points for the three different systematic trading strategies. These can be used to construct highly diversified portfolios through the combination of multiple systems and time frames to reduce overall volatility.

Entry points of different systematic trading strategies

Time Differences

Systematic trend followers, a sub-category within systematic strategies, can pursue opportunities across many different time periods. The time horizon for medium-term trades lasts on average twelve weeks and long-term trades typically exceed nine months.

For example, a long-term systematic trend-follower might use a simple channel breakout strategy applied to gold futures, where a price channel is created using 30-day high and low prices. Long and short trading signals are generated as the price reaches the upper or lower boundaries.

By contrast, short-term trades typically last between three to five days, but they can be as short as intraday or as long as a month. These trades try to capture rapid moves. The managers base their activity on swift fluctuations in prices. They rely heavily on liquidity and high volatility for returns and typically have a low correlation to long-term CTA managers.

Differences in time horizon result in major differences in sub-strategy performance in various environments. In particular, strong, persistent trends benefit long-term CTAs but hamper shorter term traders because such periods tend to offer fewer short-term price fluctuations.

Taking Advantage of Market Changes

Quantitative and directional strategies respond to price movements across a diverse range of global markets encompassing stock indexes, bonds, currencies, short-term interest rates and commodities. For example, a systematic directional trade can generate returns by taking both long and short positions in a currency pair.

Managers will typically use a number of different signals in combination to determine trade entry and exit points. It is likely that allocation size will not remain constant during the life of a trade, as managers may vary the size and degree of leverage of their position based on contract volatility. In addition, managers have to roll the contracts, which results in small additional transaction costs being incurred.

As part of the risk management process, a manager will typically scale down positions when market volatility rises and vice versa. In addition, instrument, sector and regional exposures may be adjusted to stay within predefined limits.

In general, managed futures managers tend to view price trends as a function of supply and demand for a particular commodity or financial instrument or as shifts in risk premia for different asset classes.

CTAs can benefit from changes in market perception of risk and return. There are a large number of factors that can lead to such shifts in risk premia, such as the changing state of the economy, specific events, market news, or the emergence of information not yet incorporated in the current price.

Market participants have different expectations of the future, so adjustments to expectations and the inclusion of new information in the price tends to be a gradual process. CTAs often employ strategies that are constructed to take advantage of such movements. In most cases the strategies do not seek to understand the source of the change, but rather, aim to exploit the change in the prices.

Trend followers, for example, dynamically manage exposure to emerging trends. They attempt to identify the beginning of a trend, take a position and exit it as it ends. Trend reversal strategies look for turning points. The focus is not on what may have caused the movements.

Market drivers and events continuously change over time. Trying to find the unknown source of an event is an approach that eventually becomes less stable as a forecasting tool. However, by identifying changes in risk premia through prices, a strategy may become more robust over time. This is an important factor regarding the potential long term profitability of managed futures.

This article was published in Opalesque Futures Intelligence.
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