Mechanized Financial Transactions
Artificial Intelligence in the Municipal Bond Market: Employing Algorithmic Trading and AI Forecasts
The term "mechanized financial transactions" is currently trendy. But what do people actually mean by this term?
Let's dissect it into its components.
Defining an algorithm isn't rocket science. In fact, it's quite straightforward. An algorithm is a collection of rules or instructions that enables a computer program to process different types of information [data] and produce an outcome.
If you're talking about algorithms, you're also talking about AI and data. Both concepts are relatively straightforward. At its core, AI is merely a technique for organizing, analyzing, and utilizing data for a specific purpose. Data, in essence, is anything that can be digitized and assigned a numeric value. Photographs, text, and even speech can be digitized and given a numerical representation.
The outcomes that can be derived from analyzing data can range from determining the average value of a dataset to the sophisticated predictive analytics required for self-driving cars. Analyzing the results and then refining and adjusting the program and data inputs to improve the outcome is what machine learning and deep learning are all about.
As for transactions, it's safe to assume that if you're reading an investment column on Our Website.com, you're familiar with the concept of buying and selling bonds, stocks, options, and other financial instruments.
Mechanized Financial Transactions in Practice
So how does all this come together as mechanized financial transactions?
James Pruskowski, Chief Investment Officer & Co-Founder at 16Rock Asset Management, offered a general example. By integrating AI and algorithms, the firm's municipal bond trading models regularly monitor market data - inputs such as trades, volume, spreads, bid wanteds, and request for quotes. One model is programmed to identify arbitrage opportunities based on pricing anomalies discovered by analyzing market data. With regularly updated buy and sell parameters, the computer places bids and makes offers.
An entirely automated process, this can all happen in a matter of seconds. The new trade data is uploaded, analyzed, and the model is updated to determine the best opportunities given the new information. And the cycle continues.
Noting the increasing influence of AI and mechanized financial transactions on the municipal odd lot market (an odd lot trade comprises a block of bonds valued at $100,000 or less), James also observed that separately managed account (SMA) asset growth and municipal ETFs were compelling many market participants to be price takers.
Whether it's a Financial Advisor or ETF fund manager, when faced with a pressing need to invest or sell, operational complexities force both and their executing traders at large retail wirehouses to be price takers. These prices tend to reflect the higher yielding asking price versus the lower yielding bid price. Consequently, levels on market benchmark curves (such as the ICE US Municipal AAA Curve) derived from these trades reflect these price takers' lower price, higher yielding trades. This, in James' view, defines the opportunity set in today's market.
Josh Rosenblum, Head of Algorithmic Trading at Brownstone Investment Group (previously head of Municipal Trading Strategies at the firm), offered a different take. By combining the billions of assets under management held by mutual funds and SMA advisors with electronic trading platforms, he sees buy-side firms moving from price takers to price setters. Moreover, with those platforms, buy-side firms can trade directly with each other. The relationship between broker and client is evolving into more of a partnership to develop mutually beneficial workflows.
He has seen evidence of tighter spreads in the odd lot market due to mechanized financial transactions, also noting that AI-generated predictive pricing analytics creates a self-fulfilling pricing prophecy. If the model predicts the next trade on a bond will be valued at a certain amount, the maximum buy or sell price a buyer would be willing to pay is going to be at that price or very close to it. Prediction is shaping reality. Moreover, the counterparty "buyer" is increasingly likely to be another AI-driven model. While this may have positive implications for market liquidity, computer and model dependency have their own pitfalls.
While speaking independently, James and Josh concurred that AI, electronic trading platforms, and mechanized financial transactions are creating a technological arms race. If you're not the leader, you're behind, which is accelerating technological adoption because no one wants to be left behind.
This is particularly true when it comes to execution speeds. If you aren't trading faster than your competitors, you won't get bonds. No bonds, no performance. No performance, no investors. It's a simple as that.
Chatbot is a system based on an algorithm, specifically a type of machine learning model known as a language model. More precisely, it's built on the GPT (Generative Pre-trained Transformer) architecture, which is a deep learning model designed to comprehend and produce text that seems human-like. It's not a single algorithm but a complex system involving various components. The overall process can be described as an algorithm that uses machine learning to generate responses based on patterns it has identified in the data it has been exposed to.
In truth, the entire previous paragraph was crafted by this very chatbot.
Chatbot is an intriguing technology. Whether you're planning a Caribbean trip, writing Python code, or learning about the latest hip replacement surgery procedures, almost everything you can think of is within your grasp. By asking a question, you can get an answer in a matter of seconds.
