The bond market, complex and illiquid, has always been tough for funds to crack. Now, a group of computer scientists is working on a way to use data and algorithms to do what has so far been unachievable. This all sounds good in theory, but is this just another case of investors being told “this time it’s different?” How well will these algorithms hold up during a downturn, when the sample set of data for such an event is so small? Robin Wigglesworth and Laurence Fletcher report for the Financial Times:
“This feels like the early days of the ‘quant’ equity industry,” says Paul Kamenski, co-head of credit at Man Group’s Numeric unit, alluding to the development of “quantitative” strategies in the stock market. “A lot of the research is in the early stage but the pace of advancement is likely to be faster. There’s a realisation that this is an untapped market.”
Man Numeric launched a systematic bond fund late last year, initially focused on US junk bonds but with plans to expand it into investment-grade corporate debt over the next year.
Man is wary of giving away too much detail, but one area the hedge fund has found fruitful is “alternative data”, non-traditional information such as credit card purchases, trade receipts and shipping data. This can help to parse private companies that do not disclose regular financial statements and to map out linkages between companies that are competitors and clients. It can also exploit leads and lags between how their bonds react to developments.
For example, if a cement company seems to be doing well, then it might indicate that the homebuilders it supplies are also booming.
One big barrier has been the complexity of the bond market — especially corporate debt. Whereas a company may issue just one or two types of equity, it could issue tens or even hundreds of types of bonds, with varying legal safeguards, currency, maturity and coupon payments. Moreover, big macroeconomic shifts — such as interest rates — can have a major but unpredictable impact.
Yet advances in computing, cleaner, longer-term trading data and a critical mass of bond market research have now created a tipping point for systematic fixed income investing, according to Linda Gruendken, lead scientist at Cantab.
Not everyone is convinced that bond markets represent a new gold rush for quants. Mihir Worah, one of Pimco’s top executives who leads its own quant efforts, points out that groups like his have already been mining the bond market’s inefficiencies for decades.
“It’s the next frontier. But the pickings may not be quite as fertile as some people think, as the easy pickings are already done,” he says.
Trading is the biggest challenge. Trading volumes in bonds tend to be much lower than in the stock market, and transaction costs are high. This means that even potentially lucrative signals can be difficult to exploit in practice.
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