Ash Sharma, multi-asset trading analytics manager, Aviva Investors
Next year will be a very interesting year for global economic markets with the sticky inflation and interest rates still at the forefront of everyone’s mind, as well as the global impacts of Trump’s policies – effects already being felt.
On the analytics side, this will naturally impact implicit transaction costs as key market drivers like volatility, liquidity, and spreads, could change quickly and influence expected cost models’ outputs. These market impact models have vastly improved from when they were first introduced, however, have stagnated over the last few years given vendors’ push to cover more and more asset classes to improve their bottom line. A few have already productionised new releases to the cost models, and I expect others to follow suit as AI/ML analytical techniques become more sophisticated. I also expect more advanced models to be released for non-equity asset classes, as they attempt to catch up with the maturity of the equity analytics currently available.
This continued push towards AI seems inevitable however, it will be interesting to view how the tangible gains differ between buy- and sell-side firms, vendors, and venues. Fixed income analytics will see further electronification which will naturally improve timestamp accuracy for TCA purposes. This will be especially important for credit/EM markets where the move towards algos and e-trading has been historically slower paced than rates.
Dan Reid, chief technology officer, Xceptor
One of the most significant regulatory shifts set to reshape the financial industry in 2025 is preparing for the transition to T+1 settlement cycles in the UK and EU, following North America’s transition. With trades settling just one business day after execution, firms must process transactions and reconcile data at record speed.
The convergence of AI and data automation will emerge as a critical response, as firms deploy AI-driven automation tools to optimise internal processes, enhance predictive analytics, and automate tasks from decision-making to risk management, boosting efficiency and reducing operational risks.
As regulators intensify their focus on data accuracy, transparency, and security, unlocking new data sources, including unstructured and unconventional data types, will become essential. An emphasis on data lineage—the ability to trace data back to its source—will be particularly crucial for compliance and operational insights. Tools that provide clear, auditable data trails will become non-negotiable in the quest to meet stringent reporting requirements.
With 2025’s regulatory environment demanding unprecedented agility, firms must prioritise building adaptable systems that can evolve with changing requirements. By leveraging data automation tools, operations teams can independently manage processes, enabling firms to respond proactively as new regulations, such as T+1 settlement in the UK and EU, take effect.
Colette Garcia, global head of enterprise data real-time Content, Bloomberg
A growing trend we are continuing to see extend itself is the demand for real-time data across the industry. Consumption of real-time data is becoming a competitive differentiator and is growing well beyond trading, into portfolio validation, trade operations and risk and compliance functions.
Although the majority of functions rely on multiple frequencies of data, including but not limited to real-time, delayed, historical, and end-of-day, the consumption and use of real-time is essential to control risk and position management in alignment with the speed of trading in today’s world. The continued growth of algorithmic trading, regulatory pressures including T+1 settlement and compressed reporting times and automating away from labour-intensive and error-prone processes are key drivers, while advancements in API and technology adoption are core facilitators.
Multi-asset trading strategies require a wide variety of datasets and rely on tightly integrated systems. The ability to continuously access and interpret market volatility, unexpected events and risks associated with these events in real-time is becoming a fundamental expectation of trading systems. If done well, technology enables the ability to make complex data-centric decisions in real-time leveraging API and cloud advancements to mitigate previously existing friction and allow for faster solution integration.