However, interactive technology isn't just about typing text for a response. Anyone who's conversed with Apple's Siri or Amazon's Alexa can attest to that. Conversely, Speechify can transform any text, PDF, or email into audio and read it back to you using high-quality digital voices. Our Website.com even offers an audio version of this article for your listening pleasure. (I'm afraid I can't take credit for that voice.)
If videos are more your thing, Pictory offers text-to-video, URL-to-video, PPT-to-video, and several other video-converting capabilities. You can paste this entire article into Pictory to see how it transforms my simple words into a Hollywood-style video. (Unfortunately, I don't make appearances in it. Sigh. I could really use a new agent.)
In summary, the capabilities of interactivity are virtually limitless. If you can think of it, AI can make it happen in any communication medium.
Even talking to municipal bonds isn't out of the question.
If you're curious about why you'd want to chat with your bonds, talk to Robert Kane and Saul Tessler of Munibonds.ai. Founded in 2024, this firm has an AI chat function where you input a question about a bond, and the app produces a detailed response. This user-driven functionality is far more advanced than the limited, predetermined drop-down menu options.
If the information is available, from the Preliminary Official Statement to the most recent quarterly financials, the app can find it and generate a response. It's as close as technology gets to making the municipal bond market's PDF-laden disclosure documents machine-readable. For now.
Munibonds.ai is more than just an interactive search function. It tracks both user queries and other market data, offering market sentiment analysis on any bond, portfolio, or bid-wanted list. It also provides a numerical "bond ranking." While it's not quite a credit rating, with the right user prompts, the report generated by the chat technology, including some fundamental analysis, is quite close.
By setting customized need parameters and automating that function, everyone from portfolio managers to compliance officers can have access to credit, structure, use of proceeds, market sentiment, and other decision-making or reporting information on any bond or portfolio of bonds in nearly real time, any time they ask.
AI Predictions for the Municipal Bond Market
Consider a perfect Laplace AI model. With complete knowledge of every piece of data in the universe, past and present, it could achieve omniscience, enabling perfect predictions. Whether the result would be an angel or a demon is up for debate, but after reading about AI, it's hard not to sense that this is the ultimate goal of those developing and applying AI.
Of course, this is impossible. But for the municipal bond market, even if AI provides results that are almost perfect, it's a far better outcome than what exists now.
So here are the top AI predictions for the municipal bond market:
- The pace of technological advancement and adoption across the market will increase.
- Any processes that can be automated will be.
- Market consolidation will continue, affecting broker dealers, investment advisors, and trading platforms.
- The market will become less opaque and fragmented.
- A primary market electronic shelf-financing mechanism will evolve for municipal bond issuers.
- The market will adopt standardized, digital official statements, a consistent reporting taxonomy, and machine-readable official statements.
- The pricing differential due to the current structural imbalance between odd-lot and institutional trade valuation will decrease.
- To remain relevant, credit ratings will be attached to numerical rankings that are more probabilistic.
- Climate change metrics will have a greater impact on credit analysis and ratings.
- These market evolutions will occur faster than many are expecting or are prepared for.
Remembering Nobel laureate physicist Niels Bohr's observation that predictions are difficult, particularly about the future, it's easy to dismiss any prediction with a "that can never happen" wave of the hand.
However, it's worth keeping in mind that when it comes to technology, the question isn't "can it be done" but "how can it be done" and "how fast can it be done."
Because if history has shown us anything, it's that technology always wins.
This marks the sixth and concluding piece in the series about, AI and the Municipal Bond Market. Previous articles delved into AI's impact on, pricing, economic factors, data (and even more data), credit ratings, and Alternative Trading Platforms.
The following podcasts align with this series:
*AI: The Answer to Our Issues, The Muni Matrix, Munichain, May 8, 2024*
*Understanding Municipal Market Movements with Barnet Sherman, DebtBook/Where Public Finance Thrives, June 18, 2024*
*The Evolution of Bond Trading—A Look Ahead, Forefront Communications, September 19, 2024*
- AI and alternative trading platforms (ATS) are revolutionizing the municipal bond market, enabling computers to analyze market data and make trading decisions in seconds.
- Chatbot systems, like those using the GPT architecture, can provide human-like responses to a wide range of queries, including inquiries about municipal bonds.
- The use of AI and ATS is leading to a technological arms race in the financial industry, with firms constantly looking to improve their execution speeds and machine learning capabilities to stay competitive.
