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.
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