The following is an excerpt from our recently published SWS Growth Equity 1Q2021 strategy update, which is available in its entirety via pdf or audio stream. In the full piece, we provide our take on sifting through an increasingly volatile equity market backdrop, while assessing the strategic merits of our internally managed strategy for public growth-style equity.
After enduring a year of drastic outcome disparities in public equity, we're reminded of an often-misconstrued aspect to large cap investing. The asset class tends to garner perceptions that higher "efficiency" translates into an inability to generate sustainable and attractive returns, either relative or absolute. The cited culprits range from a disproportionately higher amount of sell-/buy-side analyst eyeballs, deeper pools of liquidity, tighter bid/ask spreads, or name-brand awareness by investors among the blue chips. The embedded implication behind these factors is that large-cap cash flows are somehow easier to predict than that of more obscure or smaller cap issuers.
The Market’s Poor Job of Forecasting Digital Transformation
We know the pandemic acted as a catalyst that accelerated numerous trends poised to unfold over the next decade—online penetration of retail sales being just one example—yet it appears the market was doing an extremely poor job of forecasting the digital transformations at the root of the acceleration. The good news, we launched SWS Growth Equity to prove that attractive risk adjusted returns were possible in actively-managed large cap portfolios. We are incredibly optimistic at our ability to continue this quest as we study the latest evidence uncovered from our fundamental work.
Obscurity Is Not an Edge
Relying on the obscurity of issuer companies as an edge is becoming an increasingly tougher task for investors who imbed this assumption into their process. In the age of machine learning and natural language processing, any competitive moat tied to the practice of being one of only a few investors overturning a given rock is steadily being eroded. We’d argue entirely eroded in some cases. As Applied Materials [AMAT] reminded us earlier this month, machine-generated data has already outstripped human-generated as of 2018. We would not bank on its trendline to be mean-reverting in nature.
A Common Link to 2021 Disruptions
To some degree, the series of events that have caused year-to-date equity pricing disruptions—January's shake-out of high short interest names, February's targeting of ETF baskets held by thematic shops, and March's prime brokerage puke of underlyings used for swaps—all share a common thread. When other institutional investors' equitized exposure (e.g. risk parity, managed futures, passive factor tilts) undergo a third+ standard deviation event, algo traders pounce at the disruption, causing wide price swings.
Our chosen path is one that extracts lessons from algorithmically-enabled disruptions, rather than fight them or be wishfull of “the way things used to be.” They can be great opportunities to future-proof our investment process and calibrate its signal-to-noise ratio in the constant information-sifting exercise…