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    Modernizing Research Management to Support Investment Decision-Making

    Asset managers are changing gears in the intensifying battle for alpha.

    Research is the lifeblood of an asset manager’s decision-making process. Data and the intelligence it yields can guide managers through every step of the investment lifecycle, whether that be during pre-trade analysis (idea generation), real-time execution or post-trade evaluation (analytics and reporting). Data-driven insights are the catalyst for improving and understanding performance. Yet today, the process to organize and manage that data hasn’t improved significantly, even as the volume and complexity have increased.

    Collecting and updating data can prove to be extremely time consuming and daunting for research analysts that are stretched to the limit. Changes to the competitive and regulatory environments have further impacted the research management process, prompting a need for increased transparency and integration into the investment process.  Luckily, with the advent of state-of-the-art cloud-based platforms, many of these challenges have solutions – now more than ever before, managers can benefit from modernizing their research management.  

    Today’s Research Management – Shifting Out of Manual

    Today, research management is far from optimized to its full potential. Many managers rely on manual workflows to organize their research process, whether that be inputting and updating data into OneNote, Evernote, Outlook, Word, or Excel spreadsheets, or trying to make sense of the plethora of data from multiple applications to determine the next best idea. Not only are these traditional methods time-intensive, but they can be highly error prone, which should not be overlooked in the intensifying battle for alpha.

    In addition, the research management function is often siloed within an organization or operates with a single focus, concentrating only on qualitative insights. It can even be siloed within a single investment team, making it difficult to leverage the institutional knowledge base to help make the best decisions. Information is often not available in real-time, so may not be up to date when needed. It is also generally static in nature (today’s internal or third-party solutions are not a great deal different than any storage environment).

    Finally, there is little in terms of analytics to better understand the value of certain types of data or research. In addition, data on workflow metrics, such as time spent on certain research workflow stages, is not easily configured.

    Four Key Benefits to a Modern Research Management Program

    Clearly, there is room to improve in modernizing the research management process. While data automation is still in its nascent stages, it is evolving at a rapid pace and has the ability to profoundly shift the way managers organize their information. When making the move to a more automated research management system, managers should consider the key benefits of making the switch:

    Improved Decision-making

    As much as technology has influenced investment decision-making to date, firms often still operate in a largely siloed and analog world. Analysts gather a mix of quantitative and qualitative data that they store in multiple places, and they then must put the pieces together to tell a story. This is prompting tighter requirements for efficiency and precision, as well as integration into the investment process. As investment teams seek to differentiate themselves, leveraging leading technology to improve decision-making is critical.  Digitizing the process enables the capture of decisions that were not made or taken, adding further intelligence to the manager’s research process and the addition of the research “exhaust”.

    Real-Time Analytics

    In a world that seldom slows down, real-time internal, market and third-party data have become powerful tools for competing in the battle for alpha. Today’s investment teams require every idea to be supported by concrete evidence, largely analytics, which can only be done if they have a comprehensive data set. A modern research management program that leverages cloud-native software can provide the most “real-time” information available. This means managers can access the research they need, regardless of where they are.  Transparency improves across teams, and team members can access and annotate the same research notes and view historical changes to better analyze results.

    Increased Efficiency

    Managers need collaboration, transparency and accountability, along with growing institutional knowledge, from their analyst teams. Housing the intelligence in a centralized, automated platform is the most efficient and effective way to accommodate these requirements. Collaboration is made easier with a team-based approach to research management. By housing all internal, market and third-party data in a single platform, teams can stay abreast of market shifts in real-time.

    Effective Risk Management and Compliance

    Investment teams are increasingly subjected to heightened regulations that require firms to adhere to a level of reporting and support documentation that addresses compliance needs. As a result, portfolio managers are demanding more from their risk systems in today’s financial landscape, but many of the current solutions are inflexible and siloed. Modernized research management systems can provide audit trails along with advanced reporting that is configurable and able to handle multiple portfolios, asset classes and indices.

    Using Data to Get Ahead in the Battle for Alpha

    Data proliferation has created a new world of opportunities for investment managers but has made the research management process more complicated. As the playing field continues to shift, it’s important for managers to harness the power of modern and intelligent automated solutions to optimize their research management. Armed with the right data, investment teams can tap into actionable insights in the ongoing battle for alpha.

    Paul Fahey portrait

    Paul Fahey

    Head of Investment Data Science, Asset Servicing, Americas
    Greg McCall portrait

    Greg McCall

    Co-Founder & President at Equity Data Science

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