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Learn How Thorogood is Helping Oxford University Endowment Management Apply GenAI to Their Investment Analysis

Type:

  • Webcast

Topic(s):

  • Generative AI

Oxford University Endowment Management, or OUem, is a successful long-term asset management organization that manages in excess of £6 billion of endowment funds on behalf of Oxford University colleges and other charities. OUem has been working with Thorogood since its inception to develop and evolve quantitative applications that are key to its investment approach.

In 2023, OUem and Thorogood acted on a shared interest to understand the potential of Generative AI (GenAI). As a diversified investor that takes a very long-term view, OUem has technology-related investments within its portfolio. As an organization, it is always alert to opportunities to work ever more efficiently and effectively. OUem embarked on a journey with Thorogood to leverage Generative AI or GenAI, beginning with a focus on the activities of its investment management and operations teams. This GenAI investment analysis solution enables the OUem team to gain insights from proprietary and private qualitative and quantitative data relating to their investments in intuitive and never-before possible ways, and during this webinar, we will highlight what this solution sought to achieve and demonstrate it in action.

Learn How Thorogood is Helping Oxford University Endowment Management Apply GenAI to Their Investment Analysis (37 mins)

OUem allocates the funds under its control across a range of investments including public equity funds, private equity funds, and hybrid equity funds. As a result, the organization has access to thousands of investor reports from a rich variety of very specialized and expert sources that collectively offer perspectives not available to other organizations. Any given report may be dozens of pages long, featuring a combination of quantitative data and qualitative information about a fund, its holdings, its outlook, and its performance. Thorogood worked with OUem to deliver a new GenAI-driven solution that leverages the latest and historical quarterly investor reports from those many funds. This web app-based solution allows analysts and other interested parties within OUem not just to search through the reports, but also to ask questions of those private reports, with the possibility to add date or fund manager filters, and receive comprehensive written answers based upon the myriad of qualitative and quantitative perspectives in the report collection, substantiated by citations that can direct users to the specific report and passage from which any answer is sourced. This Thorogood-delivered solution is enabling the OUem team with a proprietary Large Language Model (LLM) to glean insights from data mostly previously inaccessible sheerly due to the volume of the reports and their contents, and ensure they are fully informed about their investments and able to answer any questions that may arise immediately and without having to contact funds directly.

In this 30-minute webinar Thorogood Data and AI consultants introduce OUem, explain their interest in unlocking more value from their quarterly investor reports, and ultimately demo a GenAI solution that is akin to the investment analysis tool that we have delivered (featuring publicly available data on Alphabet, Amazon, Meta and Microsoft and not proprietary data for confidentiality reasons), and explore how this new investment analysis tool is helping them understand potentially powerful possibilities to build upon their highly successful track record.

What we cover:

  • Introducing the business case of GenAI for investment analysis
  • Demonstrating a web-app based, LLM-driven investment analysis solution
  • Exploring the benefits of this intuitive and scalable solution

Is it for you:

  • Are you interested to understand practical applications of GenAI technology?
  • Are you considering how to enhance your investment management processes?
  • Are you seeking to unlock insights from both qualitative and quantitative data, at scale?