Data & Analytics: The building blocks for differentiation
In the recent Data & Analytics Race webinar hosted by BMLL and The TRADE, industry leaders from Berenberg, Redburn, SIX, Newton Investment Management Group and BMLL explored how historical data helps firms gain deeper insights across the trade lifecycle to deploy more sophisticated strategies and gain a trading advantage.
More and more firms are looking for data insights across the trade lifecycle and are deploying an increasing number of sophisticated strategies to gain a trading advantage. This involves analytics that contextualise real-time activity against historical averages.
Access to the highest quality data is now the new frontier in capital markets and is considered a vital differentiator. For firms having to manage a huge, cumbersome historic trade database, this effectively means having a full-time job simply normalising that data - when they should be focusing on analysing that data for valuable information and insights.
The data challenges
The processes for analysing historical data, whether for pre-trade decision making models or post-trade analytics, are getting increasingly sophisticated due to the technology changes related to querying, analysing and running data. But in order to achieve intelligible results, data science modelling still needs to be fed with high-quality, clean data. There is a real recognition that being able to get good quality data is what sets people apart.
What does that mean for the buy-side?
The buy-side wants to be able to make faster, more systematic and consistent evaluations of different brokers and algorithms to drive better trading performance. By accessing the foundation of quick, reliable data, the buy-side can better understand retail flow and see what liquidity is addressable, whether to reduce trading costs or find alpha.
What does that mean for the sell-side?
The sell-side is looking to optimise venue interaction, better understand liquidity capture, improve algorithmic performance, reduce impact, and ultimately improve trading costs. The sell-side can improve executions and better understand trades which can then be fed into their algo execution logic. For example, queue dynamics can be analysed by clustering algos, helping firms to optimise their smart order routers and execution logic.
What does that mean for venue operators?
Exchanges need to use data and analytics to achieve a better understanding of market dynamics, to benchmark performance against competitors, to optimise operation of product and solution design, and to provide contextual analytics for market participants. Exchanges can add new market types, as well as build out reports and intelligence around new venues. For example, rich, standardised public market data can be overlayed with private client data to support new products that can actually drive a meaningful liquidity outcome for clients.
Build on a solid foundation of quality data
Across the entire trading ecosystem, firms are excited about the value that can be achieved with the analysis of historical data, whether that be best execution, looking for alpha, or a better understanding of market quality and venue interaction. But this journey can only start with good quality data.
Wherever they sit upon the value chain, firms that want to gain a competitive advantage should look at their respective added value instead of spending time and effort managing, curating, harmonising and normalising a tick dataset. Organisations can outsource this task to a provider whose central value proposition is precisely this, and who can do it at a fraction of the cost. By using BMLL’s foundation of high-quality, accessible, clean historical market data, firms can focus on adding their unique insights and value-add.