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| Type | Bachelor's Thesis |
| Scope | Discipline-based scholarship |
| Title | Analyzing the Effects of Different Factors on Venture Capital Success |
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| Institution | University of Zurich |
| Faculty | Faculty of Economics, Business Administration and Information Technology |
| Number of Pages | 28 |
| Date | 2014 |
| Abstract Text | Executive Summary The motivation for this thesis stems from the dearth of transparency in venture capital performance and the often-observed and accompanying performance short- falls of investment in venture funds. The venture capital industry is rapidly growing amidst the current technology boom, with 185 new funds raising a total of 16.7 bil- lion US Dollars in 2013 alone (National Venture Capital Association 2014). Despite the large amounts of new venture capital being raised, the performance of venture capital investments have in many cases failed to beat public market performance benchmarks. In a recent report The Kaufmann Foundation determined that in their own portfolio of venture funds only a handful provided adequate returns (Mulcahy, Weeks and Bradley 2012). This is problematic, especially for certain institutional investors, as some of these (many endowments for example) face mandated capital allocation into venture funds. With this problem in mind, the research in this thesis focuses on the identi cation of a factor-based model to help explain US based venture rms' success. In contrast to other research, this thesis adopts a holistic approach by looking at a broad range of factors: both observable rm characteristics (number of funds, age of a rm, etc.), and partner characteristics (average age of partners, partner experience, etc.). In this thesis, a randomly selected sample with the addition of a handful of note- worthy venture rms were chosen as data points for analysis from 2003 to 2013. This data has been collected from a variety of publicly available sources such as Crunch- base and SEC Filings. From this data, a number of rm characteristic and partner characteristic factors were calculated including size, number of partners, average in- vestment deal size, age of rm, average partner age, average partner experience, gender ratio etc. These factors were chosen based on previous academic research and input obtained from professionals in the industry. They were then analyzed against the performance of each rm, using the estimated pooled internal rate of return as a measure. The analysis is broken down into three parts: a model consisting of solely rm characteristic factors; a model consisting of only partner characteristics; and a model consisting of a mix of the two. The results of the analyses are best modeled by a combination of both partner and rm characteristics. This is in line with the previous separate research done in the areas of partner and rm characteristics. The main factors - the number of partners and the average partner experience - both showed a large and signi cant impact on performance signaling in the best model identi ed. It is important to note that in the course of this research a number of challenges arose. The predominant problems related to lack of data access, which ultimately led to a smaller sample size than initially hoped. This limits the inferences and generalizability of the results. Additionally the actual fund performance data and the pooled internal rates of return were not accessible; hence estimates had to be used. This is not ideal and has a probable impact on the results. Nevertheless, this thesis identi es some interesting trends in venture capital performance. These results provide a great basis for future research. With an expanded sample size and more detailed performance data, a more re ned venture rm performance model could be identi ed. |
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