March 2023 was a tough month for CTAs. The failure of Silicon Valley Bank (SVB) led to a sudden reversal in interest rates that was painful for those following a trend – a trait of CTAs that is well documented.[1] The move in the SG CTA Index – the most oft cited benchmark for trend following strategies – was unsurprising given the way the event panned out. What was perhaps more surprising was the impact on performance of those CTA funds that have diversified into trend following in ‘alternative markets’, which do not follow the same whims as developed markets, so the story goes.
The performance of most (not all) such products is undeniably good. We, as a firm, are focused on liquidity and as much as diversification into less efficient markets bringing excess performance is an easy narrative to push, our instinct has been to be wary of illiquidity and the potential for product performance to come from flows into the product itself.
As posited in many posts by our Chief Scientist, Jean-Philippe Bouchaud (see for instance here and here), the performance of the Ark Invest ETF can be explained by a flow of investors – first, flows into the ETF drove its performance up, while subsequent flows out drove its performance down. The work of Gabaix and Koijen[3] measured that, in normal conditions, buying (selling) 1% of the market cap of a firm increases (decreases) its medium term price by 1%. Ark Invest experienced inflows of $40 billion into its ETF between 2018 and early 2021 that were invested in small cap “disruptive technology” stocks (with the exception of Tesla). To keep things simple, if we assume $40 billion flowed into 40 stocks each of $1 billion in market cap, all stocks double in price. This first order calculation is not far from what happened in the period of asset inflows, while subsequent outflows saw the performance fall back down to earth. This logic can then be similarly applied to the illiquid, alternative market CTAs – the flow into the products potentially producing the observed positive performance while an end to the flow could trigger the decay of impact that may wipe out a large part of the gains accumulated.
The argument goes that alternative market CTAs promise superior returns due to the diversification power of a broader portfolio. However, just how the Sharpe ratio of a portfolio evolves as a function of the number of instruments added depends non-trivially on the correlation between assets. We have shown previously[4] that building a portfolio from equally weighted Principal Components is the most robust method of diversifying. Positioning (and trading) in a real (and scalable) optimized portfolio is then dominated by considerations of liquidity (being the dominant factor dictating the cost of trading). For these reasons, we prefer to move our programs into those alternative markets that promise liquidity and diversity, for example in China. It is also worth noting that there is no free lunch in terms of convexity for alternative markets! Any diversification away from trend following into instruments that do not move with equity indices in times of crisis, dilutes the mechanical convexity that trend following on developed market equity indices produces,[5] and limits the equity downside protection offered by the program.
Many decades of experience in running CTA programs have cemented our conviction that liquid markets are the way forward for our suite of futures products, thus keeping costs under control and allowing us to respect our liquidity terms.
[1] For example, see our white paper ‘Explaining Hedge Fund Index Returns’ available for download on our website.
[2] The SG CTA and SG Trend Indices are both designed to be representative of the performance of CTA / trend following managers. While similar, the major difference – as per the current methodology – is that the SG CTA Index comprises a larger pool of funds (20), than the SG Trend Index (10).
[3] Xavier Gabaix & Ralph S. J. Koijen, 2021. ‘In Search of the Origins of Financial Fluctuations: The Inelastic Markets Hypothesis’, NBER Working Papers 28967, National Bureau of Economic Research, Inc. The paper is available here.
[4] Please refer to our academic work, ‘Agnostic Risk Parity: Taming Known and Unknown-Unknowns’ available for download on our website. Also please consult our white paper ‘Making Optimization Techniques Robust with Agnostic Risk Parity’ available on our website.
[5] Interested readers are encouraged to read our paper ‘The Convexity of Trend Following. Protecting your assets but perhaps not as much as you would like!’ available for download on our website.
Disclaimer
ANY DESCRIPTION OR INFORMATION INVOLVING MODELS, INVESTMENT PROCESSES OR ALLOCATIONS IS PROVIDED FOR ILLUSTRATIVE PURPOSES ONLY. ANY STATEMENTS REGARDING CORRELATIONS OR MODES OR OTHER SIMILAR BEHAVIORS CONSTITUTE ONLY SUBJECTIVE VIEWS, ARE BASED UPON REASONABLE EXPECTATIONS OR BELIEFS, AND SHOULD NOT BE RELIED ON. ALL STATEMENTS HEREIN ARE SUBJECT TO CHANGE DUE TO A VARIETY OF FACTORS INCLUDING FLUCTUATING MARKET CONDITIONS AND INVOLVE INHERENT RISKS AND UNCERTAINTIES BOTH GENERIC AND SPECIFIC, MANY OF WHICH CANNOT BE PREDICTED OR QUANTIFIED AND ARE BEYOND CFM’S CONTROL. FUTURE EVIDENCE AND ACTUAL RESULTS OR PERFORMANCE COULD DIFFER MATERIALLY FROM THE INFORMATION SET FORTH IN, CONTEMPLATED BY OR UNDERLYING THE STATEMENTS HEREIN.
