Kettera Strategies Heat Map - December 2020
Ag Commodities Specialists had one of its best months in more than a year in December. Outsized returns were driven by both directional and spread trades. Many managers capitalized on long corn, wheat and soybean positions – as several markets are being driven by drought conditions developing in South America and continued strong demand from China. Spread-trading specialists also cashed in.
Not unlike their discretionary brethren, the quant-driven macro managers we follow appeared to also capitalize on the natural resources/commodities theme. The more profitable programs in December seemed to generate most profits in “natural resource”-focused positions. This included direct commodities exposure, but also positioning in the indices and currencies of “commodities economies.” Equities and fixed income positions appeared to on the whole less profitable for this style category.
December was one of the strongest in over a year for most systematic trend program managers and the industry benchmarks. Nearly every veteran trend follower we track made profits in at least three of the five identifiable sectors (equities, FI, currencies, industrial commodities and ag commodities). A few ended up positive in all five. The common profitable theme appeared to be long equities positions and short USD positions. Those programs that include ag commodities (a sector not covered by all systematic trend programs) tended to do better as their models caught the bullish moves in grains.
Of the non-equities (or “Tactical”, futures and derivatives trading) style buckets that we track, AI/machine learning programs were right up there sharing the spotlight with systematic trend programs. While it’s hard to extract a common theme among the more successful programs in December, it appeared that most of the AI programs we follow benefitted from short US dollar positioning vs other currencies. Equities index trading was a mixed bag for this group – although we note that AI programs with shorter trade horizon tended to fare better in this sector than others.
The event driven camp posted a month that came close to rivalling the historic “bounce-back” month of April 2020. From our vantage point some common themes appeared to be focus in December (mostly the long side) on financials and payment processing services and technology, among others
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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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