Energy Hedges the AI Trade

Energy Hedges the AI Trade

Key takeaways

  • Market concentration, driven largely by artificial intelligence (AI), has indexes behaving like a single stock with major upside skew, making the tradeoff between managing absolute and relative risk arguably the steepest since the 2000 cycle.
  • While the AI trade absorbs nearly all available investor attention, the market has priced an ongoing energy crisis as largely solved. With energy’s correlation to the index negative over the past year, owning energy covers our AI relative risk more effectively.
  • Today’s energy sector—defined by capital discipline, fortress balance sheets and stocks still embedding oil prices anchored in the US$70s—is far more defensive than in past cycles. We expect energy proves to be an investment while many parts of AI prove to be a trade.

An Index Behaving Like a Single Stock

A lot of life, and certainly the challenge of portfolio construction, is about tradeoffs. Portfolio construction has a key constraint: many of the tradeoffs emerge because of uncertainty, with one of the main ones being that we are looking for signals in a very noisy environment. Even when we get the signal right, it doesn’t always translate as expected at the portfolio level or takes longer to cut through the noise than we thought. This matching problem between signals and outcomes directly lowers your edge at a given time scale and is why diversification is such a critical tool in portfolio construction: concentration is only justified when edge is high, but when edge is reduced the solution is to diversify more.

Right now, indexes are increasingly concentrated, effectively behaving as if they have significant edge, and record-low correlation, coupled with the “stock up, volatility up” dynamic, has them acting more like a single stock with major upside skew. Managing relative risk against this dynamic is extremely challenging: how do you manage against a single stock with a diversified portfolio? Especially when we think the actual edge is low, correlations cannot sustain current levels, and equity risk premiums are at levels that have historically called for caution.

The result is an incredibly steep tradeoff between managing absolute and relative risk—arguably the steepest since the 2000 cycle. Our investment process is anchored by managing absolute risk through an absolute valuation discipline, with our primary goal being to generate a double-digit absolute return across a full market cycle. We do this by exploiting price-to-value gaps specifically where we expect convergence because future fundamentals have a higher probability of a good outcome than the market has priced. We then look to manage the resulting volatility tax with differentiated portfolio construction, combining stocks across a much wider correlation spectrum than most peers would consider.

This entire regime depends on correlations staying at extremes. The only other times realized correlations were this low were just before the 2007 housing crisis and just before “Volmageddon”1 in early 2018 (Exhibit 1). The likely driver of this low-correlation extreme is AI: there is AI and everything else. The paradox is that while the ultimate outcomes for AI are inherently uncertain, the market is behaving as if it has a lot of edge around these AI unknowns. Ultimately, we believe that correlations will spike higher, and our guess is the timing will be measured in quarters rather than years.

Exhibit 1: Indexes Are Seeing Record Low Correlations...Again

Another key driver of this low-correlation market is record dispersion in stock betas, emerging not only across sectors but within them (Exhibit 2). If you don’t manage this dispersion directly, the gap in beta between a prudently diversified portfolio and the index creates an active beta drag—a relative risk tax.

Exhibit 2: Sector Betas Are Seeing Record Dispersion

See more: The Skill That Fades When AI Does the Prep