Company performance can be measured in various ways. Differences in both product and market strategies of firms make it difficult to evaluate all companies using the same performance measures when looking at a relatively short timeframe. The PCs included in our study are mainly ventures raising capital as part of a growth strategy. Whilst some young firms grow organically, others rely heavily on up-front investments in research, marketing or in establishing a market position providing the necessary scale for profitable operations. Given these differences among firms, we argue that there is no perfect single measure for portfolio company performance in the short run. We have chosen five different measures of performance which are both relevant in the assessment of PC performance and has a distribution that makes them usable in regression analyses.
Meaningful ordinary least squares multiple regression analysis depends on four assumptions regarding the variables included in the regression model. First, the error term should have an expected value of zero independent of the value of the explanatory variable. Second, the observations should be i.i.d., i.e. identically and independently distributed. Third, big outliers should be rare, and not resulting from errors in the collection of the data. The fourth and final assumption is that there is no perfect multicollinearity. The second assumption is dealt with in the chapter on data and biases. The first is dealt with by the use of control variables. We will in the following paragraphs discuss the choice of performance measures considering the third and fourth assumption.
Outliers are observations with values that to a large degree differs from what should be expected given the distribution in the sample and the nature of the variable in question. The data sample used in our analyses do have observations of revenues, costs and profits that are extreme compared to the averages in the sample. However, they should not be treated as outliers without taking the nature of our sample into concern. What should be deemed normal when studying PC performance? A particular feature of PCs is that they are extremely risky investments, where the VC funds only expect about two out of ten investments to be profitable.
In addition to such a poor success rate in general, there is the hope of investing in the next “big thing” such as Spotify or Snapchat, of which the chances are even smaller. So, which observations of performance should be deemed abnormal? We find it plausible that none of the extreme observations are outliers that should be excluded from the data sample. We have used public accounting data from proff.no and regnskapsdata.no to verify that the extreme values do not result from errors.
Even though the extreme values are not outliers that can be removed, they do have great influence on the regression analysis. In addition to heavily influencing the results of the regression analysis, their inclusion results in a violation of the OLS assumption 1. As the outliers are not evenly distributed across the other variables, being so few, their presence results in skewness, with the expectations of the error term not being zero across all values of the included variables. To overcome this violation, we make use of two different approaches.
First, we divide the performance into two groups, resulting in binary dependent variables and the use of logistic models. Second, we use logarithmic transformations when studying continuous variables. The main weakness of this approach of logarithmic transformations is that they discard observations being equal to zero. We overcome this problem by adding the value one to all observations of performance. We make use of growth rates defined as the logarithm of for example revenues in the final year over revenues in the base year. This measure of growth rates, depending on logarithmic transformations as described in the previous paragraph, do not have a problem concerning extreme values.
The fourth OLS assumption is that there is no perfect multicollinearity between the variables included in the model. By the use of growth rates rather than including lagged observations we overcome the multicollinearity problem. One of the alternatives to this approach would be to compare levels in the final year and control for the level of the variable in the investment year. This would, however most certainly result in a problem regarding multicollinearity.
Other approaches to the use of panel data are fixed effects regressions and differences in
differences estimation. We make use of a data sample containing few observations, and the additional requirements related to the data sample regarding the use of differences in differences estimation reduces the data sample to 84 observations. We find this too small of a sample to estimate the effects of VC firm industry specialization.
We present analysis of three different event windows. The first event window is defined by the period from the time of investment (year one) to year five. This is used to test the effects of VC firm industry specialization, on PC performance, from VC entry until the fifth year after the investment. This period is chosen due to the nature of venture capitalists being invested in a portfolio company for four to six years before exiting the venture. There might, however, be significant differences in the effects on PC performance within the mentioned event window.
We have for this reason included two sub-periods of the five-year window.
The first of the two sub-periods of the event window ranges from the investment year (year one) until the third year, allowing us to see immediate effects of industry specialized VC´s on performance. It is plausible that some of the effects related to resource availability, signalling of quality to potential partners and access to the VC´s network materialize quite short time after the VC entry. Assuming that we are able to control for selection effects on performance, the presence of differing effects of specialization between the first sub-period and the entire period would indicate that the value-adding effects differ in nature between the event windows.
The second sub-period of the event window ranges from year three to year five. The effects dependent on changes in management, strategy or that by other means requiring more time to influence the operations of the PC, will not materialize in the form of changes in performance until some years after the VC entry. It is for this reason interesting to study if there are differences regarding the effects of the VC´s degree of industry specialization between the sub-periods.
The following paragraphs will present the five different performance measures included in our models. First, we present the two binary measures and the rationale for making use of binary performance measures. Thereafter, we present the continuous measures and explain why we include both measures in the analysis.
We define the two following performance measures as the ability of a portfolio company to increase their profits or revenues in the period in question. This measure is not subject to
concerns relating to extreme values, and may therefore be more robust than the continuous variables used elsewhere in the analysis. There are multiple firms in our sample experiencing large declines and increases relative to the other firms. A weakness with the use of binary performance measures is that they do not capture the variance among companies within the groups. Continuous variables are able to make use of differences not included in binary measures.