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By some estimates, there are over one million fixed-income securities issued by U.S. state and local governments to fund projects like schools, highways, and utilities. Each of these securities has unique tax treatment, credit ratings, and yield structures. They primarily trade in an "over-the-counter (OTC) " market with transactions occurring directly between parties rather than on centralized exchanges.
This decentralized structure leads to less frequent trading, as many investors adopt a “buy and hold” strategy, resulting in few daily trades. Some bonds are even less liquid, trading only by appointment. This means they don’t trade continuously like stocks. Instead, trades occur when a buyer and a seller agree on a price, which can happen sporadically. Because they trade OTC, pricing is determined through negotiation rather than a centralized exchange.
Each municipal bond has unique characteristics – issuers, maturities, credit ratings, and potential tax treatments – making price discovery more challenging than for liquid, standardized securities like Treasuries. Unlike corporate bonds, municipal issuers follow different accounting standards, making financial comparisons difficult. Legal protections for bondholders vary by state, and political factors like pension liabilities and tax policies impact creditworthiness. This structure has historically led to wide bid-ask spreads, meaning the difference between what buyers pay and what sellers want can be larger. Most municipal bond trades require an intermediary, such as a broker, to facilitate transactions. Investors looking to buy or sell municipal bonds must often work with brokers, and buy on the offer, rather than executing trades instantly.
Compared to other markets, the marginal buyer in the municipal space is the retail investor (or their advisor). According to Invesco, roughly 44% of issues are owned by individuals, who seek after-tax yield and decent credit, while another 27% is held in co-mingled vehicles (like municipal bond mutual funds, ETFs or separately managed accounts) aimed at the same client base.
These idiosyncrasies mean municipal bond portfolios are managed by specialized asset managers with teams of expert analysts. Once someone becomes a bond analyst, they tend to stay one. But despite the asset class’s complexity, management fees and expense ratios aren’t generous – and like the rest of the industry, they’ve seen pricing pressure.
The role of artificial intelligence
Heterogeneous data, fragmented markets, invaluable domain knowledge, and margin pressure highlight areas where AI can be transformative. However, a simple technology solution without business context will be frustrating. A solution developed with the workflows, relationships, and institutional capabilities of a particular investor can be transformative.
In some ways, the situation for municipal bond managers resembles that for alternative investors: Both depend on proprietary deep-dive research on securities or transactions that have been hard to scale. Underwriting involves information from disparate sources and non-standard formats, with data that may be incomplete, stale, and multimodal.
How do you price a bond that hasn’t traded in days? How do you build a portfolio for a client who favors Mississippi GO bonds when you’re structured to buy revenue bonds from larger issuers?
"Agentic AI" – a form of AI that combines machine learning, large language models, and automation technologies to make decisions and perform tasks independently – is rapidly emerging as a transformative tool for navigating and clarifying opaque financial markets. KAIA (Kapital AI Analyst), an agentic AI technology developed by Praxis Solutions, is a fully bespoke, client-specific approach that simultaneously builds on the learnings of every previous implementation.
This framework is, in turn, informed by capital markets experts, crystallizing the intellectual capital of clients and uncovering insights that dozens of (human) analysts cannot see. KAIA squeezes out inefficiencies from the investment process; eliminates noise and frictions that can lead to blind spots; and is seamlessly integrated between and across existing infrastructure.
This new school of agentic AI solutions can be seamlessly integrated with other functionality and tools. For example, a comprehensive solution might integrate a workflow engine, compliance, and audit tracking directly into the underwriting process – rather than as an afterthought or add-on module, as is the case with a traditional SaaS construct. In the age of AI, automating routine tasks in intelligent ways compounds cost savings as the process scales.
The application of KAIA to real-world client challenges has delivered 70% reductions in raw underwriting time just from automating the investment process. In a market as broad, opaque and inefficient as the municipal bond market, in which compliance oversight and efficiency are paramount, these initial efficiency gains are just the tip of what may prove to be a transformative technology iceberg.
John Sweeney, President of Praxis Solutions, combines a deep background in finance and investments with advanced technologies, including AI and blockchain, to revolutionize wealth and asset management. Formerly of Fidelity Investments and Figure Technologies, John is dedicated to creating tailored solutions that boost performance and drive meaningful change for financial institutions.
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Advisor Perspectives welcomes guest contributions. The views presented here do not necessarily represent those of Advisor Perspectives.