By Daniel Leveau, VP of Investor Solutions, SigTech
On the backdrop of a weak performance in 2022 for both the equity and fixed income markets, investors are increasingly utilizing trend following strategies as a portfolio diversifier.
Trend following strategies aim to systematically capitalize on persistent upward and downward trends in asset prices. Simply put, it does not matter how the general market performs. Rather, what is important is that the market is heading in one direction and continues to move in that direction.
This approach can be applied to a wide range of asset classes to achieve diversification across a broad investment universe.
Investor flows
Investors have increasingly sought to improve portfolio diversification after the S&P 500 posted its worst first-half year return since the beginning of the 70s. Trend-based strategies’ long volatility profiles – i.e. they generally profit during periods of rising volatility – often deliver strong returns during market crashes (e.g. 2008). Thus it is understood as an insurance within an investor’s portfolio.
Looking at general flows to hedge fund strategies in 2022, investors have reduced their exposure to the biggest hedge fund category; equity long/short. Trend following and managed futures strategies have profited from substantial inflows.
Why trends exist
The reason why investors have been able to exploit trends to systematically achieve strong investment results is found in behavioral finance. By studying the impact of psychology on the behavior of investors – and the ensuing effect on asset prices – behavioral finance seeks to resolve contradictions between classical economic theory and reality.
The most common examples of such biases, which help explain the existence of trends are:
· Herding: investors tend to follow actions of other investors
· Overreaction: investor reaction to new information is exaggerated
· Confirmation bias: investors actively seek and interpret information to support existing beliefs.
How to detect trends
Trends may be identified using various methodologies based on traditional technical analysis, statistical time-series filtering or utilizing machine learning and AI.
Some of the most commonly used trend indicators are:
· Relative strength index (RSI) is a momentum oscillator that measures the speed and change of price movements and is used to spot overbought and oversold market conditions. The data is normalized to move within a range of 0 to 100.
· Moving average convergence divergence (MACD) is used to measure the direction and the strength of a trend. This indicator compares the difference of the moving averages calculated over different windows.
· More advanced filters such as L1 and Hodrick-Prescott can be used to identify a time series’ trend by minimizing noise with the application of a penalty associated with variations in the estimated trends.
The need for speed – and high-quality data
Trend following strategies have historically proven their value by delivering attractive risk-adjusted returns and offering effective portfolio diversification. This has resulted in steady inflows from investors, in turn leading to trend-based strategies becoming a more crowded space.
To stay competitive and develop an edge, investors need to build robust trend following strategies that are easily adapted to changing market conditions.
Managing this process via Excel spreadsheets or disparate legacy systems is complex, prone to errors, and slow. When sourcing, validating and making data operationally-ready is an increasingly complex task, quant teams can spend more time managing data than analyzing it.
With managers already struggling to mitigate inflation risk amid uncertain market conditions, access to high quality data and efficient analytical tools is more crucial than ever.
Yet, old habits die hard. Trends are driven by strong behavioral biases that have persisted for decades. It is, as always, hard to predict what the future will bring but trend following strategies have historically offered a safe haven for investors during tumultuous times in the financial markets.
