Is systematic macro just farming risk premia?

Been meeting more and more systematic macro guys out in conferences and they all just seem to sound like stuff you'd read in AQR white papers (momentum, carry, etc.). A lot of discourse around factors, not so much around idiosyncratic alpha.

Are systematic macro folks mostly trying to just do these sorts of things well? Is it hard to do these things "well"? Aren't constructing these kinds of strategies super commodified and known by every one and their mother? I don't understand how such teams are additive to a HF structure if I as an allocator could just go buy these factors ETFs.

Totally understand that people are trying to get paid and edges are hard to come by in macro, but just looking for some color.

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Systematic macro strategies often revolve around capturing risk premia, but it's not as simple as just "farming" them. Here's the breakdown:

  1. Core Focus on Risk Premia:
    Systematic macro strategies frequently target well-documented factors like momentum, carry, and value. These strategies are grounded in academic research and are designed to exploit persistent patterns in asset prices. For example, time series momentum (TSMOM) strategies, as highlighted in the Moskowitz, Ooi, and Pedersen (MOP) paper, are a cornerstone of many systematic macro approaches. These strategies profit from trends in asset prices, often across diverse asset classes like equities, bonds, currencies, and commodities.

  2. Challenges in Execution:
    While the concepts behind these strategies may seem commodified, executing them effectively is far from trivial. Key challenges include:

    • Data and Signal Quality: Identifying robust signals while avoiding overfitting or data mining is critical. Strategies like TSMOM minimize data mining risks by diversifying across assets and ensuring stable risk exposures.
    • Transaction Costs and Fees: As noted in the Hurst, Ooi, and Pedersen (HOP) paper, fees and transaction costs can erode alpha. Systematic macro teams invest heavily in optimizing execution to mitigate these impacts.
    • Risk Management: Position sizing and volatility targeting are crucial. For instance, systematic macro strategies often scale positions to maintain consistent risk exposure across assets, as seen in MOP's constant volatility approach.
  3. Why Not Just Buy Factor ETFs?
    While factor ETFs provide exposure to some risk premia, they often lack the sophistication and customization of systematic macro strategies. Systematic macro teams:

    • Diversify Across Horizons and Asset Classes: They implement strategies across multiple timeframes and asset classes, which ETFs may not replicate.
    • Adapt Dynamically: They adjust exposures based on market conditions, something static ETFs cannot do.
    • Seek Alpha Beyond Factors: While risk premia are a foundation, systematic macro teams may also exploit inefficiencies or anomalies that aren't captured by standard factors.
  4. Additive Value to Hedge Funds:
    Systematic macro teams bring value to hedge fund structures by:

    • Providing Diversification: Their strategies often have low correlation to traditional asset classes and other hedge fund strategies.
    • Performing in Extreme Markets: TSMOM, for example, tends to perform well during market extremes, offering a hedge against tail risks.
    • Leveraging Scale and Expertise: Large hedge funds can invest in infrastructure, data, and talent to execute these strategies at a level that individual allocators or ETFs cannot match.

In summary, while systematic macro strategies may appear commodified on the surface, their success hinges on execution, risk management, and the ability to adapt dynamically. These factors make them more than just "factor farming" and justify their role within hedge fund structures.

Sources: https://www.wallstreetoasis.com/forum/hedge-fund/demystify-systematic-macro?customgpt=1, A Contrarian View - Raging Bull, Reflections from year 4 as an equity analyst

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big part of it is. and AQR/ MAN/ CFM papers are the best to learn that stuff. there's differentiation of creative application of that, often in signal or portfolio construction. use of ML etc as well. these factor ETFs or QIS strats have significant constraints, they're bound to never work. 

there's a lot of really idiosyncratic stuff in systematic macro. often short term. capacity/ liquidity constrained. or leverages non-traditional datasets. 

 
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Entirely depends on your AUM. If you are running less than a yard, you can find heaps of strategies and alpha to exploit in systematic macro, especially intraday. Naturally no proof of this, but from experience, the lower the strategy capacity, the higher the sharpe, as most of the big players won't even bother to police these inefficiencies. 

For the huge name players that have 10s of billions to deploy, yeah, its pretty much all a slightly improved CTA strategy mixed with some macro, or risk premia harvesting, and often a blend of all of the above. 

Fully systematic is too competitive and too restrictive, if you can live in the semi-systematic space, youll thrive. 

 

Awesome color, very thankful.

Would you say its easy to move from systematic to semi-systematic? Seems like the extra technical skills that a systematic guy brings could be quite helpful to a semi systematic team

 

@Macro Arbitrage and @econometricks I’m curious about the idiosyncratic (novel) systematic macro strategies you mentioned. Can you elaborate?
I’m an experienced semi-systematic macro researcher and believe I’ve developed novel low/mid-frequency strategies, that don’t surface in the literature or my conversations with peers, so I’m always looking for a reality check.
Happy to speak in DMs if easier.

 

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