(Bloomberg) - The propensity for U.S. stocks to log the bulk of gains when the cash market is shut overnight has had investors scratching their heads for years. Now there are exchange-traded funds in the works that seek to let investors cash in on the phenomenon.
AlphaTrAI Funds Inc. intends to launch three exchange-traded funds employing the same trading strategy for large-cap, mid-cap and small-cap stocks to capture the difference in returns during the hours the U.S. market is open and those after regular trading ends, according to a regulatory filing.
Simply put, owning these funds would be like buying stocks at the close and selling them at the open.
A host of factors may explain why overnight action has delivered better returns. Different trader populations are awake at different times. Liquidity ebbs and flows as the session rolls on. These overnight funds are likely to gain attention during reporting season, when earnings are typically announced outside regular trading hours, according to Dave Lutz, managing director at JonesTrading.
It’s “well documented the overnight session has well outperformed regular hours,” he said, adding that such ETFs are “going to be a whirlwind during earnings season!”
Lately, however, news about the Ukraine war dominated the overnight trading, contributing to significant losses during that time frame.
To Eric Balchunas, senior ETF analyst at Bloomberg Intelligence, another issue could be higher transaction costs given the funds would adjust holdings every day. The filing did not list proposed fees for the funds.
Yet Balchunas still sees potential demand for such products as they fall into the “packaged trade” ETFs that perform more complex trades so investors don’t have to execute them themselves.
“We’re in an era now where people are looking for creative hot-sauce-esqe stuff to put on top of their boring portfolios,” he said. “The ETF industry never ceases to amaze in its creativity.”
Max Gokhman, chief investment officer for AlphaTrAI, declined to comment, citing regulatory constraints.
By Lu Wang and Vildana Hajric
