As the passive trend looms, investors are asking tough questions about alpha. That means long/short funds need to reassess every aspect of the investment and research process, embracing new technology and its unique ability to:
- Augment analyst teams by increasing access to more varied datasets
- Expand research across sectors, geographies, and strategies with actionable intelligence
The Why: Five Challenges of Conventional RMS
The term “research management system,” or RMS, typically has a very narrow focus. Current offerings typically manage note-taking and collaboration, tasks that represent only 20% of the actual work in the research lifecycle. The full lifecycle includes source definition, data acquisition, research, collaboration, and insight (see Fig. 1). Each stage requires unique skills supported by multiple tools. The end result is often a labyrinth of data and systems that are hard to manage.
Beyond this issue, however, conventional, software-driven approaches to research tend to create five serious challenges for funds.
Fig. 1: The full research lifecycle
1. Cataloguing, not content creation
Conventional RMS software primarily helps analysts organize research notes they have obtained from a variety of external sources. In other words, the software depends on users to generate data. It does not connect to other systems or services used to create the research. This often results in segregated information and intelligence loss due to the inability to “connect the dots” in an efficient and repeatable manner.
2. No easy customization
Research needs change quickly, so funds need a flexible way to integrate new data sources, quantitative models and analytics. But customizing conventional RMS software is either very expensive or not possible because the licensing terms do not allow modifications. This means funds must instead acquire new systems or services to accommodate new requirements.
3. Legacy technology
Almost all leading RMS products have a legacy technology stack that is difficult to upgrade. The components tend to be tightly coupled, so any significant change in functionality requires a large investment in time, money, or both. In general, legacy technology is designed for efficient storage and searching but lack the power and performance to handle complex or time-consuming calculations, such as price targets and cash flow analysis.
4. Inadequate compliance readiness
With MIFID 2 and increased overall investor vigilance, it is mandatory to retain the lifecycle of an investment decision from the original idea to the investment recommendation to the trade decision. Current RMS products lack robust audit trail functionality, forcing portfolio managers to rely on memory or add a separate system to meet this need.
5. No portfolio integration
RMS products cater to pre-trade activities, so there is very little focus on post-trade investment tracking. But integrating fund portfolio data is a must for a modern RMS. If it were possible to establish price targets and alerts within the RMS, as well as revise them as event unfold, it would unify the process and complete the investment research lifecycle.
The How: Building a Framework for Transformation
While these challenges offer compelling reasons to select a new RMS tool, research teams are often reluctant to do so. Familiar RMS tools are very comfortable, if inadequate, and research teams resist any change that might slow the research process. On a more practical level, replacing the RMS is often painful because the team must learn a new system that is built according to a generic model that is very difficult to customize for in-house research processes.
This is why IVP recommends a framework-based approach to the RMS rather than a product-based one. Using a framework allows funds to take a more personalized approach, one with the potential to fully integrate the research process with the investment lifecycle.
With a framework approach, funds follow a five-step process (see Fig. 2) that outlines current requirements and desired capabilities as well as a path to execution — instead of moving directly into comparing available tools.
Fig 2: Developing a research framework
The framework-based approach is very flexible and can accommodate broad-based requirements in a relatively short amount of time. We will provide more detail about this process in future blog posts.
For now, however, imagine a proprietary RMS built specifically for your fund, that you can use to market unique research processes and alpha generation capabilities to new investors. As your in-house tool, it enables you to use alternate datasets, build analytics on top of them, devise an overarching workflow to manage the end-to-end research process and more. In other words, a proprietary RMS not only delivers better results — it becomes a unique way to differentiate your fund from those that use very similar tools from the same two or three vendors.