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Venture Capital

Fund Certification, Performance Prediction and Learnings from the Past

Series:

Armin Höll-Steiner

This book contains three studies. The first study investigates the relationship between private equity investors and fund managers and how intermediaries can mitigate their agency problems. The incentive structure of three intermediary types and their behavior in signaling fund qualities to investors are studied theoretically. A recommendation which intermediary to consult is given. The second study presents a new statistical method to predict the performance distribution of venture capital direct investments. The accuracy of this method is investigated and compared to existing approaches. The third study is about the European venture capital market’s historic development before and after the internet bubble and reasons for the bad development especially after the bubble.

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List of Tables XVII

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List of Tables 3.1 Summary Table of All Variables Used in the Analysis and Prediction of Venture Capital Performance . . . . . . . . . . 110 3.2 Structure of the Dataset . . . . . . . . . . . . . . . . . . . . 114 3.3 Distribution Patterns of Return Measures IRR, log(IRR), MIRR and log(MIRR) . . . . . . . . . . . . . . . . . . . . . 118 3.4 Parameters of the Default Rate of Individual Investments (Credit Portfolio View) . . . . . . . . . . . . . . . . . . . . . 122 3.5 Parameters of the Default Rate of Investments in a Quarter (Credit Risk Evaluation Model) . . . . . . . . . . . . . . . . 128 3.6 Parameters of Investment Performance of Non-Write-Off In- vestments Measured in IRR . . . . . . . . . . . . . . . . . . 131 3.7 Parameters of Investment Performance of Non-Write-Off In- vestments Measured in MIRR . . . . . . . . . . . . . . . . . 134 3.8 Comparison of results of my prediction approaches (CPV based and CREM based) with results based on Buchner at al. (2009) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 3.9 Parameters of Investment Duration of Non-Write-Off Invest- ments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 4.1 Focus of funds in full fund sample along stages and sectors . 165 4.2 Comparison of committed capital volumes in full fund sample with EVCA data and VentureXpert data . . . . . . . . . . . 167 4.3 Comparison of stage focus of funds in full fund sample and in VentureXpert . . . . . . . . . . . . . . . . . . . . . . . . . 168 4.4 Logistic regression to detect potential biases in full fund sample169 XVIII List of Tables 4.5 Composition of CEPRES sample of VC investments along realization status, stage and industry . . . . . . . . . . . . . 171 4.6 Comparison of fund performance data from VentureXpert, BVCA and my full fund sample . . . . . . . . . . . . . . . . 179 4.7 Absolute performance of fully and partially realized Euro- pean and US Venture Capital investments into portfolio com- panies measured in Gross IRR since inception and Total Value to Paid-In (TVPI) . . . . . . . . . . . . . . . . . . . . . . . 182 4.8...

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