When describing the municipal bond market, the first adjectives are usually “staid” or “stodgy”. The terms “data-driven” or “technologically cutting edge” are not usually the first prefaces attached.
That’s unfortunate—and increasingly inaccurate. Technological tides have risen steadily in this market, particularly in investing and money management, from digitized financial data to algorithmic trading. Now they are sweeping through.
Technology drives market innovation by its functions and the speed at which it can provide them. The question is not can it be done, but how can it be done, and how fast can it be done. The results are transformative. And with artificial intelligence and machine learning, it’s only moving faster.
Technology also taps into basic market psychology: fear and greed. If your worthy competitor has technology enabling them to move faster, more efficiently, more accurately, and cheaper, you’d better catch up technologically otherwise you are going to be left hopelessly behind. Either stay cutting edge or get cut.
$900 Billion Reasons For Technology
For money managers in the municipal bond market, technology and data are inextricably linked to asset growth and investment performance. For example, consider municipal bond mutual funds. With around $900 billion in assets and millions of shareholders, open-end mutual funds and ETFs not only dominate the institutional market but influence nearly every part of the municipal bond market as a whole. These assets are also concentrated, with 70% of the market’s mutual fund assets held by just 10 money managers firms.
Correspondingly, competition for assets and fund performance is ferocious, imposing intense pressure on fees, costs, and performance. With multibillion-dollar funds, just a handful of basis points can help or hurt the bottom line or make the difference between top quartile or bottom quartile performance.
To be competitive, these firms need the most efficient and economic processes in the three key business elements of money management—investing, operations, and compliance. The drivers both behind and leading each of these are technology and data.
Top decile investment performance requires analysis of trades, pricing, credit risk, and market trends. Operations oversee smooth and accurate trade execution, tracks holdings, manage fund accounting, and report on essential metrics such as pricing and variances. Compliance has to ensure a myriad of seemingly ever-changing rules and regulations are being followed, with increasing emphasis on pricing given the recent Securities and Exchange Commission Rule 2a-5 and the new Rule 31a-4.
But it’s not just huge mutual funds faced with these pressures. Other institutional investors, such as banks and insurance companies, face the same issues in managing their portfolios. Then there are the individual investor surrogates such as wealth advisors and separately managed accounts. Municipal bond holdings are a core of the asset growth in these portfolios. They face the same issues, perhaps even more so given the sheer number of portfolios they manage.
Who Is Who
So who are some of the cutting-est edge tech and data leaders looking to provide the market with the technology and data solutions for these business segments?
Speaking with start-up founders as well as long-term muni veterans, there were many technology and data companies noted, but a few names came up repeatedly.
To some, it comes as a surprise that the market’s chief regulator is one of those at the forefront. It shouldn’t. The Municipal Securities Rulemaking Board, charged with the weighty job of protecting and strengthening the municipal bond market, “enabling access to capital, economic growth, and societal progress in tens of thousands of communities across the country”, is committed to technological change as part of meeting that mission.
Not only does it “build technology systems that power our market and provide transparency for issuers, institutions, and the investing public” such as Electronic Municipal Market Access (EMMA) for disclosure filings, but also has launched EMMA Labs.
EMMA Labs is remarkable, “an innovation hub where market stakeholders can collaborate on active prototypes.” It’s a technological sandbox, free and accessible to anyone. It was established to invent and share feedback on preliminary concepts that could eventually make their way to being implemented on EMMA—or elsewhere. The goal is to have “decision-ready data” to make information actionable.
There are two lab areas, “Active Lab” and “Idea Lab”. The Active Lab areas have nearly fully formed apps and platforms in the midst of beta testing. The Idea Lab areas are the incubators, the place to float an idea, get inputs, see if it gets any traction, make pivots, and eventually maybe even get it to an active lab.
The Active Lab area currently has three initiatives underway: Market Analysis, Structured Data, and Disclosure Search.
For analysts, investors, and researchers, the Market Analysis lab has a bevy of searchable data and statistics on key market metrics, such as overall volume, daily trades, size of trades, and customer buys and sells.
