Down the Securities Master File Rabbit Hole...

I continued to work with Nasdaq’s DataLink Retail Traders Activity Dataset this past week. I had set out to enrich the data and was pulling corporate actions from various sources I had access to when I realized that I would need a way to map all of the multiple data sources to enrich this dataset.

I rolled up my sleeves and started researching the best way to accomplish that goal which got me thinking about master security files and the current value of data, the overall landscape of data vendors, and the considerable amount of data sources available today within the marketplace.

Data as a Competitive Advantage

Traders and Investment Managers are always searching for improved ways to quickly access accurate and consolidated investment information to find new trading ideas, make strategic trades, monitor portfolio performance, calculate attribution, and manage compliance.

When data is used as a competitive advantage, accurate data becomes the foundation of any investment portfolio or trading strategy. Therefore, we can state that data shapes our understanding of trading opportunities and how market fluctuations and risk exposures impact our investment decisions. A consistent, timely, agile, automated, and scalable data management solution is the key to meeting the requirements of accurate and timely consolidated data assets.

When dealing with many interfaces and countless rules managing incoming data flows, we must have an efficient way to collect and store all of the data accurately. An Investment Securities Master File is a handy way to organize data for investors who need an instrument catalog or obtain financial data from various external and internal feeds and other sources.

Therefore, we can surmise that the investment securities master file is the foundation for managing the complexity of multiple data sources that contribute to our understanding of the markets and execution of our investment strategies.

The Investment Security Master File

When creating a security master or golden reference copy, we must consider current and future data vendors and what identifiers they might use for the securities within their datasets, assuming non-alt data; thus, symbology, the data necessary to map from one feed identifier to another, becomes the critical component of the master file and the main subject of this discussion

While there are frameworks that we can leverage for the architecture, storage, and governance of data assets; as well as assessing and managing the life cycle of said data assets; along with exploring the practical uses of the various categories of data: metadata, reference data, and master data; and while some aspects of those frameworks can benefit an individual investor, my objective is to keep things within the realm of simplicity and ease of use with very few rules and remain highly agile and won't focus on the available frameworks.

Data Sources & Vendors Landscape

There is an abundance of data sources available to all investors, both freemium and paid, across all asset classes, so we need to use a security master file to consolidate the information from multiple sources.

Several vendors specialize in providing Security Master Files to organizations (DTCC, S&P CGS, EDI, Bloomberg, IHS Markit, etc.). Those are out of the realm of possibilities for individual investors.

We can think of most data vendors as falling within two overall categories: Collectors/Consolidators and Originators of data.

Collectors/Consolidators will capture, cleanse and normalize the data from various feeds and consolidate them within their platform, making them available to customers: Maystreet, AlphaVantage, AlgoSeek, Polygon.io, IEXcloud, Intrinio, EDGAR, WRDS, etc. are good examples.

Originators are those vendors that perform the above functions and that develop their own unique data sets: Nasdaq Datalink(formerly Quandl), CBOE DataShop(MDX+LiveVol), ICE Data Services (formerly IDC), Bloomberg, Refinitiv, Xignite, TagniFI, AlphaSource, Statista, FRED, Morningstar, TradingEconomics, SDC Platinum, SESAMm, Thinknum, etc.

Institutional Hat Perspective Thoughts

Security Master Files are not foreign to Capital Markets sell-side institutions, which have dedicated teams to collect, curate, manage and govern financial market data. This team's focus is solely on Market Data and less on data assets generated from customer data or banking operations. In my experience, they represent a significant portion of an organization's operating budget. They provide research data and create metrics from the market data collected to comply with regulations such as FINRA rule 5310 – Best Execution, MiFIDII RTS 27/28, and MAS SFA 04-N16.

The Security Master file also provides financial product reference data. It is the foundational building block of the quantitative financial analytics engine on the buy-side, supporting TCA, order routing and volatility analytics, algo-wheel performance, trade desk, risk support, signal generation, and other use cases.

Over the past six to seven years, companies like Maystreet, Xignite, AlgoSeek, Intrinio, Polygon.io, and a few others have emerged in the marketplace to fill a specific need within Capital Markets. In the case of Maystreet and Xignite, they provide global market data access and a one-stop-shop to the sell and buy-side around market data management.

They introduce significant TCO efficiencies from streamlined processes and eliminating infrastructure management costs for their clients, allowing organizations to shift their focus from data capture and management to workflow optimization, improving time to market for any strategy.

Bloomberg deserves a special mention, given they continue to innovate and add features that support the buy and sell-side quantitative financial analytics and have wholly embraced python. Bquant is an excellent example of BLP keeping up with the times and a well-integrated tool with complete access to their entire data asset library.

To be continued

The following article will deal with more implementation and less commentary and thought process.

Disclaimer: The views expressed are solely those of the author in his private capacity and do not in any way represent the views of past/current employers.