Organizing Your Securities Data with a Security Master
April 25, 2024
Read Time: 14 minutes
Technical Article
In our current market, an average of 5.1 billion US equities trades occur on a daily basis.1 Trillions of records including trades, transactions, and positions are derived from financial market activity across the globe. The majority of these are centered around a security — the asset or instrument that is traded or invested in the financial markets. Investment managers trade and hold positions on multiple securities that form part of a fund or portfolio. Securities carry hundreds of attributes that are used between various areas of the business from portfolio analysis, reconciliation, and investment accounting. In the world of finance, data accuracy is crucial for success. Maintaining a true representation of all securities of interest throughout an organization is vital for front-office decision-making, trade operations, and risk management of investment lifecycles.
Investment firms continually grapple with vast amounts of data. Incoming files and datasets from order management systems, market data vendors, brokers, and financial market participants each carry their own security naming format, descriptor, identifier, and other attributes that differ between sources. These dispersed datasets also comprise ratings, liquidity scores, payment schedules, corporate action treatment, and related metadata that needs to be collated and mapped to drive processes across systems. Capturing, extracting, and mapping this data is often challenging, not only due to inconsistencies in formats, but also in keeping pace with the frequency and volume of data accumulation.
Akin to the role of a primary key, a security serves as the common denominator between associated trade files, positions, returns reporting, and accounting. Clean, consistent, and centralized securities data is essential to power data science initiatives, and to attain integrity across trade capture, asset servicing, and accounting processes. Yet, interpreting and reconciling data between multiple disconnected sources is often a manual and resource-intensive undertaking, ultimately leading to errors and increased operational risk.
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Security master systems are designed to serve as a comprehensive, unified solution for organizing a firm’s securities and reference data. A security master forms a core component of a firm’s investment ecosystem. It feeds downstream systems across dispersed teams and tightly integrates with the straight-through processing trade lifecycle. While front, middle, and back offices each use data differently to accomplish distinctive tasks, a combined source of securities substantially increases opportunities for growth and decreases repetitive comparison between datasets.
The primary benefit of a security master is its ability to establish a harmonized, consistent, and standardized set of security data across an organization. A golden source of securities data serves as a firm-wide single source of truth. A security master enhances operational efficiency between various functions, from front-office traders to back-office accounting, that regularly reference and utilize data centered around a security.
Modeling securities, supporting new asset classes, and capturing custom attributes are continually revolving requirements. Modern, cloud-based security masters are purpose-built for the investment industry, supporting numerous asset types including stocks, bonds, options, and derivatives. Advanced security modeling functionality supports the breadth of OTC, non-listed instruments, alternative assets, and private securities.
Security masters are designed to seamlessly integrate with external data providers and market data feeds to automatically update security information. This automation accelerates data processing and eliminates the need for manual data entry or periodic updates, reducing the risk of errors and ensuring data is current. A master data solution transforms incongruent data to make it cross-functionally usable by centralizing and simplifying securities data management.
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Investment firms develop and utilize a variety of investment trading strategies. One such strategy is systematic trend following, which uses past performance and pattern analyses on myriads of historical security data. This long-term trend following has proven highly successful for one investment manager who reported returns of almost 125% for Q1 of this year.2 Systematic trading models use huge amounts of data and data science to modernize portfolio construction. These algorithms utilize security information including asset class, liquidity, related assets, currency, market sector, and other key attributes. As firms invest more in data science, it is important they recognize that the success of these strategies is reliant on the precision of their data, and to exercise caution to avoid eroding those returns with operational inefficiencies.
A security master brings together all of a firm’s security data, terms and conditions, classifications, sectors, security metadata, and creates relationships to link to other securities. A centralized view of securities facilitates data synchronization across departments and systems. Contemporary security master systems generally feature intuitive, user-friendly interfaces that empower users to locate the securities’ information they need, reducing the burden on IT or data team resources. Various integration methods can be used to ingest data and connect systems. APIs enable easy querying of historical data for a variety of research, for example, retrieving all US listed securities within industry sectors whose volatility has a standard deviation of 1-3, or swiftly querying all stock dividends over the last 10 years.
