The surge in FAANG stocks is exposing a blind spot in fundamental-risk models that is largely overlooked by investors and may endanger their portfolios, according to AllianceBernstein.
Cluster risk, occurring when performance patterns become correlated among a group of stocks with similar business profiles yet different sector classifications, is a hazard than can often slip under a risk manager's radar, AB's David Dalgas, Klaus Ingemann and Thomas Christensen wrote in a blog post.
Facebook Inc., Amazon.com Inc., Apple Inc., Netflix Inc. and Google owner Alphabet Inc. -- collectively known as the FAANGs -- are a prime example, AB said. Together the mega-cap stocks accounted for more than a quarter of the gains in the S&P 500 from the beginning of the year through August and have become increasingly correlated, according to AB. However only Facebook, Google and Apple are classified as technology stocks under MSCI's Global Industry Classification Standard, while Amazon and Netflix are seen as consumer-discretionary shares.
This creates a potential problem for risk managers, who would tend to monitor portfolios so that they don't have too much exposure to either the technology sector or the consumer sector rather than looking for a correlated group of shares from different industries.
"A risk model that isn't aware of this correlation may prompt a portfolio manager to purchase all five FAANG stocks - a position that could be vulnerable to a sharp downturn of the group," according to AB. "Or, the risk model may suggest adding to positions in Netflix or Amazon.com without considering the possibility that these positions may suffer if Apple has a bad day."
Cluster risk can also arise in other sectors, such as financials and health care. American Express Co. is classified as a financial stock while fellow credit-card peers Mastercard Inc. and Visa Inc. are listed as technology shares.
And the solution? Investors do not need to abandon standard risk models, but should apply "fundamental logic" to their quantitative processes, according to AB. "And when clusters are identified, portfolio managers should aim to limit positions accordingly."