However, fewer firms have the required level of data literacy, or the ability to assess the value in the transaction data which they themselves generate through their interactions with the market, from price formation to trade confirmation and settlement.
It is imperative to see the interdisciplinary collaboration between engineering, quant finance and data science as a strategic weapon. Typically, most firms see them as separate and distinct verticals and cross-pollination and driving horizontal integration between them remains challenging. This is where much of the value lies and tying them together in teams results in the most exciting outcomes.
For example, when a firm hires teams of data scientists, how can they possibly add value if they don’t understand the business and how their insights could be built into the workflow of users?
Most buy-side firms are sitting on a gold-mine of data, but it is siloed across different desks, venues and asset classes. Once this data is aggregated across all transaction channels and third-party feeds and analysed in real-time, the buy-side firm can begin to draw out a number of important and highly valuable insights from the data.
Let’s take, for example, questions around execution quality. For a buy-side trader, the quality of a transaction only becomes apparent after the event. The amount of information that the trader can access in real-time is extremely limited. This means that, when it comes to a trade which the trader might have time to handle over a longer time period, there isn’t much scope for adjusting strategy part way through.
Real-time data analytics which are built on the combination of the firms’ own transaction data supplemented with market data can show the trader, in real-time, exactly how the market is reacting to their trading activity. It allows them to understand whether the market is moving away from them when they trade with certain dealers or whether their counterparties are internalising their flow, which is having no impact on the market at all. Clearly the latter is the preferred option.
This kind of analysis, which we call transaction quality analysis, goes beyond best execution analysis and gives a much finer grained and ‘360 degree’ view of the transaction.
Crucially, delivering these insights in real-time can allow traders to adjust course throughout the day and learn much more quickly from the data. This is absolutely critical for today’s automated business environment. And if the full organisation – from traders to investment strategists, quants, managers, monitoring, compliance and credit teams – are using the same data source, the efficiency gains will be dramatic.