Around 80% of all US corporate bond volume (based on dollar amount) is still traded telephonically, while 90% of all US equities volume (based on dollar amount) is traded electronically.
This disparity is quite shocking on the surface, but when you look under the hood of the two markets one can understand why this is the case. Equity markets are much simpler than fixed income markets. While a company such as Ford has a single equity ticker,
it has 17 different bonds outstanding with varying price levels and liquidity. Equities typically trade over an exchange while bonds trade over-the-counter, meaning liquidity is much more visible for equities compared to debt. Additionally, the bond market
is less transparent in pricing and trades less frequently than equity markets.
In 2017, the total US stock market dollar volume was roughly $98T while the US Corporate and Municipal Bond market trading volume in 2017 totaled
~$15T. The simplicity of equities and the size of the market led to a technological leap forward that fixed income has yet to experience. However, automation and artificial intelligence can help solve many of the operating inefficiencies of fixed income
markets beyond trading telephonically. They can resolve many of the problems that are detrimental to settlement, market data, reconciliation and other crucial functions in the fixed income markets.
Between 1996 and 2017, the amount outstanding in fixed income markets has grown nearly 400% from $11T to $41T. The growth of fixed income markets begets technological change in the space,
and blockchain, automation and AI have the potential to create massive change for the marketplace.
The transition from idea generation to trade execution in fixed income is still very inefficient. The analyst provides fundamental and quantitative research, screens, then filters investment ideas. The portfolio manager then has a discussion with the analyst,
chooses bonds and manually builds the order. The trader then goes to market to execute the trade, with no guarantee that the liquidity exists for the bonds chosen. This process can take anywhere from one day if things run smoothly to multiple weeks if there
are speed bumps along the way. In fixed income trading, timing is everything, particularly when yields and liquidity are not at their peaks.
Factors Cited as Greatest Opportunity for Growth Over the Next 5 Years
Emerging technologies will enable efficiencies to be realized that allow bond managers to transform the state of their investment operations. Historically siloed functions—fundamental research, quantitative research and liquidity analysis—can all happen
in tandem via technological systems, namely a decision enhancement tool that can filter through bonds and provide the portfolio manager with a list of viable investment options. The portfolio manager decides which bonds are the most attractive and then the
trader can execute the trade with more insights around where he or she can find liquidity across the broadest swath of the market.
Automation will play a key role in improving a multitude of processes in the fixed income lifecycle. Robotic Process Automation(“RPA”) essentially takes the robot out of the human. The average knowledge worker employed on a middle- and back-office process
has a lot of repetitive, routine tasks. RPA is a type of software that mimics the activity of an individual in carrying out a task within a process. It can do repetitive tasks more quickly, accurately and tirelessly than humans, freeing workers to do other
tasks requiring human strengths such as emotional intelligence, reasoning, judgment and interaction with the colleagues and customers. There are three main areas where RPA can play a key role in fixed income.
The first is trade processing: Using RPA to help with trade processing automates exception handling to create and send intelligent alerts based on certain exception criteria. It also eliminates the need to manually upload data files to back-office systems
since systems interact with one another. RPA also improves process quality and transaction processing volume by reducing the potential for human error.
RPA can also add value with reconciliation. Automated systems can help retrieve data in numerous forms from external parties and internal accounting and recordkeeping systems. Tools can format the information, compare data sets and—based on defined rules—make
corrections and adjustments. RPA can eliminate time-consuming reconciliation performed manually with spreadsheets. “Compared to human effort, RPA can retrieve
and prepare data sets in many different formats from external parties and compare based on predefined rules.”
The third is fund administration. Validation checks can be automated across multiple segments of the financial reporting process where the reporting is repetitive and rules-based. Eliminating the risk of human error from financial reporting would enhance
operational efficiency and reduce overhead for bond asset managers.
In 2018, State Street published a survey of 500 investment executives following a 2-year study to identify growth drivers for the industry. They found that emerging technologies such as blockchain and AI were believed to be the leading opportunity for growth
of their businesses over the next 5 years. Most shocking was the increase between 2017 and 2018, where this statistic jumped from only 18% to 48% of the surveyed population.
There are many emerging companies, and innovation teams at large financial institutions, working on developing applications using these technologies to address many of the structural issues that have plagued the fixed income markets. AI, machine learning
and blockchain stand to create ground-shifting change across such verticals as securities origination and distribution, clearance and settlement processes, credit underwriting and trade finance.
Confirmed Blockchain Transactions Per Day
Clearing and settlement of securities transactions have perennially hampered the middle- and back-office operations of broker dealers and asset managers. The fact that the normal corporate bond takes two days to settle has a lot to do with how our financial
infrastructure was built. Today, a simple bank transfer—from one account to another—has to bypass a complicated system of intermediaries, from correspondent banks to custodial services, before it can be accounted for on the records of an investor. Counterparties’
bank balances must be reconciled across a global financial system, comprising traders, funds, asset managers and more.
Distributed ledger technology could enable transactions to be settled directlyand provide more effective chain of custody reporting relative to existing protocols, like SWIFT. Rather than using SWIFT to reconcile each financial institution’s ledger, an interbank
blockchain could provide a single source of truth for transaction records. This means that instead of having to rely on a network of custodial services and correspondent banks, transactions could be settled directly among counterparties. Blockchain and distributed
ledger technology also allows for transactions to clear and settle when a payment is made. This stands in contrast to current banking systems, which clear and settle days after a transaction occurred.
The future of technology will cut out middle men and shorten transaction times, clearing the way for asset managers to offload busywork and focus on expending human capital where needed.