Eric Heleine, head of trading desk at Groupama Asset Management, delves into the growing role of technological innovation and automation in buy-side trading.
This article first appeared on Best Execution, a Markets Media Group publication.
There is a significant shift happening in the world of buy-side trading. Innovation and automation are not just buzzwords; they are integral components driving the evolution of trading desks.
Technological innovation in buy-side trading
The role of research analytics in trading workflows has seen a substantial increase, transforming the way buy-side traders operate. Traders now leverage advanced analytics to make informed decisions, enhancing their trading strategies and execution. This shift towards data-driven decision making has fundamentally changed the trading landscape, enabling traders to uncover hidden patterns, predict market trends, and optimize their trading strategies.
Machine learning, a subset of artificial intelligence, is one such innovation that is making waves in the trading industry. The introduction of language model learning (LLM) for finance, for instance, has opened new avenues for traders. LLM models, trained on vast financial datasets, can generate valuable insights, predict market trends, and even automate certain trading decisions.
These models can analyse vast amounts of data in real-time, providing traders with actionable insights that were previously inaccessible. This can lead to more accurate predictions, better risk management, and improved trading performance. Furthermore, these models can learn and adapt over time, continually improving their predictions as they process more data.
However, the application of machine learning in electronic trading is still in its early stages, with a noticeable gap between academic research and professional practice. Bridging this gap is crucial for the full realisation of machine learning’s potential in electronic trading. This requires ongoing collaboration between researchers and practitioners, as well as a commitment to continuous learning and adaptation in the face of rapidly evolving technology. Traders must learn to navigate into the complexities of these models and ensure they are used responsibly and ethically. Regulatory bodies are also grappling with the implications of these technologies and are working to develop regulations that ensure their fair and transparent use.
Automation in equity and bond trading
The world of trading has seen a significant transformation in recent years, with automation playing a pivotal role. Automation in trading brings a host of benefits, including risk reduction, measurable outcomes, and cost savings. It has become an integral part of many businesses across the financial services sector. The role of traders is also evolving due to the adoption of new technologies.
The buy-side trading profession has shifted to becoming a role for tech-savvy individuals with a keen focus on data. With the rise of automation and trade expectation management, Buy-side traders can dedicate their time and expertise to more complex and demanding orders, ensuring that bias is removed from trading activities. Automation allows traders to perform their duties more predictably and efficiently, leading to faster, more informed decisions that cause minimal market disruption. This new reality has prompted traders to acquire new skills, such as Python scripting and data analytics, and adapt to the ever-changing trading industry landscape.
One of the advantages of automation in trading is the improvement in performance measurement and trade performance analysis. Automation can only be applied to measurable concepts; therefore, machine learning plays a significant role in facilitating this transition. Machine learning is a crucial tool used to capture and measure the vast amounts of available data. It can be used to develop algorithms that follow market trends and match price orders to be as passive as possible. These algorithms can then be fully automated and supported by machine learning and artificial intelligence. Another advantage of automation in trading is trade exception management. By incorporating technological advancements at the trading desk, traders can enhance the performance of existing processes and save time and money for clients.
Machine learning is being used to enhance liquidity management in equity trading by analysing vast amounts of data to predict market trends and identify optimal trading opportunities. This allows traders to manage their orders more effectively, ensuring they can buy or sell shares without causing significant price changes. Machine learning algorithms can also analyse historical trading data to identify patterns and trends, which can be used to predict future liquidity conditions. This can help traders to plan their trading strategies more effectively and manage liquidity risk.
However, the use of automation and machine learning also presents challenges. One of these is the need for large datasets to create coherent buckets and identify patterns. The accuracy and reliability of machine learning models depend on the quality and quantity of the data they are trained on. If the dataset is too small or not representative of the market conditions, the models may produce inaccurate predictions or fail to identify meaningful patterns. This could lead to sampling risks, where the model’s predictions are based on a biased or unrepresentative sample of data. Traders and firms need to ensure they have access to sufficient, high-quality data to train their models effectively and mitigate these risks. This forces buy-side firm to question their size, and the investments in tools and people they must or can make
The future of buy-side trading desks
As we look to the future, it’s clear that innovation and automation will continue to shape the landscape of buy-side trading desks. Traders will need to adapt to these changes, developing new skill sets and embracing new technologies.
One such trend is the shift towards ‘trading by exception’. This approach involves using intelligent, best-execution automation tools for a significant percentage of all orders, freeing up time for traders to focus on more complex and demanding orders. This shift not only enhances trading efficiency but also allows traders to provide valuable insights, potentially adding hundreds of basis points to performance.
However, the future also presents challenges. The rapid pace of technological change means that traders must continually update their skills and knowledge. Additionally, the increasing reliance on automation and machine learning models raises questions about transparency, accountability, and the potential for algorithmic bias.
The future of trading will be heavily influenced by AI. It’s not a risk, it’s an opportunity. To fully embrace this technological revolution and achieve the necessary critical size, buy-side trading desks must invest in their technological infrastructure. This involves not only expanding in terms of people, skills, and resources but also in terms of technological capabilities and infrastructure. These investments are crucial to support the advanced analytics, machine learning models, and automated trading strategies that are becoming increasingly central to buy-side trading.
Conclusion
In this era of digital revolution, no form of potential alpha generation should be overlooked. Buy-side trading desks must embrace innovation and automation, leveraging them not just for operational efficiency but as valuable parts of the investment process. While challenges exist, the opportunities presented by technologies like machine learning and automation are too significant to ignore.
The urgency for buy-side trading desks to adopt advanced technologies such as LLM is not a question of three to five years, but rather one to three years. Those who fail to adapt quickly risk falling behind in an increasingly competitive landscape. Achieving a critical size, in terms of both personnel and technological capabilities, is essential for buy-side trading desks to meet these ambitious objectives. As we move forward, the role of the trader will continue to evolve, shaped by the ongoing interplay of technology, regulation, and market dynamics. The future of buy-side trading will be defined by those who can effectively harness the power of innovation and automation to generate alpha and deliver superior performance.
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