Kettera Strategies Heat Map - January 2021
In Ag Commodities Specialists, for directional strategies, returns in January were driven again by long corn and long soybean positioning. But some of the more outsized performance numbers also came from spread / relative value programs (e.g., KC vs Chicago wheat spreads and reverse cattle crush spreads fared well, as they did last month).
January was a much more challenging month for the model-driven global macro camp. The market sector that was the main driver of returns last month, commodities, offered only frustration in January. Those few quant managers that ended the month profitably seemed to have fixed income to thank - where some managers’ models were correctly positioned to catch weakening prices. Equities markets were also somewhat lucrative, although the timing of reducing exposures toward month-end was a large factor.
Last month offered investors a mixed bag of returns in the Systematic Trend Programs sector. It seems those systematic trend programs that had the best luck during the month maintained long positions in commodities, were short North American fixed income, and kept G10 currency exposure to a minimum. Others were less fortunate.
Cryptocurrency traders are not strangers to volatility. With BTC surpassing $40,000 for the first time, January left many of the programs we track with their best month ever. The relationship between BTC and Ethereum (ETH) also yielded some interesting opportunities. As BTC corrected in the second half of the month, ETH kept rising – with the ratio between the two dropping from a (relatively high) 42.0 down to 26.0 by month-end.
The last few months have offered a tough ride for Volatility/Options Traders. After enjoying the spotlight from January to August of last year, most relative value vol managers have been on a steady slide since – and the pop up in VIX in late January didn’t give many the boost they needed. (The reason why, as we’ve noted before, is that the programs selected for this style bucket are not purely “long volatility”, but rather “relative value” with a “long bias” – and never nakedly short volatility.)
While January was not a record-setting month for Equities Long/Short, most programs were net positive. But the dispersion of returns among L/S equities was staggering. January kicked off with chaotic markets as Reddit-inspired retail investors banded together to spark a rally in beleaguered stocks that were among the most-shorted in hedge fund land. How a manager navigated the havoc that ensued. One thing that has emerged: Profiting from short positions, which is already hard enough as it is, just got a lot riskier. A social-media-organized mob can quickly take price away from fundamentals.
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For the “style classes” and “baskets” presented in this letter: The “style baskets” referenced above were created by Kettera for research purposes to track the category and are classifications drawn by Kettera Strategies in their review of programs on and for the Hydra Platform. The arrows represent the style basket’s overall performance for the month (e.g. the sideways arrow indicates that the basket was largely flat overall, a solid red down arrow indicates the basket (on average) was largely negative compared to most months, etc.). The “style basket” for a class is created from monthly returns (net of fees) of programs that are either: programs currently or formerly on Hydra; or under review with an expectation of being added to Hydra. The weighting of a program in a basket depends upon into which of these three groups the program falls. Style baskets are not investible products or index products being offered to investors. They are meant purely for analysis and comparison purposes. These also were not created to stimulate interest in any underlying or associated program. Nonetheless, as these research tools may be regarded to be “hypothetical” combinations of managers, hypothetical performance results have many inherent limitations, some of which are described below. No representation is being made that any product or account will achieve profits or losses similar to those shown. In fact, there are frequently sharp differences between hypothetical performance results and the actual results subsequently achieved by any particular trading program. One of the limitations of hypothetical results is that they are generally prepared with the benefit of hindsight. In addition, hypothetical trading does not involve financial risk, and no hypothetical trading record can completely account for the impact of financial risk in actual trading. There are numerous other factors related to the markets in general or to the implementation of any specific trading program which cannot be fully accounted for in the preparation of hypothetical performance results and all of which can adversely affect actual trading results.
Benchmark sources:
1-The Hedge Fund Intelligence Global Macro Index and HFI Currency Index
2-The Societe Generale Trend Index and SG CTA Index
3-The Societe General Short-term Traders Index: (same link as above)
4-The Eurekahedge AI Hedge Fund Index
5-The BarclayHedge Currency Traders Index and BTOP FX Traders Index
6- S&P GSCI Metals & Energy Index and S&P GSCI Ag Commodities Index
7-The CBOE Eurekahedge Relative Value Volatility Hedge Fund Index
8-The Eurekahedge-Mizuho Multi-Strategy Index: (See above)
9-The Eurekahedge Long Short Equities Hedge Fund Index: (See above)
10-Blend of BarclayHedge Equity Market Neutral Index with Eurekahedge Equity Mkt Neutral Index (see link above)
11 – Barclay Crypto Traders Index.
Indices and other financial benchmarks shown are provided for illustrative purposes only, are unmanaged, reflect reinvestment of income and dividends and do not reflect the impact of advisory fees. Index data is reported as of date of publication and may be a month-to-date estimate if all underlying components have not yet reported. The index providers may update their reported performance from time to time. Kettera disclaims any obligation to verify these numbers or to update or revise the performance numbers.
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