In the Structured Data lab, there are case studies on how isusers are using standardized, structured data in financial reporting. One case features the City of Flint, Michigan’s use of Extensible Business Reporting Language (XBRL) to standardize its financial reporting. Rob Widigan, former CFO
Click on Disclosure Search, and in the screen appears only a blank search bar to enter whatever your search term might be. As with the best-designed apps, its simplicity is its power. Type in, say, Flint, Michigan, and up pops a filterable list of issuers (1,129 search hits, 1,000 downloaded—but who’s counting?) that have Flint, Michigan somewhere in their disclosure. Click on any line and that document appears, right on the page (or pages), with the search term neatly highlighted for easy finding. No more control-F or mind-numbing scrolling.
One can only imagine how simple it will be to find, sort, and compare financial information with this tool when the data is provided in a standardized, structured format.
Of course, private companies see the market demand for data and technology solutions. Take S&P Global Market Intelligence as a good example of data capture. Accumulating key information on municipal bond underwriting for the past 20 years, they can accurately self-describe the “oceans and oceans” of searchable data in their Muni Deal Query as the “Golden Record” for the market. It includes 150 fields covering $10+ trillion of municipal bond issuance across more than 320,000 financings, which translates to well over 4 million bonds.
Yes, it has the usual bond stuff about issuers, underwriters, coupons, maturities and so forth. But its real value for data scientists is to be able to effectively compare the factors in the dataset with, say, historical market technicals or issuer financials or economic indicators. With that, there can potentially develop accurate predictive issuance, pricing, and investor targeting models.
Start Me Up
It’s not just long-established firms following the underwriting process. Organizing the actual nuts-and-bolts of the process has also attracted a start-up, MuniChain. The firm’s technology provides “a workflow solution for municipal market participants”. This is perhaps the politest way of saying that a process involving issuers, underwriters, investment bankers, advisors, attorneys, analysts, and traders, can become such a disorganized mess that only software engineers can sort it out.
The best analogy is that MuniChain’s application is to bond underwriting as the Salesforce CRM is to the sales process. It keeps track of (and a record of) every participant, all communications, all document updates, all components of the debt structure, and the final pricing. Kiss the dreaded email search goodbye. By organizing, logging, and filing every step in the process, the app establishes a one-source-of-truth reference to turn back to when the inevitable “amnesia effect” occurs as everyone moves on to the next deal.
Data and technology are also taking on perhaps the thorniest market issue, the problem of pricing municipal bonds.
Here’s the problem. While the market may have over 50,000 issuers and $3.9 trillion in bonds outstanding, fewer than 0.10% actually trade. Think of it this way: if bonds don’t trade, how do you determine what the bonds’ market prices are supposed to be?
This is genteelly referred to as the market’s “structural problem”. From those few trades, independent price evaluation services such as Bloomberg and ICE have complex algorithms to extrapolate pricing across the market. But the lack of real, live trades for price discovery on over 99% of outstanding bonds is why municipal bond market valuations are described as opaque. At best.
It gets worse.
As fixed income investments, investment grade municipal bond prices are based off of a triple-A yield curve. The curve itself is comprised of interest rates ranging from one year to 30 years. If interest rates go up or down, that is reflected in the curve. Bonds priced off of the curve are adjusted accordingly. So if you’re buying or selling a bond, one way to determine a generic price would be to see where the yield curve rate is for that bond’s maturity and price the bond off of that.
But here’s the rub: what if you don’t have an accurate, up-to-the-minute yield curve to refer to?
Traditionally, the market has turned to Refinitiv’s Municipal Market Monitor (MMD) yield curves if only because there haven’t been many other competitors. The MMD is released only three or four times a day, depending on market conditions. Sometimes it is posted consistently, other times seemingly randomly.
Between these postings, investors, underwriters, traders, and anyone else involved in trying to price a bond accurately, are often left to the equivalent of an educated guess. Curves other than MMD exist (you can find those on the MSRB website) but most are subscription-based and are publicly available only at the end of the day. Not particularly helpful in a fast-moving market situation.
