Publication Type

Master Thesis

Publication Date



In this paper, I tested the effects of three proxies for venture capitalist(VC) reputation on its invested company's long term industry-djusted operating performances (ROA , ROE), market-to-book ratio and survival time (time to delisting) in the aftermarket. VC's market share and VC's IPO share have strong and positive association with the post-IPO long-term performance metrics, and the effects are statistically significant even after accounting for self-selection bias. For long term survivorship of start-up companies, I applied hazard analysis to the IPO company's time to delisting with accelerated failure time (AFT) model as the baseline hazard function, and found that start up companies with backing from higher VC's market share and VC's IPO share VC firms tend to have lower hazard rate of de-listing. The expected time to delisting is also found to be much shorter in the pre-technology bubble period (1985-1996) compared to during and posttechnology bubble period (1997-2007) for higher than median value reputable VCs. As the findings are robust even after controlling for business expansion and contraction cycles, this lend credence to the idea that during the technology bubble period, over 4 optimism in VCs and too much uncommitted capital chasing after too few quality deals have resulted in reputable VC investing in mediocre quality companies. By cross-testing the effects of different quartiles of VC reputation proxy rankings on the long-run survivorship of the companies, VC market share is found to be the most consistent and effective amongst the proposed VC reputation proxies in explaining its effect on the IPO companies' long-run survival.


financial performance, initial public offering, long-run returns, rate of return, stock exchange, venture capitalist

Degree Awarded

MSc in Finance


Corporate Finance | Portfolio and Security Analysis


Chua, Choong Tze

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.