Kettera Strategies Heat Map - November 2022

Systematic Trend Strategies

Long term trend following strategies suffered a large “give back” month in November. There was a substantial and broad-based reversal in trends across all asset classes, driven by falling inflation rates and rising expectations about an upcoming slowdown in interest rate hikes. Equity and bond markets generally rose, while the USD weakened significantly, all against well-established long-term trends. Base and precious metals rallied, while energies sold off, again, against established trends. Some shorter- and medium-term systems caught these moves, but most of the larger, long-term models took hits before exiting. In short, these strategies were not able to catch a break anywhere.

Discretionary Global Macro Strategies

Although discretionary macro trading was mixed in the general universe, many if not most of the discretionary programs we follow appeared to be positive. Performance depended on the asset classes that the manager focused on, as well as the extent to which a portfolio was still exposed to the crowded risk-off trade that had been in place for most of the year. The “risk off” trade, which has generally consisted of short equities, long USD, and short bonds, reversed in mid-November. The better performing strategies were those that had patiently waited – often taking earlier losses - for the USD to weaken and bonds to strengthen.

Quant Macro Strategies

Performance across Quant Macro strategies was also quite mixed, but – in contrast to their discretionary brethren – these managers had more negatives than positives. The reversal in the big risk-off trends mid-month, appeared to stymie macro models a bit more – not unlike the systematic trend models. Those programs that ended the month in positive territory generally saw gains in long equities and long commodities (i.e. base and precious metals) and ags (i.e. soy complex, softs), due to more agile and shorter-term behavioral models catching non-price, fundamental-based signals.

Agricultural Commodities Specialists

November was generally choppy with range-trading across the primary grains markets (corn, wheat, soy complex), with neither breaks nor rallies able to hold. The corn market started off the month in a slide and then chopped around for the rest of the month, while wheat sold off after the Black Sea Grain corridor was reestablished.  Among the most popular positions were long-biased corn and short soybeans (due to expectations of generous supply stocks), and traders lost out on both. The more successful ag-focused programs tended to have captured profits in soft commodities, including short coffee and long sugar and cotton.

Industrial Commodities (Energies and Metals) Specialists

Metals specialists generally had a good November, particularly fundamentals-based strategies that have been positioned long the base metals (metals other than gold or silver). Such positions suffered  through losses for a while, as macro headwinds overwhelmed micro supply shortages. But these markets have finally rallied, due to expectations that China will end rigid “Zero-Covid” lockdowns (increasing demand for metals). Energies traders were largely flat to negative, as volatility in crude and products markets, and natural gas, remain high and choppy, making trading difficult for both directional and spread strategies.

Currency Specialists

Overall, this category – which has been enjoying one of its best years in nearly a decade – faced a challenging month. When weaker-than-expected U.S. inflation numbers were released  on November 10, the markets moved quickly and viciously – including a drop in price of the US dollar. This was all on expectations of a reversal by the U.S Fed in 2023.  In the USD-based exchange rates, the largest mover was USD/JPY which fell 7.7% and AUD/USD and EUR/USD which rose 6% and 5.3% respectively. Some systematic shorter-term FX strategies were nimble enough to catch the USD reversal in G10 currencies, while most discretionary strategies and fundamentals-based models were caught off-guard by the speed of the reversal.  While fundamental model-based (or econometric) FX strategies tend to suffer at these inflection points, they also tend to outperform once the fundamental data catches up following the initial shock.

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Kettera Strategies

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. Blend of Eurekahedge Macro Hedge Fund Index and BarclayHedge Global Macro Index
2. The Eurekahedge Macro Index
3. The Société Générale Trend CTA Index
4. The Société Générale Short-term Traders Index
5. The Barclay Hedge Currency Traders Index
6. Blend of Bridge Alternatives Commodity Hedge Fund Index and Barclay Discretionary Traders Index (for February only the Barclay index was used as the Bridge index was unavailable.)
7. The Barclay Agricultural Traders Index: (same link as above)
8. The Eurekahedge Commodity Hedge Fund Index
9. Blend of CBOE Eurekahedge Relative Value Volatility Hedge Fund Index and CBOE Eurekahedge Long Volatility Index (same link)
10. Blend of Eurekahedge Multi Strategy Asset Weighted Index and Barclay Hedge Fund Multi Strategy 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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The views expressed in this article are those of the author and do not necessarily reflect the views of AlphaWeek or its publisher, The Sortino Group