A security master data management system contains detailed and granular information about each security, including identifiers, ratings, classifications, terms and conditions, and payment schedules. Organizations that leverage high volumes of data frequently have security data sourced from multiple vendors. This increases the importance of being able to programmatically relate identifiers to their corresponding records, and master conflicting data appropriately. All instruments are created based on the correct asset data model representation that capture and present cohesive views of each security, with attributes and applicable metadata.
With the abundance of asset classes available and traded in today’s economy, many security masters are under pressure to support more complex, alternative, and private asset classes, which exponentially augments the number of attributes required. Modeling securities with their related data develops a firmwide view across the public and private markets spectrum and provides the front office with numerous metrics for analysis. To ensure capture of all critical data, purpose-built data models enable seamless ingestion and enrichment of securities and additional attributes such as credit ratings and market indices.
An investment strategy is only as good as the accuracy and reliability of your security master data. A disorganized security infrastructure with inconsistencies, duplicates, or errors renders even the best strategy and algorithm futile. In contrast, maintaining a single, authoritative source of security data can eliminate anomalies and ensure that all downstream systems and processes operate with clean and current data.
Sophisticated security masters incorporate data validation rules, data cleansing, and exception management workflows within the underlying framework to permit capture and storage of valid and accurate data only. This empowers the front office to confidently consume data for strategy and product development, accelerating time to market and maintaining competitiveness.
A security master streamlines the straight-through processing of trades and investment lifecycle data. Middle- and back-office functions can tremendously benefit from increased efficiencies by automating the trigger of security-related transactions, and in return programmatically update security data from lifecycle events. Integrating with trading, settlement, and reconciliation processes expedites transaction processing, allowing for faster and more optimized operations while mitigating risk of manual error. Embedded data quality checks drastically reduce the time to sift through disparate data to connect pieces manually. Standardized data formats, units, and definitions across different sources ensure consistency and comparability for the various teams that interact regularly with the data.
Firms can optimize operational efficiency by automating frequent and repetitive manual tasks. For instance, a security master solution can considerably improve corporate actions data workflows and capture all impacted data points, including ad-hoc events that result in security ticker changes, such as mergers and acquisitions. Automation of complex, frequently occurring tasks helps minimize human oversights and frees up resources to focus on more strategic initiatives. Hence, the front office can create growth, increase volumes, and promote creativity without straining the operations teams or requiring drastic increases in team sizes.
A strong data quality framework drives data governance and underpins the foundation of robust regulatory compliance and risk management. Advanced automation and embedded quality control in security master systems are designed to reduce the risks inherent in unorganized, manually processed, and incorrect data. An auto-generated audit trail on every change that is carried out on securities data maintains traceability and offers a transparent view into the transformation journey of any data point. Investment management firms are subject to strict regulatory requirements, and a security master can help firms strengthen their compliance processes. Reporting with data that has been thoroughly screened for quality ensures that information submitted to regulators is accurate, consistent, and valid. Security masters offer, or can integrate with, data quality through validation rules. These rules perform comprehensive checks on each security attribute data point as it flows into the system, guaranteeing that all data is captured correctly and in the required format. Any discrepancies are flagged, and an alert system notifies the appropriate teams to check the data and initiate a resolution workflow. Validation rules can be customized to a firm’s specific security data model.
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Integrating with a cloud-based data storage solution advances the capabilities of a security master and creates more possibilities for data science. Data warehouses, data lakes, and data lakehouses are increasingly being integrated with investment management ecosystems to ingest, store, and manage large quantities of data across the trade lifecycle. This allows firms to grow without having to replace or continue adding on additional storage capabilities to accommodate increased volumes of information.
Each solution offers distinct capabilities for a variety of use cases. The right solution for one firm may not be the best fit for another. For investment firms utilizing data for mainly business intelligence purposes, a data warehouse provides convenience and ease of use with structured data. On the other hand, data scientists who are consistently performing advanced calculations and analytics, will benefit from the flexibility, and access, to raw, unfiltered, and structured data. To cater to a wider range of users from operations analysts to data teams, a data lakehouse offers the best of both worlds, accommodating diverse skillsets and data manipulation use cases.
