Ep. 6: Using AI to Build One of the Best Datasets for Investing
Jul 19, 2023
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In this episode, Thomas Li discusses the crucial role of precise historical financial data in fundamental investing. Daloopa solves the problem of data extraction and management in Excel. The unique approach of structuring financial data is explained, along with the use of machine learning and AI models. The episode also highlights the importance of risk statement updates in 8K filings and the value of unique data and improving user experiences with AI. Plus, the hosts share their experience of coming up with the name 'de lupa' for their company.
Daloopa provides a comprehensive, single-source database for historical fundamentals, solving the time-consuming task of financial data extraction and management in Excel.
Daloopa prioritizes accuracy and completeness by structuring financial data exactly as reported, utilizing machine learning and AI models to navigate complexities and serve as a 'perfect messenger' of publicly available data.
Deep dives
Building High-Quality Analytics and AI in Finance
Thomas Lee, co-founder of Delupa, discusses the journey of building a world-class software that models fundamental investor data. The idea for Delupa stemmed from the need to centralize and improve the quality of fundamental data used by asset managers. By creating a single golden set database for historical fundamentals, Delupa aims to provide high-quality data to customers, enabling them to focus on analysis and generating ideas. The company's philosophy is to be a perfect messenger of publicly available data, prioritizing accuracy and completeness. While Excel is often seen as old technology, it remains the preferred delivery mechanism for financial modeling and analysis. Delupa is also exploring the use of AI to enhance data delivery and usability, with the goal of providing customers with a seamless and efficient experience.
Challenges in Building a Database of High-Quality Data
Building a database of high-quality data is a complex and time-consuming task. Delupa faced numerous challenges, such as solving edge cases, handling different reporting styles across global companies, and ensuring accurate translation of financial language. The company took a meticulous approach, focusing on delivering accurate and precise data to customers. Delupa's philosophy is to provide a perfect set of data, allowing users to compare and analyze companies without the need for normalization or complex taxonomies. The company continues to refine its processes and leverage AI to streamline data collection and ensure constant improvement in data quality.
The Importance of Accurate and Reliable Data in Finance
Delupa recognizes the crucial role of accurate and reliable data in the finance industry. With the market valuing data more than ever, there is a strong emphasis on providing high-quality data to customers. Delupa's objective is to deliver data that is complete, accurate, precise, and updated quickly. The company values the human element in data validation and leverages AI to automate data collection and triage tasks. By offering an ultra-high-quality database, Delupa aims to empower analysts, portfolio managers, and other financial professionals to focus on generating returns and insights, rather than spending time on data entry and scrubbing.
Future Plans: Using AI to Enhance Data Delivery
Delupa plans to leverage AI further to enhance data delivery and improve user experiences. While currently utilizing Microsoft Excel as a delivery mechanism for data, the company aims to develop an independent delivery platform that offers a seamless, browser-based interface. The goal is to enable customers to access and analyze data in a more efficient and user-friendly manner, customized to their specific needs. Delupa is exploring AI-based solutions for chart generation and other functionalities to offer intuitive and valuable features to its customers. The focus remains on continuous improvement and delivering maximum value through cutting-edge technology.
In the 6th episode of the Hedgineer podcast, Thomas Li, co-founder of Daloopa, delves into the crucial role of precise historical financial data in fundamental investing. The discussion highlights a significant problem faced by financial analysts: the painstaking, time-consuming task of data extraction and management of financial models in Excel. As Michael and Thomas have both experienced first hand this is no easy task, but Daloopa aims to address this issue (and pretty much as solved it) by providing a comprehensive, single-source database for historical fundamentals, ensuring high quality and precise data for fundamental equity investors. This innovation is a game-changer for hedge funds and asset managers, offering them a more efficient and reliable resource for fundamental investing.
The conversation moves towards the unique approach Thomas and team take to structuring financial data. The focus is placed on presenting data exactly as the company reports it, ensuring accuracy over taxonomy in their data model. Thomas also discusses how with the help of machine learning and AI models, the company navigates the complexities of KPIs and adjustments, acting as a 'perfect messenger' of publicly available data.
This is a great episode for financial analyst, hedge fund engineers, entrepreneurship, and anyone interested in learning about a core problems for a fundamental investor.
