The strategy
Systematic Technical Futures strategies utilize computerized mathematical models to generate buy and sell decisions. Trading is, to a large degree, systematic and occurs mostly in highly liquid markets with low transaction costs. Models are based on quantitative analysis of technical factors and indicators. The most typical examples of this class are trendfollowing or countertrend models, which are based on momentum indicators.28
Almost all systematic models have a time horizon or a combination of different time horizons defined by the inherent time constants of their indicators. For long- term models, momentum-based trendfollowing is typically most widely employed.
Systematic long-term models are usually rather simple; they use a variety of differ- ent moving average (momentum) indicators, which help to detect extended price trends. Most models have been optimized and back-tested for a number of years.
The majority of trades executed with this strategy are unprofitable. Nevertheless, such strategies can generate positive returns because they close losing positions quickly (‘cut losses’) and remain in profitable trades (‘let profits run’).
Short-term models use a larger variety of statistical tools including momentum and countertrend trading techniques, breakout indicators and pattern recognition.
More exotic analysis is sometimes performed with the help of non-linear models, neural networks, genetic algorithms and frequency models. The models try to exploit statistically measurable short-term market inefficiencies.
E X A M P L E S
Examples of short-term Systematic Technical trades are:
■ short-term trendfollowing and countertrend following with automatic stop-loss limits
■ directional volatility positions executed with options
■ systematic equity or commodity index (e.g. GSCI) arbitrage
■ trading based on a trained (optimized) neural network or other applica- tions of ‘artificial intelligence’ or non-linear or time series analysis.29
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Sources of return
The distinction between long-term Systematic Active models and Systematic Passive models can be blurred. Their sources of return are quite similar. As with most Managed Futures strategies, parts of the returns of Systematic Active strategies are based on the risk premium for providing liquidity to Futures and derivatives markets and providing commercial hedgers with the opportunity to transfer their natural price risk. But active models also have a skill component. The correct systematic prediction of trends requires good model development abilities. Short-term model- based trading aims at exploiting market inefficiencies and inhibited information flow in financial markets. Returns are here a function of the manager skill in devel- oping models that detect complex predictable short-term price patterns.
Most Systematic Futures models are of a trendfollowing type and many managers and producers of these models claim that trendfollowing models generate inherent returns as a result of mass psychology, i.e. crowd behaviour (a clear violation of the
‘weak’ form of the efficient market hypothesis). For most financial markets, however, academic research provides no clear support in favour of these claims for trend per- sistence in security prices.30 Likewise, there is a claim for currencies to exhibit serial correlation, but again, this is not convincingly supported by empirical studies.31 Risk factors
Managers usually have clear exposure to certain markets. Therefore, the predomi- nant risk of systematic Futures and Currency strategies is market risk, i.e. the risk of the market moving against the established position. Trendfollowing strategies in particular are exposed to the risk of steady losses over an extended period of time in non-trending, directionless market environments, which exhibit numerous price reversals (‘whip-saw markets’). This is illustrated in Figure 3.9 (left-hand side).
The structure of financial markets changes continuously and models built in the past might not be suited for markets in the future. Systematic models thus bear the risk of structural changesin the underlying markets. It often occurs that models that have worked very successfully for a period of time start showing persistent performance problems. Systematic long-term trendfollowing models had excellent performance during the 1980s and early 1990s when financial markets showed strong directional
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trends. Fewer trends were observed in the later 1990s, and the models accordingly showed much lower returns. In 2000/2001 performance was again good for many Futures strategies. But it has to be noted that the variability of returns between managers increased during 2000–2001, i.e. managers diverged in performance.
Systematic trading strategies rely solely on the functioning of models that were developed as a result of research efforts performed during some past time period.
A very important risk factor is model risk, which refers to the risk of the model being flawed or unsuited for the current market structure. Most models are opti- mized on past data, i.e. the parameters of the model are determined such that the model shows the best performance for particular periods in the past. This is a dan- gerous undertaking and bears the risk of over-fitting, also referred to as ‘curve fitting’. Provided with a sufficient number of free parameters, every model can be tuned such that it shows excellent performance on any (even random) data set!
However, when used with data that the model was not optimized for (‘out of sample test’), performance may be significantly worse, sometimes rendering the model useless. The more complicated the model and the more parameters involved, the higher the risk of over-fitting. Simple trendfollowing models bear less risk of over-fitting than complicated ‘non-linear modelling approaches’ that have a lot of different parameters. The investor should always inquire about the optimiza- tion procedure that was undertaken during the development of the model and about the performance on out-of-sample data or during real-time trading.
Systematic Futures and Currency trading bears operational risk. Most models are run on computers and their failure or programming errors (‘bugs’), electricity breakdown etc. can lead to trading losses. Furthermore, the trading of a large number of different positions bears execution risk.
Because approaches are very diverse, it is difficult to determine general market conditions in which all the different models perform consistently well or particu- larly poorly. Strategies which exploit short-term market inefficiencies perform best in less developed and therefore less efficient markets. Most strategies perform poorly in directionless ‘whip saw’ market conditions. Long-term trendfollowing models in particular need persistent trends for good performance.
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