As in any market or sector, embedded incumbents with outmoded solutions are exposed and vulnerable to newer, faster, and more efficient technologies. MMD is no exception.
Enter ficc.ai and Spline Data, two hot new start-ups that are using data, artificial intelligence, and machine learning to tackle the pricing and yield curve problem.
The principals of ficc.ai (the company’s name is in lowercase) realized that given the size and illiquid nature of the market, no human or group of humans can offer real-time pricing. This is why the firm developed AI models offering accurate real-time pricing for the entire universe of municipal bonds, enabled by the latest advances in machine learning. Along with terms and conditions data, and current trading data for every bond, ficc.ai uses prices of Muni ETFs, changing up to the second and throughout the day, to capture the tone of the market at any given point in time. Consistent backtesting and rigorous analysis ensure price accuracy across the entire universe.
The other company looking to apply cutting-edge statistical analyses to create real-time yield curves and predictive pricing models from trading data is the appropriately named Spline Data.
[Fun Fact: “spline”—with the “i” pronounced as “eye”—is defined as “a piecewise polynomial function used to approximate a smooth curve in a line or surface” or, for those of us who are mere mortals, it is the math applied to a data set to create a continuous and irregular curve, like a Yield Curve.]
Spline Data openly measures and benchmarks its pricing and curve performance against actual market movements and trade prices. The firm is focused on predicting execution prices, “nowcasting” and not “forecasting”. Backtesting for variances and updating its models accordingly, the firm’s machine learning is always growing and building for greater accuracy.
The implications of more accurate pricing in a timelier manner will have a waterfall effect throughout the market, contends Spline’s founder. Better relative-value identification, tighter bid-ask spreads, more algorithm trading, and better execution—particularly in odd-lot trades of under $1M—will create an overall more efficient market, benefiting issuers and investors alike.
Musing further on what bringing standardized, structured financial data to the market could mean for pricing, he envisions the potential for curves based on or linked to financial metrics such as debt service coverage or days cash on hand—or any metric or basket of metrics. The potential combinations and analytics are only limited by the number of fields reported.
Credit and risk assessment is another critical issue for the market. Actual default risk in the municipal bond market is ridiculously tiny. Just turn to the US Public Finance US Municipal Bond Defaults and Recoveries, 1970-2020 report Moody’s Investor Service publishes annually. The venerable source of this longitudinal study tracks default data on 13,706 investment-grade rated bonds, determining the five-year Cumulative Default Rate (CDR) for investment-grade government-backed municipals at 0.04%. Having your investment-grade rated bond default is nearly the same as your odds of being struck by lightning. Even the 10-year cumulative default rate on theoretically “riskier” investment grade bonds backed by Competitive Enterprises was a meager 0.23%.
But credit risk is more complex than a binary default/no-default outcome. There are many metrics that affect the financial performance of an issuer and its ability to pay on debt. Arguably no business is more focused on this than the insurers of municipal bonds, which explains why Assured Guaranty Ltd. (NYSE: AGO) created AG Analytics earlier this year. This technology platform targets replacing fragmented data resources now in use by municipal asset managers, analysts, and other market participants. Currently, managers have to switch across multiple systems and data sources for credit research, trading data, and portfolio management. The AGA platform, incorporating dynamic data-driven analysis, offers the technological tools for analysts and asset managers to determine critical insights on obligor-level risk and resilience.
As noted, any market or sector with outmoded, outdated, and inefficient processes is ripe for newer, faster, and more efficient solutions. It will attract capital, technology, and expertise like a magnet attracts iron.
The municipal bond market is no exception. With the combined great forces of economics, capital, and regulation converging, technological innovation is going to prevail in this market as it has in every other market.
And while each of these companies and others are applying technology and data to specific parts of the market, it’s the overall effect that makes their work compelling. These technological advances being introduced will make the municipal bond market as a whole more liquid and efficient, benefiting all market participants in each of the specific ways they are part of the market.
In the end, technology wins.