Kettera Strategies Heat Map - April 2021
In Commodities – Agricultural, returns for most ag specialists - both directional and spread traders - were driven by the continued bullish corn and soybean moves, along with constructive wheat positioning. May corn futures rallied 31%, and in the process, the ratio between soybeans vs corn moved from 2.55 to 2.12, making soybeans less attractive from a relative value standpoint. The cattle crush positioning further enhanced gains for many managers, especially toward month-end.
In Commodities – Industrial, with the possible exception of Nat Gas, most of the action in this sector in April was in metals – particularly industrial metals. Most of the specialists we track benefitted from the 10-year high in Copper that came toward the end of the month. Iron ore was another standout performer, with spot prices up over 18% as Chinese domestic steel prices rose and destocking continued. Precious metals – particularly Silver – also added to profits.
Not surprisingly, systematic trend followers with meaningful allocations to commodities outperformed those that primarily trade financials. Long positions in ag commodities and metals continued to reward those riding impressive upward trends. Most systems also caught the strong upward moves in equities, both in N. America and Europe. Fixed income was more of a mixed bag, and FX appeared to be a detractor from returns. We also note that the longer the holding period of the models, the better the performance appeared to be.
In contrast to the systematic CTAs above, for most quant macro managers fixed income was one of the best performing markets. For those trend followers covering both this sector and commodities, both sectors contributed approximately equally. Returns from equities was largely flat to slightly positive, and FX was largely a negative contributor.
AI / Machine Learning is a difficult category to generalize, given the wide variety of strategies and markets covered. However, just looking at the AI/ML futures trading programs, it appeared that equities indices and commodities (primarily industrial metals and ag markets) were a common theme here. One distinguishing feature of April was that these types of strategies also seemed to navigate the FX markets a bit better than others - particularly in shorter-term positions – capitalizing on the slide in the US dollar vs the major units.
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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 IndexIQ Hedge Global Macro Beta 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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