The objective of this thesis was to see how fair value reporting and measuring is implemented in the private equity industry, and investigate the value relevance of BV and FV across type of investment and valuation method. To address the different challenges, I have looked at different accounting requirements in accordance with NGAAP, IFRS and USGAAP. The different accounting standards have shown us a complex framework for fair value accounting. Thus, additional guidelines (IPEV) have been published to try to overcome some of the challenges which are especially relevant for the private equity industry.
To test how good the private equity industry is to estimate fair value, I have conducted a study of realized investments by six Norwegian private equity companies. The study reveals that estimating fair value is not necessarily an easy operation. According to my data sample, 3 of 4 investments are underestimated, meaning that the fair value estimate is lower than the transaction price. The averaged deviation for the whole sample is -25 %. Based on the extent of accounting requirements and measuring guidelines, I expected the deviations to be less.
The data sample shows that multiples are the most common valuation technique used to estimate fair value. The findings are not very surprising, because the technique is
relatively easy and quick to use and recommended by the IPEV Guidelines. In addition, multiples contribute to the lowest difference between transaction price and fair value estimate with an average deviation of -14 %. Even though I should be careful with drawing fundamental conclusions, it is reassuring to see that the most common method also predicts the transaction price best.
The regression analysis has shown that, when applying only book value (BV) or fair value (FV) as independent variable, FV contributes to explain deviation in the
transaction price best. The rational explanation behind this result is that FV is the most value relevant figure when arriving upon an intrinsic value of the portfolio company.
Further, the analysis shows evidence to support that investment type explains some of variation in the transaction price. However, the explanation power when including type of investment, in addition to fair value estimates, does not increase.
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In light of theoretical expectations, BV should be able to explain some of the variation in the TP. This expectation is motivated by the fact that historical values are used as a base value in valuations. Thus, by expanding the regression equation with more independent variables, and making the model more advanced, it is possible to show that BV, together with not recognized goodwill (GW) and interaction effects of investment type (V), increase the explanation power of the equation. This is an interesting result, because it shows that BV is not irrelevant when assessing FV of an investment and consequently the belonging TP. Even though FV is a result of both BV and GW, the analysis shows evidence to suggest that the coefficient of determination and standard error of estimate improve due to coefficient effects when FV is divided between two variables. Notice that BV seems to be more value relevant for venture investments than other investments when the intrinsic value is estimated.
When applying multiple (M) as a fixed and interaction effect, BV is to be the most value relevant figure. However, equation is based on only 36 observations. Thus, we should not emphasize the results too much and be careful when comparing test results for and the other equations.
The different standard error of estimates and coefficient of determinations for each regression equation is summarized in the table below where they are ranked to each other.
Equation Sε R2 adj Variables (xi)
TP8 75,43 93,40 % BV; GW; M; (BV*M); (GW*M) TP7 75,47 90,70 % BV; GW; (BV*V); (GW*V) TP6 76,12 90,50 % BV; GW; V; (BV*V); (GW*V) TP2 82,80 89,00 % FV
TP3 82,95 88,80 % FV; V
TP4 83,27 88,70 % FV; V; (FV*V) TP5 83,72 88,50 % BV; GW; V TP1 112,98 79,50 % BV
Table 18 – Sε and R2 for all regression equations31
Over all, my findings seem to pinpoint the fact that fair value measuring in the private equity is extremely difficult. The lack of quoted prices, in combination with increased demand for transparency and fair value measuring, are factors that are difficult to
31 The coefficient of determination for equation 1 and 2 is unadjusted due to only one independent variable in the regression equation.
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combine. Fair value measuring is relatively new in the Norwegian private equity industry, which could suggest that the accuracy of the estimates should improve in accordance with practical experience. However, estimating fair value might seem as an impossible task when the lack of market information is an essential cause. Thus,
minimizing the deviations between the realized transaction price and fair value estimate could be an intermediate aim in order to improve the fair value estimates. From my point of view, I would say that it is important that investors and other stakeholders have faith in the reported figures if the industry should continue to develop in the future. This could be achieved by applying some of the improvements I have suggested in chapter 7.
In general, increased transparency is a key word for the industry. Thus, I would urge the industry to abandon the path of undue secretiveness. Less secretiveness could
contribute to put challenges regarding fair value measuring in the private equity
industry in the spotlight, and hopefully contribute to improve the valuation process and the quality of fair value estimates.
Focus on fair value measuring is important because both existing investors and potential future investors have a real interest in the fair value of investments in financial
statements. This is an important reason to get control of this process and to been seen to be operating at the top of the peer group in terms of the valuation process (PwC, 2008).
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