What are the key factors contributing to the rise of credit portfolio trading?
Portfolio trading has seen a dramatic rise these past few years. It involves trading a basket of bonds of variable credit quality and risk as a single, all-or-none transaction, whereby the trade instruction specifies that the entire order must be filled. It’s not very different from list trading which has existed for a long time where dealers were requested to price a backet of instruments. However, in this scenario, traders don’t have the option of choosing the bonds included in a portfolio. Despite this, portfolio trading has become increasingly attractive.
The rise in credit portfolio trading can be attributed to a combination of factors: primarily, the advances we’ve made from a technological standpoint and recent regulatory changes.
On the technological front, the ability to analyse data has improved dramatically in recent years. Data analytics has allowed traders to optimise portfolio pricing and risk management, for instance, by looking at historical data and market conditions. In addition, algo trading has made portfolio trading more efficient by automating workflows and streamlining execution.
The pandemic and working from home strengthened demand for more efficient trade executions. Moreover, financial market regulations such as Mifid II have helped drive more post-trade transparency, audit trails and proof of best execution.
What are the key challenges linked to the adoption of portfolio trading and what innovations are helping combat these issues?
One of the top challenges is the complexity surrounding pricing. It is difficult to accurately price a diverse portfolio of bonds as different bonds have different amounts of data available on them; therefore, establishing a precise value for a portfolio with multiple bonds can be difficult. Additionally, portfolios frequently contain both highly liquid and illiquid assets, which makes it difficult to find buyers willing to accept a portfolio with such a mix of bonds, potentially posing significant risk.
In addition, regulatory changes in recent years have made it more costly for banks to hold on to riskier assets. Financial institutions have become reluctant to have inventory on their books, with their risk warehousing capabilities significantly reduced.
Innovations in algorithmic trading, improved data analytics, and new technologies are helping to combat these challenges. Algorithms can help significantly in offloading the risk of a portfolio. For instance, traders can automatically offload less-desirable bonds from their portfolios by identifying the best times and price to sell them. In addition, better data analytics and new technological capabilities are helping traders to be more efficient in portfolio construction and risk assessment. Data analytics and AI tools can analyse huge volumes of data accurately, which helps traders judge the balance and risk of a portfolio transaction and make better decisions.
What are the key trends likely to shape the future outlook of credit portfolio trading?
While automation is already underway, big data and AI will continue to be at the forefront. The vast amount of data that is generated by electronic trading can be processed using AI to generate more sophisticated pricing, portfolio construction, risk analysis, and execution. Significantly, this data can be processed by AI to make decisions almost instantaneously, it is much faster and more accurate than what a human could do.
In parallel, as portfolio trading continues to grow, so will regulatory scrutiny. Portfolio trading often involves high-value trades with high capital requirements. Therefore, regulators are keen to manage risk and ensure there are no unscrupulous trades. This is important in preventing potential financial instability and ensuring that as portfolio trading becomes more widespread, it does not lead to market disruption. New regulations around credit portfolio trading are, therefore, highly likely in the coming years, with the potential to alter the market environment around portfolio trading.
Finally, ETFs are playing a significant role in shaping the outlook of credit portfolio trading as they increasingly become the natural tool for liquidity and risk management while entering portfolio positions and adjusting the derived risk profile.
What is the wider role of credit portfolio trading within the broader fixed income landscape?
Credit portfolio trading plays an important role in enhancing liquidity and promoting greater efficiency. By bundling multiple bonds into a single transaction, portfolio trading promotes liquidity for bonds that might not trade independently. Importantly, this helps to improve the liquidity of the entire fixed-income market whilst at the same time protecting the leakage of information during trade execution.
Portfolio trading is significantly more efficient than trading individual bonds. Previously, if traders were looking to sell 10 bonds for example, it would take a significant amount of time, with an individual having to make multiple calls to sell even a single bond. With portfolio trading, by bundling bonds together, this process is much more efficient.
This process promotes better practice in the fixed-income market as a whole and reducing the time and effort needed to execute trades, reducing the potential for errors while ensuring best execution.