The data sample

Based on figures provided by the six respondents, the whole data set consists of 55 observations which make up the analysis sample. Each observation gives information about different variables related to the former portfolio company. These variables are:

Variable Notation Definition

Book value

(mill NOK) BV

BV is the equity value reported in the annual/quarterly report by the portfolio company. Due to NGAAP reporting BV is usually historical cost values.

Fair value estimate

(mill NOK) FV

FV are the private equity fund's own estimate of the portfolio company’s reported equity. In other words, the value adjusted equity in the portfolio company. These estimates are figures available as close up to the transaction date as possible.

Transaction price

(mill NOK) TP

The TP is the achieved selling price when the portfolio company was sold in the market. Thus, I regard TP as the intrinsic value of the portfolio company.

Goodwill

(mill NOK) GW

GW is the portfolio company's excess value calculated by the fund manager. We can also define it as goodwill not recognized in the portfolio company's balance sheet. GW is defined as the difference between FV and BV.

Type of investment V

V is an indicator variable (dummy), where the value of V=1 if the portfolio company is a venture investment or 0 if the company is something else.

Valuation

methodology -

According to IPEV it is recommended to use, at least, two methodologies when estimating fair value. Thus, the

respondents have been able to report all methodologies used during valuation. The methodologies are preferred to be in accordance with the Guidelines, but if the methodologies do not correspond to those in the Guideline the company-specific¹⁶ valuation method is reported.

Table 5 – Definition of data sample variables

The following table¹⁷ gives information about the variables’ mean, standard deviation (StDev), first quartile (Q1), median and third quartile (Q3). In addition, the relationship between BV, FV and TP is enclosed expressed as two different multiples in order to give an impression of the relative difference between the mentioned variables:

Variable N Mean StDev Q1 Median Q3

16 By company-specific I mean the actual method used that is not in accordance with the IPEV Guidelines.

17 All figures are denoted in million NOK except for V which is a dummy variable.

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V 55 0,67 0,47 0,00 1,00 1,00

FV/BV 55 3,57 5,46 1,00 2,00 4,00 TP/BV 55 5,79 7,60 1,69 2,99 5,34

Table 6 – Descriptive statistics of sample variables

As we can see from the table above, the mean of BV, FV and TP is increasing. The logical reason behind this pattern is that TP values are, on average, larger than FV, while FV values are larger than BV. The standard deviation shows that variation from the mean, for these three variables, is larger for TP than FV. BV has the lowest standard deviation.

Notice that V is applied as a dummy variable which can only be 1 or 0. Thus, the mean states that 67 % of the observations are classified as venture investments. The

remaining 33 % is classified as buy-out investments. I would like to pinpoint that this is only a simplification I have done in order to reduce the number of dummy variables in the regression analysis. The complete distribution of investment types will be presented on page 61.

We can also compute the correlation between the sample’s variables. The correlation matrix shows that BV, FV, TP and GW are highly positive correlated with each other.

BV FV TP GW V

BV 1,00 - - - -

FV 0,94 1,00 - - -

TP 0,89 0,94 1,00 - - GW 0,83 0,97 0,91 1,00 -

V -0,32 -0,31 -0,26 -0,29 1,00

Table 7 – Correlation matrix

These correlations are important to have in mind during the testing of the hypotheses I defined earlier, because it can potentially cause some complications in the regression analysis. I will come back to possible complications if the problem arises.

An interesting relationship is to see in what occasions TP is greater, equal or lower than FV depending on the relationship between BV and FV. The whole distribution of TP versus FV, given the relationship between BV and FV, is summarized in table 8 on the next page. Table 8 tells us that 4 observations have a reported book value greater than estimated fair value. Of these, 2 observations achieved a transaction price greater than the estimate, while 2 were below the estimate.

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TP > FV 2 BV > FV 4 TP < FV 2 TP = FV 0 TP > FV 11 BV = FV 11 TP < FV 0

TP = FV 0 TP > FV 28 BV < FV 40 TP < FV 12 TP = FV 0

Table 8 – Distribution between BV, FV and TP

According to NGAAP and the prudence principle, the investment is supposed to be written down when fair value is lower than historical cost values. Based on that

principle it is somewhat strange that we can observe BV greater than FV. However, the data sample consists of the last reported BV, and the latest FV. Thus, one explanation why we observe BV greater than FV might be due to events affecting the value between reporting dates. The remaining 51 observations have BVs equal, or greater than FVs.

Independent of the relationship between BV and FV, the fund managers never compute a FV equal to the TP.

Due to different numbers of realizations between the private equity respondents, there is not an even distribution of observations in the sample. The distribution between the different private equity companies denoted A-F, is as followed:

Diagram 1 – Share of observations between respondents

Diagram 1 is important to have in mind when we interpret discoveries later on, because the different practice and skill level of each fund manager affects their respective fair value estimates.

A; 20

B; 12 C; 9

D; 8

E; 3F; 3

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We can also sort the different observations according to type of investment, which yields the following distribution:

Diagram 2 – Distribution of investment types

Notice that venture investments also include expansion investments. These investments are carried out in firms with considerable turnover, but where the fund managers consider the potential for growth increasing as high. I choose to characterize these investments as venture in order to use the same categorizing as presented in chapter 2.

In addition, these investments have more common features with venture investments than buy-outs due to growth potential rather than potential for value increasing.