As the demand for digital transformation and off-platform solutions grows, flexible access to data and personalization through APIs will play an ever-increasing role in buy-side user strategies. Users are now seeking to receive exceptionally automated and completely customized solutions that strengthen their elaborate workflows and perfectly support their targets.
In addition to giving buy-side users essential, unique, and quick access to financial data, APIs provide adaptable and personalized benefits that enable users to select the services they want, without being charged for those they do not need.
Long-time users of APIs with analytics and reporting automation often inherit a layer of scripts, files, and configurations that have been built up over time. Such legacy setups embed operational risks that increase time to market and introduce breakpoints into the process including duplicate or incorrect data fields or calculations, timing conflicts, and excess or unused raw data. However, that is only the tip of the iceberg. In addition to unpacking that technical debt, users need portfolio data element calls that are more adaptable to ensure they are only sourcing what they need, when they need it.
Portfolio analysis APIs allow you to measure performance, risk, style, and characteristics for portfolios and asset classes within your system. You can customize and calculate portfolio return, investigate sources of performance, view the recent valuation and trading history of portfolio constituents, and compare portfolio characteristics and weights to any benchmark available to you, such as Barclays, FTSE, MSCI, S&P, and more. APIs allow you to upload, edit, and receive up-to-date holdings data so you’re able to evaluate current portfolio performance in real time.
There are several ways in which content-specific endpoints for individual datasets (e.g., ESG scores, social media, and non-standardized company disclosures) and adjustable formula-based endpoints (i.e., for cross-content requests, universe construction, and user-defined business logic to support buy-side clients’ overall market data research and decision processes) enable buy-side users to work more efficiently.
Report-building APIs provide an environment for users to access the data they need outside of external data platforms without the burden of sourcing, packaging, and formatting the data for consumption. Buy-side asset managers can save on development costs through efficient data sourcing and preparation, giving them more time to optimize their data analysis and visualization. Here are a couple of ways that such APIs can deliver value to asset management:
APIs that enable users to access data they need outside of a specific platform are at an advantage. Through more efficient data sourcing and preparation, buy-side asset managers can save on development costs and free up their time to optimize data analysis and visualization. A couple of ways that combined report APIs can add value to asset management are by providing:
Having the ability to load proprietary content and seamlessly integrate it with the wide breadth of market content on a single system is no longer nice to have—it’s crucial. Some research management solutions provide a designated place for buy-side firms to manage their proprietary research and communicate with different members of the investment team such as analysts, portfolio managers, and traders.
Addressing both business continuity and redundancy needs, APIs provide a channel for clients to extract and integrate internal research notes, meetings, and other findings into additional tools, client portals, or proprietary interfaces in real time. Moreover, they can help asset owners investing in funds, private securities, and other non-public entities to create and maintain custom internal research symbols alongside relevant metadata.
As technology continues to transform the financial services landscape at an exponential pace, buy-side firms are looking for more access and transparency while looking to reduce costs and inefficiencies and APIs are one way to achieve this. With the massive volume of data that is continuously being collected, harnessing the power of all that data would be an impossible task without artificial intelligence (AI) that’s easy to access.
Financial data providers are leveraging artificial intelligence and machine learning technologies to transform the way they discover, evaluate, and act on information. Named entity recognition (NER) uses natural language processing (NLP) to create structure from unstructured textual documents by finding and extracting entities within a document. NER services trained on financial documents can extract companies, people, locations, health conditions, drug names, numbers, monetary values, and dates from text, and then subsequently map the entities back to industry-standard identifiers, allowing users to connect the tagged entities to other relevant content sets.
Buy-side firms are using NER APIs to augment research capabilities and tag internal research notes. This connects the companies and individual people mentioned in the notes to other datasets, ultimately streamlining the process of connecting ideas and transforming them into meaningful content. For buy-side wealth users, NER APIs can help identify important pieces of news that surface for high-net-worth individuals (HNWIs) and assist wealth managers with relationship extraction.
Customized and automated solutions such as FactSet’s Developer Portal, which provides software development kits (SDKs) and recipes for mitigating a range of user issues, are the future of the buy-side workflow. As these solutions continue to evolve and provide new user benefits, increasingly firms are becoming committed to innovative technological trends surrounding APIs, artificial intelligence, cloud computing, and more.
Disclaimer: The information contained in this article is not investment advice. FactSet does not endorse or recommend any investments and assumes no liability for any consequence relating directly or indirectly to any action or inaction taken based on the information contained in this article.