Case Study: Deeper insights and faster decisions with AI-powered historical data at US-based Mutual Fund

See how one mutual fund saved two days per analyst during earnings and 50% when building new models with Daloopa.

Company Background

This case study covers the mutual fund business of a US-based global investment management firm managing over $1.5 trillion in assets. Founded nearly a century ago, the firm provides a broad range of mutual funds, sub-advisory services, and separate account management for individual and institutional investors. Renowned for its rigorous research process and long-term investment strategies, this mutual fund aims to deliver consistent and sustainable returns for its clients.

Results Overview

By implementing Daloopa, the mutual fund achieved significant improvements in various areas:

  • Cut model building in half: Comprehensive data sheets cut each analyst’s model building time by 50%.
  • Saved days of work during earnings: Automated data updates saved each analyst 1-2 days during earnings season.
  • Increased In-Depth Analysis: Analyst spent more time delving deeper into coverage and uncovering new ideas, as Daloopa data allowed for faster first looks.
  • Improved Accuracy and Reduced Errors: The use of Daloopa's data infrastructure significantly minimized errors in financial models, enhancing the overall accuracy and reliability of the analysts' work. This increased confidence in the data allowed for more precise investment decisions.

Below, we will walk you through how one mutual fund decided to overcome its workflow challenges by migrating to Daloopa and achieve these results.

 

Challenge: Leveraging Deep Data for Informed Decision-Making

As a mutual fund manager, the firm's investment approach involves in-depth research, meticulous analysis, and collaborative decision-making. The team of more than 90 analysts works together to delve deeply into company and industry data to inform their ideas and make investment decisions. This process includes:

  • Conducting comprehensive analyses of company financials.
  • Understanding industry trends and competitive dynamics.
  • Engaging in extensive discussions with management teams and industry experts.
  • Collaborating closely with internal teams to refine investment theses.

The mutual fund faced challenges in balancing the maintenance of research on existing investment ideas while generating new ideas and managing the responsibilities of staying knowledgeable about their investment universe. Given the volume and complexity of data, the team sought ways to efficiently aggregate and analyze information to make timely and informed investment decisions.

 

Solution: Implementing Daloopa’s AI-Powered Data Infrastructure

To enhance their research capabilities, the mutual fund setout to automate data discovery, aggregation, organization, and integration into their existing models. They found Daloopa and chose to conduct a proof of concept (POC) to test how Daloopa’s AI-powered data sheets could be integrated into their analytical workflow. The goal was to see if this integration could allow analysts to access accurate, comprehensive, and up-to-date data seamlessly, helping them balance their model build and maintenance tasks more effectively.

The POC team consisted of 8 analysts, supported by the decision-making of DORs, data strategy, and compliance teams. They all were assessing the tool for different aspects – from driving efficiency, ease of use, and security.

The POC revealed a significant reduction in time spent on data discovery and model updates. Compared to other solutions, Daloopa stood apart due to its ability to provide the deepest set of data accurately and quickly after earnings reports. Other tools often lacked in one area or another and relied on manual processing, resulting in errors or incomplete data.

The success of the POC resulted in 6 of the 8 analysts choosing to onboard to Daloopa.

With leadership approval, analysts adopted Daloopa and began implementing their datasets into their models. This integration allowed analysts to access accurate, comprehensive, and up-to-date data seamlessly.

Implementation Highlights:

  • Complete onboarding of 90 analysts’ models within 12 months: All 90 analysts were onboarded over the course of 12months. Onboarding included all Daloopa data fully added to all existing models, as well as any new models that needed to be built with Daloopa data sheets to expedite the process.
  • Onboarding manager for successful implementation: Daloopa’s operations team worked seamlessly with the 6analysts to ensure a smooth implementation of the Daloopa Excel Add-in. They collaborated with the internal IT team and ensured the integration of Daloopa data into their models with the Excel Add-in was both successful and speedy.
  • Training from the Daloopa Customer Success team: Daloopa conducted initial trainings, empowering the mutual fund team to use the product effectively and efficiently.

 

The Results: Deeper Insights and Faster Decision-Making

The adoption of Daloopa’s data infrastructure significantly enhanced the mutual fund’s research process, with a 50% decrease in time to build models per analyst and an additional 1 to 2 days of work time reclaimed per analyst during earnings season. This immense time savings allowed for deeper dives into company and industry data, leading to more informed and timely investment decisions. Analysts reported roughly 10-15+ hours saved per quarter per analyst with quarterly updates, as Daloopa removed 50% of time needed to build out historical data for new models on average, and in some cases up to 75% of time. Overall, it is estimated that Daloopa can save each analyst 70-100 hours per year (17-25 hours per quarter) on model maintenance alone. Additionally, Daloopa helps analysts gain back 2days during the busiest times each quarter to focus on areas that are a "much better use of time."

Daloopa also enabled deeper analysis and efficient modelbuilding. Team members gained more time to delve deeper into coverage and uncover new ideas, as Daloopa's datasets allowed for faster first looks and more efficient new model building. One analyst highlighted that with some tools, only ~10% of functionality is used, whereas 80-90% of Daloopa's functionality is unique and highly valuable. Another analyst noted that Daloopa's auditability sets the product apart, exemplified by uncovering numerous errors in the datasets while integrating Daloopa data into the analyst’s data sheets with the Excel Add-in. In the POC, nearly every user felt that Daloopa's data is deeper than any other provider. Moreover, Daloopa's use of hard-coded data cut down on lag times and crashes in Excel, ensuring smoother operations.

 

Key Outcomes

Analysts were immediately able to realize these three areas of improvement in their workflows:

  • Further In-Depth Analysis: Analysts could perform more comprehensive and detailed analyses, gaining deeper insights into investment opportunities.
  • More Timely Data Updates: Automated data updates reduced the lag in information, ensuring that decisions were based on the most current data.
  • Improved Accuracy and Reduced Errors: The use of Daloopa's data infrastructure significantly minimized errors in financial models, enhancing the overall accuracy and reliability of the analysts' work. This increased confidence in the data allowed for more precise investment decisions.

 

Long-Term Benefits

The integration of Daloopa into the mutual fund’s research workflow has positioned the firm to maintain its competitive edge by consistently delivering well-researched and informed investment decisions. The firm’s commitment to leveraging advanced technology underscores its dedication to achieving superior client outcomes. In the months and years to come, the firm aims to further strengthen its partnership with Daloopa to continue enhancing its research capabilities.

 

Conclusion

By leveraging Daloopa’s AI-powered data infrastructure, this US-based mutual fund has enhanced its research capabilities, enabling analysts to delve deeper into data and make more informed investment decisions. This case study highlights the importance of comprehensive data access and efficient data management in achieving long-term investment success.

 

Interested in learning how Daloopa can enhance your modeling capabilities and improve your investment research process? Request a demo today to see our data infrastructure in action.