By Tim Lind, Managing Director, DTCC Data Services
As technology has introduced new ways to manage, compile and interpret the vast amount of data across the financial services industry, post-trade data has emerged as a new source of information that can unlock greater insights and understanding of market dynamics. While leveraging post-trade data has often been the focus of firms’ back-office teams to measure operational efficiency, there are increasing opportunities to use historical transaction activity for front office use cases. Specifically, firms’ front office teams, including systematic traders, can leverage this data to gain increased transparency around market momentum and ultimately help to create additional alpha.
In order to understand how this data can be leveraged, it is important to consider how post-trade data was originally created through the automation of clearing and settlement. After all, the primary role of the back office has been and remains to process transactions efficiently, create scale for the business, and manage operational risk. Historically, the case for automating back-office functions focused on headcount, reducing operational costs and risks, and decreasing the time needed to resolve trade exceptions. This meant that the back office has traditionally been viewed as a cost center rather than a driver of alpha.
In the process of digitizing back-office workflow processes, transaction histories turned into a new, compelling data asset. By capturing the history of post-trade events including trade confirmation and clearing and settlement, new data sets have emerged to unlock insight into trade flows, including valuation, liquidity, risk and market sentiment.
For innovative investors, post-trade data can be analyzed from raw data sets to actionable insights on pricing dynamics and macro trends. Buy-side traders armed with the wealth of back-office data have an advantage, using empirical trade data for price discovery and to achieve better outcomes for investors. Ultimately, sophisticated traders that have been able to capture alternative data in multi-factor models can gain greater insights into where the market has moved and what might be next.
In today’s investing landscape, it is not surprising that there is an increased focus on alternative forms of data to inform investment decisions. In fact, large hedge funds are already recognizing the power of back-office trade data and are increasingly devoting resources to data management and data science capabilities and expertise. Traditional long-only asset management firms are typically behind sophisticated hedge funds around their data management capabilities and use of modern technology. In response to this, data and analytical providers will be looking to deliver new solutions targeting traditional asset managers that will allow them to stay competitive by leveraging novel alternative data products. At the same time, market structure continues to evolve in every asset class, with greater participation of retail investors in equity markets to challenges in understanding liquidity in corporate fixed income markets. Investors are faced with the on-going challenge of understanding market behavior and how to capitalize on innovation.
In a time of continuing market volatility, leveraging new sources of alternative data, like historical transaction data, can create value and drive powerful new insights. From increasing transparency on price discovery, to identifying market-changing forces, to ultimately creating alpha, post-trade settlement data can support investors as they look to identify trends that are shaping the industry and seek to capitalize on market opportunities.
