For a medium-sized investment management firm based in California, quality estimates data was a critical component of day-to-day operations. Whether used to predict earnings surprises, accurately assess analyst ratings, or define an investment strategy, the higher the quality of the content,the more accurate the resulting output. However, despite the firm’s decades of experience and successful management of over $4 billion in assets, it struggled to find the breadth and consistency of data required to perform at its peak, sacrificing time and efficiency to prepare content for analysis.
With its previous data provider, the firm’s estimate database was frequently incomplete or inaccurate.The quantitative research team tasked with identifying patterns for idea generation and algorithmic trading was left spending countless hours loading and managing content, rather than backtesting data sets.The firm also needed to supplement historical and industry-specific data to account for gaps in its provider’s collection methodology, which relied on broker contribution portals with sources and submissions often difficult to verify.
In order to continuously evolve its investment strategies and stay at the forefront of the industry, the firm agreed it needed a data provider that could give its researchers and quant team more accurate and verifiable sources of estimates data. Integration and data connectivity enhancements were also of utmost importance to the firm.
As the firm began to define its requirements for replacing data providers, two key themes emerged. First, and most importantly,the firm required accurate and diversified data sets, supported by a strong collection methodology. Second, consistent symbology was a critical component of the firm’s wish list. With a data ecosystem dependent on a complex matrix of relationships, a lack of reliable identifiers had been an obstacle for adding new content sets in the past.
Like many others in the financial management industry, the firm’s overall goals boiled down to a desire to do more with less, make informed decisions, and augment previously established workflows. Furthermore, as data requirements differ significantly from business to business, it was also critical to find a data provider willing to collaborate and understand the firm’s needs.
Anxious to empower its research team with more efficient data and tools, the firm began to evaluate data providers. Following the evaluation of several vendors, a conversation with FactSet led to a demonstration of the industry-specific estimate data. Impressed with the depth and quality of available content, the firm agreed to trial FactSet.
During this phase, FactSet’s dedicated support and implementation teams worked with researchers and technologists to understand the demands of the firm’s dataflow,the responsibilities of its employees, technical requirements, and workflow dependencies. In concert with the dedicated support team, FactSet’s issue tracker tool gave the firm an additional avenue to ask questions during the rollout, providing near instantaneous responses from the deployment team.
The firm was already set up to receive data via HTTPS/flat files, one of FactSet’s delivery options. Leveraging FactSet’s data feed loader, integrating the firm’s desired data sets took less than 24 hours, dramatically expanding the pricing, consensus, and broker-level estimates available to it overnight.
Consistent symbology was another must-have for the firm, and with FactSet, it was able to organize and connect disparate sources, identifiers, and data points to a single master entity.This newly enabled data connectivity allowed the firm to easily link company actuals to forward looking estimates within its quantitative and research models. The firm was now able to continuously update its investment philosophies and stay on the cutting edge of research. FactSet was also able to meet the data management needs of the firm, providing a scalable solution for future data acquisitions.
Having partnered with FactSet to address a variety of data accuracy, coverage, and organizational needs during the trial, the investment manager gained the ability to streamline its day-to-day operations,and the confidence to officially adopt FactSet as its data provider.
Facilitated by the FactSet support team, the investment firm found the transition to FactSet to be nearly seamless. The investment firm began to use FactSet as its chief data feed provider, also ordering FactSet workstation deployments to aid in the analysis of the research teams’ newly revamped data ecosystem.
With new data and symbology solutions in place, the firm was able to address the accuracy and reconciliation issues that had formerly plagued its research group. With the previous data provider, researchers relied on statistical packages to program if/then statements that linked symbology and company data. While this manual process gave the team the specific data outputs it required, it also added considerable time to the researchers’ day-to-day. By collaborating with FactSet to identify synergies in its workflow, data integration and connectivity were automated within the delivery process, providing the necessary data without the inconvenient checks and balances of the previous workflow.
Thanks to FactSet, researchers at the firm could now concentrate on the quantitative data required to define strategies and identify patterns. Recovering the valuable time spent on a burdensome reconciliation process made the research team more efficient, while a more stable data ecosystem and unique content helped to gain intelligent insights around companies, markets, and portfolios.