Data storage solutions coupled with security masters enable users to perform complex analyses and generate insightful reports, housing substantial amounts of security data. Long-term storage of securities data enables the front office to analyze trends, perform back-testing, and conduct in-depth analysis on investment strategies and market patterns.
A unified security master and cloud-based data storage enable data governance and lineage tracking, ensuring data integrity and enabling firms to understand the origin and transformations of their data. Designed to handle increasing volumes of data efficiently, data warehouses, lakes, and lakehouses allow firms to scale their data management capabilities as their business grows without compromising performance.
Contemporary security master systems offer several enhanced features such as bitemporality, advanced data analysis, retaining historical data, and data lineage.
Access to past security attribute values, such as pre- and post- corporate action event data, is invaluable for back-testing and developing new trading strategies and models. Bitemporal support across data facilitates point-in-time analyses by persisting a history of security attributes with effective and knowledge dates. Bitemporality and data lineage creates the possibility of regression analysis and tracking the impact of changing variables over time.
Modern-day, integrated security master and data storage solutions offer functionality for performing comprehensive analyses across datasets from a variety of data sources. Capabilities to run queries, locate specific information, or build calculations on top of existing data equip firms to extract even more value from their security master data. Data visualization, charting, and reporting functionality enhances analytics for tracking performance and measuring risk. Exploring data through seamless links and relationships to other datasets and attributes helps firms form a narrative in their analyses and reporting for investors and senior management. The front office can generate dashboards pertinent to their needs, such as visuals for past performance inference and trading strategy ideation.
Security masters integrate with various data sources bringing in data on trades, orders, and executions, all of which include security identifiers, classifications, and metadata. Market data feeds include real-time and historical market data and market statistics. This also comprises updates to backdated information, making the knowledge date vs. effective date timestamps critical for the integrity of data history. Financial data and accounting systems are united by the security traded or held. Over time, this incoming data accumulates substantially. A security master gathers historical data organically by processing data vendor data updates, which can be populated with past data changes at any point in time. Incorporating a sophisticated storage mechanism such as a data warehouse, lake, or lakehouse with a security master allows firms to accommodate growing volumes of data at scale.
Tracking data origins, transformations, and modifications ensure data integrity and accountability. Security masters are built to automatically preserve full audit trail history and lineage on all data points stored, enabling firms to implement robust data governance practices. Lineage supports compliance with regulatory reporting requirements by providing a transparent view of data modifications, enabling auditing processes, and demonstrating data integrity. A security master can provide a reliable and comprehensive foundation for investment management firms to manage risk.
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In a fast-paced, competitive, and highly regulated investment management industry, ensuring data accuracy, consistency, and governance is paramount. With data volumes on the rise, this is becoming increasingly difficult for investment firms. A modern, comprehensive security master solution can help firms efficiently manage securities data across their portfolios and operational functions. Incoming data from multiple data sources requires aggregation and normalization. The front, middle, and back office are at a tremendous advantage when they are accessing the same consolidated data in an interactive solution. Enriching securities data with additional attributes beyond basic identifiers and details, such as ratings, indices, financial ratios, or ESG metrics, can help drive advanced analytics and decision-making.
To summarize, a security master offers several benefits:
Additionally, a security master fused with a data storage solution marries two core components that define the essence of excellence in security master data management: the data framework and the data itself. This greatly enhances efficiency, agility, and scale for a modern data-driven organization. Huge amounts of historical security master data can be stored and referenced easily without impacting technology performance. Integrating with a data platform further enhances capabilities through:
A cohesive solution to manage your entire securities universe and data management workflow can ultimately lower the total cost of ownership by eliminating the need for multiple systems, particularly legacy systems and dated applications that are difficult to connect to, with more contemporary, cloud-based systems. Data centralization reduces duplication and errors, ensuring up-to-date information for stakeholders, and contributes towards achieving optimized investment outcomes.
Learn more about managing the complexities of your data in our ebook Build, Buy, Partner: A Framework for Optimizing Time to Value for Data Initiatives.
Our professionals explore a series of considerations to contemplate whether you’re embarking on a new initiative or re-evaluating your existing data framework.
Sources:
1 U.S. Equities Market Volume Summary, April 2024
2 Institutional Investor: This Fund Is Up 124 Percent This Year, April 2024
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