It’s PhDs vs. MBAs in Credit Investing, as Quants Jump In
Systematic investors use computer models to scan entire markets and select securities that fit their strategies. Traditional asset managers will have to “evolve or risk being left behind.”
Robot investors are increasingly taking over credit markets.
Systematic money managers, making fast decisions about corporate bonds to buy and sell using complicated algorithms, have seen their assets more than double over the past year by one measure, according to Barclays Plc strategists. By another metric, there could be US$90 billion to $140 billion of funds in these strategies in U.S. high-grade and junk bond markets.
It’s the latest sign of how bonds are trading with growing frequency in credit markets, thanks in part to exchange traded funds (ETFs). That higher liquidity is allowing innovations that have fueled stock trading for decades, including black-box trading models, to gain more of a foothold in company debt markets as well, according to Barclays.
“The number of funds and the total deployed assets associated with systematic strategies have ballooned recently,” Barclays strategists Andrew Johnson and Dominique Toublan wrote in a note to clients on Friday. “Up until the past few years, systematic strategies occupied a relatively small corner of the credit market. This has been changing rapidly, and the effects on liquidity and price action are already apparent.”
The shift is pitting investors with technical PhDs against traditional portfolio managers who have business degrees. Systematic investors use computer models to scan entire markets and select securities that fit their strategies. That allows them to examine much more information than fundamental investors, who can scour through only so much data, and also helps them find more obscure opportunities hidden in the market. However, computers may not always be able to pick up nuance in legal documents, listen to tones on earnings calls, or follow relevant news in the same manner as humans, the strategists said.
Nonetheless, they expect the systematic swell to continue, as electronic and portfolio trading have become more popular in credit markets, improving liquidity. Roughly $3.3 trillion of cash volumes were traded electronically last year, with about 16 percent coming from systematic accounts, Barclays strategists found. Because systematic firms look far and wide for investments, they will probably find opportunities others miss, thereby providing liquidity in obscure corners of the market.
“This breadth-first approach is therefore likely to create demand for bonds in less liquid areas of the market and provide additional price discovery,” they wrote.
Traditional asset managers will have to “evolve or risk being left behind,” they wrote.
Automated credit trading has been growing for years, particularly among brokers and dealers, but was formerly focused on helping traders buy and sell securities more efficiently. What’s different now is that the robots are making more decisions about what securities to buy and sell.
To come up with their assessments, the Barclays team counted all the U.S. mutual funds that have the terms “systematic,” “quant,” or “algorithm” along with “corporate,” “credit,” “bond,” or “fixed income,” and found that their assets under management had doubled to $3.7 billion over the past year. They also spoke with Barclays’ algorithmic trading desk to find that one in six electronic requests for quotes come from systematic accounts. They took that material and came up with an estimated range of total assets under management (AUM).
The strategists had some caveats in their report, noting how difficult it is to assess the size of systematic trading because of how many investors combine quantitative and fundamental strategies. A survey of investors at the Barclays Hedge Fund Symposium found that nearly three-quarters of them favored that “quantamental” approach.
They also outlined risks associated with increased systematic trading, especially the potential for trend-followers creating “herding behavior” around certain names and amplifying volatility. Similarly, factor models that try to build on momentum, search for underpriced bonds, or take advantage of bid-ask spreads can cause sharp movements in prices, as can tactical strategies that trade individual bonds.
“Credit investors of all stripes should understand the signals that can bring systematic investors to the market and drive real price action,” the strategists wrote.
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