Econometric Analysis and Modeling

For the normal performing-sample defined above, we determine the factors influencing the outcome of the investment return via an Ordinary Least Square (OLS) regression analysis with the cash flow based IRR as the dependant variable. The regression model we estimate can be stated as

IRRi =α+β₁X₁i +β₂X₂i +...+βKXKi +Ui, (3) where:

IRRi: Internal Rate of Return of observation (investment) i, α: constant factor of the regression function,

βk: regression coefficient of explanatory variable k=1,…,K, Xki: value of the explanatory variable k in observation i, Ui: residual value in observation i,

i: i=1,…,I and I = total number of observations in the data sample.

In total, we consider a diverse collection of K explanatory variables on four different levels: (1) the investment manager, (2) the fund characteristics, (3) the portfolio company characteristics and (4) various market or macroeconomic variables. The variables are chosen with regard to prior academic literature. In addition to the variables presented in detail hereafter, which are the

variables kept in the final model, we have tested alternative variables. Overall, more than 50 potential influencing factors have been analysed.⁴ The resulting factors were selected according to their significance level, their contribution to the explanatory power of the model and multicollinearity restrictions. The final variables of our model variables are as defined in Table 1.

(1) Investment Manager: The investment manager is the PE or VC firm which manages the consecutive funds. The experience and reputation of the investment manager generally grows over time, because unsuccessful funds could cause the investment manager to not being able to raise the next fund. Maula and Seppä (2001) provide evidence that reputation strongly affects the IM’s ability to select, certify and add value to investments, and to utilize negotiation power in the new investment’s valuation. Therefore, we introduce the investment manager age in years since its foundation at the initial investment date of the fund into the company in our analyses. Contrary to this idea, Gompers and Lerner (1999) found that reputational concerns induce younger partnerships to work hard to achieve success. A further explanation for a possible negative influence is provided by Schmidt and Wahrenburg (2004). They argue that established fund managers older and closer to retirement, and therefore, put less weight on the effects of their actions on future business opportunities.

(2) Fund Characteristics: At the fund level, we test for the impact of the fund size on investment return. Several empirical studies have confirmed the importance of the fund size on success, for example Cumming (2003); Gottschalg et al. (2003); Diller and Kaserer (2004). Most studies argue that the performance decreases with increasing portfolio size due to less monitoring and value-added assistance.

(3) Portfolio Company Characteristics: At the portfolio company level, we test for 5 different characteristics: (3a) Our dataset includes companies from different nations. We control for the effects of geographic origin of the portfolio company by including a dummy variable,

indicating, whether the company is based in the United States. Studies reflecting on the relevance of the location for example in regards of legal regulations, macroeconomic conditions, or investment pattern include for example Bottazzi et al. (2005); Keuschnigg (2004); Cumming and MacIntosh (2002); Jeng and Wells (1998) and others. (3b) Further, we examine the portfolio company industry by introducing several sector-dummies in the analyses. Due to high information asymmetries we observe in Table 2, Panel B the highest IRR standard deviations for the high tech sectors: biotech, computer and telecom. This is also reflected by the high numbers of total losses and out- performers for high tech investments in Table 3. These sectors have the highest return dispersions even within the normal subset used for the regression analysis. (3c) Analogous to the sector, the degree of information asymmetries, as well as the return on investment, varies strongly depending on the stage of the company development. Therefore, we control for the influence of the stage of the company at the fund’s initial investment. The last two portfolio company parameters are more related to the investment behaviour than to company characteristics. (3d) The investment duration is the total time (measured in years) between the initial investment and the exit date. If the deal is not fully realized, then we take the valuation date to be the exit date and determine the investment duration. As described before, the investment duration may be linked to the growth of the investment. We assume that non-performing investments will derail some time after the initial investment and will not live as long as successful companies. (3e)The second variable related to the investment behaviour is the total number of rounds, which is represented by a proxy as the total number of cash injections received by the company. The stepwise allocation of capital to a company in several financing rounds, instead of financing the venture upfront, is described as staging. The importance of staging as a mechanism to control an investment and to affect its success has been confirmed uniformly by several authors, e.g. Gompers (1995), Neher (1999) and Krohmer et al. (2007).

(4) Market and Macroeconomic Variables: Furthermore, we control for a variety of market and macroeconomic variables. (4a) First, we want to take into account whether the

investment was exited during a period of abnormal market conditions, leading to exaggerated valuations and returns. Therefore, we create a dummy variable which is equal to 1 if the final exit or valuation took place during the so-called “internet bubble”, i.e. between September 1998 and March 2000 and equal to 0 otherwise. (4b) Analogous to the exit period, we examine whether the investment was started during periods of poor average vintage year performance on the overall private equity market. We further account in our model for bank lending conditions, business cycles and stock market fluctuations by considering the (4c) short-term and (4d) long-term interest rates at investment date, (4e) the average variation of the real U.S.GDP growth per annum over the entire investment period and (4f) the average variation of the corresponding NASDAQ Sector Index during the investment period.⁵ The short-term interest rate is defined to be The Federal Reserve Bank 1-month Treasury bill for U.S. investments and the BBA Libor rate for European investments⁶, and for the long-term interest rate we set it as the 10-year US–

Government Security. These macroeconomic variables have been recognized as relevant factors in several empirical studies, including for example Gottschalg et al. (2003), who can show empirically, that PE performance is positively related to public market performance and GDP and negatively related to interest rates. Moreover, to assess private equity market conditions, we test for two additional variables: (4g) the number of PE or VC backed IPOs at the date of exit or final valuation, this number is an equivalent for the liquidity conditions on the IPO exit market, and (4h) the average fund IRR per vintage year, one year before the investment date, classified for the US and EU venture capital submarkets. By including this IRR-market-benchmark in the analyses, we control for possible cyclical effects in the sample.

The last component to consider in the regression analysis formulation is the model residual.

We analyse, whether the regression model residuals, which assemble the effects not captured by the explanatory variables, are independent and identically distributed or correlated with each other

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in some way. To do so, we perform a bootstrap simulation and estimate the empirical correlations between the residuals. We first create for our entire sample different subgroups of consecutive (2-3) investment years. For each subgroup we create 5,000 independent bootstrap samples of the residual values, each consisting of 2 data values, and calculate the correlation coefficients. As a result, we get for each subgroup and for the average over all subgroups, a correlation value very close to 0. Hence, our model does not face autocorrelation problems, and therefore, the correlations need not be considered further in our simulation analyses. Additionally, the regression model has been analysed and meets the regression model restrictions like multicollinearity and heteroscedastisity.

The results of the OLS regression analysis are presented in Table 4. We can observe that almost all of the included explanatory variables are highly significant and in line with our expectations with regards to the direction of influence. However, two parameters show different signs than expected. First, the “exit in bubble”-dummy, which controls for abnormal market conditions with exaggerated valuations and returns, shows a negative relation with performance.

As most of the deals exited in this period fall into the out-performer subsample with extraordinarily high returns, it might be possible, that the remaining deals which are considered in the ‘normal’ regression might be the lemons of this market period, leading to a negative relation with returns. Furthermore, the number of cash injections is negatively related with investment performance. Krohmer et al. (2007) argue, that firms in distress receive more frequent rounds of cash injections as investors “gamble for resurrection,” perhaps attempting various turnaround efforts in the hope of minimizing losses.

In total, the F-statistic shows a high significance for the overall model at the 1 % level. The R² and the adjusted R² values are greater than 0.2 and indicate, that our regression model explains more than 20% of the variation of the investment performance.