The digital revolution took its time getting to fixed income, but today it’s transforming the investing landscape. Already, major advances in technology are helping early adopters gain unique insights and act faster in markets where speed and alpha are increasingly and inextricably linked.

Predictive analytics, automation, artificial intelligence (AI) and machine learning make huge, complex data sets meaningful and useful. With hundreds of thousands of issuers, trillions of dollars in bonds and a largely over-the-counter trading system, the fixed-income world has long needed such upgrades.

Bond managers that have prepared for the digital revolution in investing combine technology with human savvy to solve the market’s most vexing problems. Here’s how.

Turning Puddles of Liquidity into Pools

The ability to find counterparts willing to trade a given bond at a given price has always been the biggest factor affecting a bond manager’s ability to create alpha. Unfortunately, liquidity has grown scarcer and more fleeting since the global financial crisis. Dealer balance sheets have shrunk even as the size of the market has grown dramatically.

When fewer people are willing to take the other side of a trade, prices can move sharply and transaction costs can rise. The problem is especially pronounced when news hits markets, as even small events can give bond investors the jitters and cause liquidity to evaporate in a flash.

For managers, the difficulty of monitoring liquidity conditions compounds the problem. Liquidity pools—markets that provide liquidity for securities—are highly fragmented across third-party sources. It’s inefficient for asset managers to monitor each one and then compare the data. Firms that can’t effectively assess a bond’s liquidity can’t act on their investment ideas, and trades that never happen can’t make money.

Thankfully, new technology is helping plugged-in traders identify counterparts faster and more easily by pulling all external fixed-income trading platforms together on one digital platform. It’s an essential innovation in a marketplace that will digest and react to every new bit of information faster and faster.

Firms that adopt these kinds of platforms can become price makers instead of price takers, resulting in better executions, lower transaction costs and faster investment of cash inflows.

One-Touch Access to Credit Research

To generate better returns for investors, firms need to know not only which bonds are available in a market, but also which bonds help achieve a portfolio’s strategic goals.

The trouble is that research analysts and portfolio managers (PMs) must consider dozens of factors in the analysis, from the capital structure of a bond to the financial health and governance practices of the issuer. Traditionally, fundamental analysts have provided qualitative analyses, forcing PMs to spend precious hours or days going back and forth with various research teams to determine whether a bond is a good investment for the portfolio.

Firms that quantify, digitize and centralize their fundamental ratings make the evaluation process more efficient. Their credit research and rating processes reside in a consistent, accessible framework. This framework allows traders and PMs to immediately grasp an analyst’s assessment of a bond’s risks and potential and to quickly compare two or more similar bonds.

At a time of fleeting liquidity, this centralized format helps managers make the quick calls necessary to seize opportunities that slower-moving firms might miss.

Bringing in the Machines

To fully harness technological advances to build better portfolios and maximize alpha, firms must open new research and trading platforms to machine readers.

Just as consumers have done with the likes of Siri and Alexa, digital-minded bond managers are turning to virtual assistants to search and synthesize enormous amounts of data.

Virtual assistants build orders more quickly and with fewer errors than a human, who is prone to fat-thumb mistakes even when he or she is extremely careful. Automated order building is more revolutionary than it sounds, considering that 80% of corporate bond trades are still carried out over the phone.

But sophisticated virtual assistants can be taught to do a lot more. Some investment managers feed a virtual assistant a list of criteria based on market conditions, research ratings and individual issuer characteristics and receive a list of potential investment opportunities within seconds.

The natural next step would be to teach machines to continuously scan the market on their own for investment opportunities that meet predetermined research criteria. Once machines can do that, it’s a small step for virtual assistants to proactively suggest potential investments to PMs.

Virtual assistants will also likely learn to monitor portfolio activity and look for unusual behavior that could indicate human error. They may even begin to answer simple questions for clients about their exposure to certain credits or risks. And as AI and machine learning advance, they could add value we cannot yet imagine.

Humans should always retain control over key decisions and complex client interactions. But brainy virtual assistants—helped along by further advances in data science and machine learning—will bring bond managers and their clients a host of benefits, including better trade execution, capturing more opportunities to buy and sell bonds, and the time and ability to discover unique research insights.

For fixed-income investors, it’s never been more important to find out whether a manager is at the forefront of the bond market’s digital revolution or losing ground to the analog status quo.

The views expressed herein do not constitute research, investment advice or trade recommendations and do not necessarily represent the views of all AB portfolio-management teams.

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