The bears are out.
With the S&P on the brink of a bull market, many Wall Street strategists remain convinced that 2023’s top-heavy, A.I.-inspired trek towards last summer’s highs is inherently unsustainable.
Measuring from the October lows, the S&P came into the week a rounding error away from a 20% gain.
It was around these levels late last August when Jerome Powell snuffed out the rally with a terse Jackson Hole speech, setting the stage for a new leg lower in equities and all manner of turmoil in FX.
Arguably, the Fed should likewise endeavor to cool the current rebound, or at least to blunt the euphoric character of the Nasdaq 100’s meteoric rise. The animal spirits are stirring, and that could worsen the Fed’s “sticky” inflation problem through the wealth effect channel, particularly as Americans venture out on vacations and otherwise revel in the summer heat.
But according to some strategists, stocks are likely to buckle under their own weight (and the weight of last year’s policy tightening) regardless of what the Fed does from here. “The downside risk to US earnings is now,” Morgan Stanley’s Andrew Sheets said, reiterating a few of Mike Wilson’s favorite margin compression talking points, and tipping his hat to the well-socialized liquidity drain narrative. “While a deteriorating liquidity backdrop is likely to put downward pressure on equity valuations over the next three months, we also see EPS disappointment ahead as revenue growth slows and margins contract further,” Sheets said.
So, if you ask Morgan Stanley, stocks are likely to be pressured from both sides — contracting multiples and disappointing earnings as negative operating leverage bites. Wilson released a 74-page mid-year outlook on Monday outlining a more nuanced view. I’ll get to that shortly, but suffice to say the bank believes the current cyclical bear market won’t end without one more tactical correction. “At current valuation levels, we believe the equity market is optimistically discounting both Fed rate cuts in 2023 and durable growth,” Wilson said. “We view the likelihood of those outcomes playing out simultaneously as low.”
Meanwhile, JPMorgan’s Mislav Matejka cautioned on “long and variable” policy lags, and said a handful of dynamics supporting equities, including savings buffers and still hefty margins, are set to fade. Stocks, Matejka said, will confront an increasingly onerous growth-policy conjuncture in the second half. He favors growth shares over value.
I’d be remiss not to note that equities are probably still getting some support from systematics. Realized vol is now 6%ile and 1%ile on a one-year lookback (one-month and three-month, respectively).
“As anticipated, the vol crunch means further extension of ‘buy-to-reallocate / re-leverage’ flows out of systematics,” Nomura’s Charlie McElligott said late last week.
Over in Europe, Morgan Stanley’s Graham Secker said this year’s equity resilience should “become increasingly challenged over the next three months.” He cited the same list of worries — so, waning economic momentum and the liquidity headwind. “We see cracks emerging,” he wrote.
Another major Wall Street bank expressed “fundamental confidence” in their year-end S&P target of 4,000. The A.I. obsession will fade as companies integrate the technology, the bank said, effectively suggesting that as A.I. is normalized, the hype will wane. Count me skeptical of that contention, at least.
The current AI boom is driven almost exclusively by one technique: LLMs using the transformer architecture. Their capabilities are impressive and ingenious new use cases are discovered daily, often by chaining them together to process each other’s output. The fact that LLMs can be programmed in plain English is even more remarkable.
Every technology has its limits. An LLM is basically a device for predicting the next word in a sequence. Although their potential is huge, it cannot be generalized to solve all problems everywhere.
Just as calls for humanity’s impending doom at the hands of the machines are overblown, I believe the economic impact of this technology is being overestimated simply because the machines suddenly SEEM to have mastered language. In fact, we’ve merely discovered a new way to use statistics to reflect our own skill with language back to us. There is no intention or motivation to these things; they’re just the first technology that comes close to passing the Turing test, and so they feel like artificial general intelligence to us.
Vis a vis markets, there’s going to be a prodigious wave of investment, but any product or service that merely calls other people’s models will be swiftly commoditized by those who control the models and provide the capital-intensive compute platforms on which these models depend. It’s certainly not conducive to creating new behemoths of the future.
Likewise, numerous problems just can’t be solved by predicting the next word. We may have stimulated a wave of discovery in other areas of machine learning, but these will take time to research and develop.
TL;DR the AI revolution is not here now. I’m not sure that markets are pricing the timeline accurately. They do seem to understand who will benefit, though.