Qianyue Dong
Pronouns: she, her, hers
Research Mentor(s): Yuan Shi, PhD student
Research Mentor School/College/Department: Finance, Ross School of Business
Presentation Date: Thursday, April 22, 2021
Session: Session 1 (10am-10:50am)
Breakout Room: Room 3
Presenter: 1
Abstract
The world is encountering a remarkable transformation to the digital economy. Uber, Alibaba, Airbnb…More and more large companies are having no real capital. What is the truly valuable things today for a company? It is data. Data affects a company’s valuation, from the simple question of revenue earned to different metrics that show a company’s ability. Valuation is, however, a tricky work to do today: most data are not transparent to the public, and factors are complex behind. The project aims to determine the exact factors behind company valuation, and how stock prices react to data changes. The project discovers around 230 randomly chosen companies’ open data ( and 46 for each student) from their historic 10k and 10q filings In each company, information is divided into two parts: business models and real data. Business models show a company’s structure, including their products, customers and type. Data includes their revenue breakdowns, geographical revenue breakdowns and different non-financial metrics. All of the information are categorized by industry and MAU (monthly active user). In the categorizing process, analyzation is also made, including differences throughout the whole industries, definition variation between MAU, and summed up of certain categories of metrics. Regression will be performed to make the analysis, and discovered the real factors behind valuation. The project is on the way to the final result. However, no specific conclusion has been made yet, since only part one of the project has been finished in the last semester. After the regression and categorization are finally performed, a clear conclusion is expected to be made before the semester ends. The result will make people getting a clearer picture of how companies are valued and what kind of data is most valuable. In conclusion, the project is still in progress, with most raw data already collected, and further analysis ready to performed. This finding will be rather new in this area that could filled the gap of “tricky work” previously stated. One other hope is that this projects could make the market more transparent to the public.
Authors: Yuan Shi, Qianyue Dong
Research Method: Data Collection and Analysis
