10Dynamics – June 2024
Introduction
In their own way, every investor believes they are systematic.
Each investor believes they’re operating a system to process information and produce actions that reliably result in a combination of outsized returns and controlled risks. However, even though an investor or manager believes that they are acting systematically – this does not make their strategy Systematic.
Systematic investing uses programmatic logic and models applied to data to take advantage of predictable movements in a wide variety of liquid markets to produce returns. These strategies are based on a bank of programmatic methods and sit on top of ever larger data sets to forecast price movements over a variety of time periods.
Examples of Systematic strategies include:
Equity Long/Short: capturing performance differences across stocks without being subject to overall market movements;
High Frequency: trading regularly on very short-term price fluctuations and dislocations;
Trend-Following: predicting medium-term future price movements based on recent market behaviour.
Systematic decision making
Discretionary strategies often make use of statistical analysis and quantitative information, but the final decisions lie with a person, and this can lead to significant limitations. These limitations typically stem from bias or emotional decision making from the person in the process. There are well-known human biases such as confirmation bias, recency bias and narrative bias, plus every day emotional decision making that undermines disciplined investing. Biased decisions are suboptimal at best, and outright wrong at worst.
A Systematic strategy will remove people from the loop wherever possible and prudent, to minimise the bias introduced into the investment decision making process.
Modern automated decision making employs a range of techniques on a spectrum from Knowledge to Search. Search-based methods explore deep into a decision tree and attempt to discover the best possible end state using sophisticated optimization methods.
Knowledge-based methods use a web of conditions, equations and multivariate models often derived from human experience or first principles which, when combined, allow the machine to take an informed view on the future based on the data. The benefits and limits of both will be explored in a future post.
Knowledge vs Search across various hedge fund strategies
Benefits
For institutional and professional investors, there are combined benefits to adding systematic strategies into your existing approach.
These include:
Enhanced return potential;
Risk management and;
Decorrelation.
Enhanced returns (over and above a 60/40 strategy for example) are derived from exploiting market inefficiencies, trends or anomalies which are impossible to detect with human analysis. These opportunities are too esoteric, complex, or short-term for a non-systematic strategy to capture.
Risk is systematically managed; for example, simple ‘hard limits’ can be introduced for metrics such as volatility, Value at Risk, and leverage.
Systematic strategies can also decorrelate an investor’s portfolio by opening them up to the widest possible range of liquid markets, and generating returns in a variety of macro-economic conditions including in unstable climates where other strategies perform poorly. Many strategies can operate across markets using consistent methods and some managers deploy algorithms that detect and exploit opportunities agnostically of the underlying market.
The combination of diversification, healthy returns and strong risk controls means that top systematic managers often have Sharpe ratios above one.
Risks
While Risk Management is a key advantage of introducing systematic strategies, risks remain.
Systematic strategies often utilize a high degree of optimization and can suffer from a continual decay of their performance without constant innovation. Overoptimization can also lead to system fragility or rigidity, resulting in a lack of adaptability to future market behavior.
Another risk to consider is well described by the standard warning that “past performance is not indicative of future results.” While relevant to all strategies, this is perhaps most true for systematic strategies since their core assumption is that the past does, in fact, inform us about the future. The extent to which that assumption is true determines the performance of systematic strategies in the coming years.
Key takeaways
Systematic strategies use programmatic decision making and minimised human intervention to discover and generate alpha and control risks.
Systematic hedge funds can deliver non-correlated returns and are often highly diversified.
Systematic funds often employ programmatic downside protection and are less exposed to human bias than discretionary funds.
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