Why do startups increasingly choose to trade on private secondary markets (PSMs)? I examine the trade-offs associated with this decision and the characteristics of startups trading on PSMs. Using a recent regulatory change that expanded investor access to PSMs and drawing on novel startup share price data, I show that PSM-listed startups face higher fundraising uncertainty and typically experience declining share prices once listed. My results reveal a risk-return trade-off, in which startups accept increased financing risk for higher returns when fundraising succeeds and broad stock markets are supportive, benefiting from price discovery without foregoing private status.

Presentations: FMA Annual Meeting (Doctoral Student Consortium and Special PhD Paper Presentation) (2025), UZH Rising Scholar Conference in Finance (2025), Dauphine Finance PhD Workshop (2025), SFI Research Days (2025), EPFL-UNIL PhD Workshop (2024, 2025)

Standard machine learning algorithms can significantly improve the selection of successful startups. This improvement does not come at the expense of missing out on highly successful startups, and is robust to forward-looking and selection bias. I provide evidence that "AI-empowered" firms actively use these technologies, contributing to a decline in predictability and rationalizing the existence of these potential gains.

Presentations: EPFL-UNIL PhD Workshop (2024), SFI Research Days (2024), 9th Annual Cambridge Conference on Alternative Finance (2024), World Finance Conference (2024)

Institutional Venture Capital (IVC) Firms are responsible for most of the successful outcomes in the U.S. VC market, with a distinct (separable) and incremental (positive) effect toward success.

Presentations: EPFL-UNIL PhD Workshop (2023), Junior Academics Research Seminars (JARS) (2024)