Academic and business appraisals 

We were instructed to assess potential damages arising from possibly false promises regarding a business idea. The potentially violated party had invested a large amount in reliance on information about a business model. However, it turned out that the information on the business model was incorrect, resulting in a partial loss of the investment amount. The question now was: What business success could the injured party have actually expected if the promises had been true? 

At the core of such analyses is regularly the simulation of a so-called counterfactual state, i.e. the assumption of a holistic alternative historical development according to the case that the promises would have been appropriate. A one-dimensional focus (“what would have been the effects in the context of the observable environment?”) is not sufficient here; rather, attention must always be paid to the possible interactions of the central variable with the environment. In this specific case, according to our analyses, the business model in its successful state would have had an impact on the local price level, on the supply of labour in the region (and thus on the salary level) and on the competitive situation. We therefore simulated a complex economic system – as far as possible based on data and using quantitative tools – and carried out the assessment of the success of the business model, which was necessary to determine potential damage, in an integrative manner. 

As part of the determination of equity interest rates for the fourth regulatory period in incentive regulation in the energy sector in Germany, we were commissioned by several utility companies in 2022 to critically analyse the decisions of the Federal Network Agency (BNetzA) and the expert opinions on which they were based. In its decision of 12 October 2021, the BNetzA had previously set an after-tax return on equity of 4.13 % for new plants for the fourth regulatory period of incentive regulation (2023-2027 for gas network operators, 2024-2028 for electricity network operators). 

Our analyses focused on the market risk premium and the beta factor. Without going into too much detail here, it should be noted that we were able to uncover numerous new aspects and problem areas in the BNetzA’s reasoning and derivation, despite the existence of several other critical third-party reports and opinions. The most important point regarding the market risk premium was that the BNetzA used an incorrect definition of the “as-if-risk-free” base investment – via which the market risk premium was derived using historical data series of market returns. The total return index of bonds used by the BNetzA does not fulfil the requirements of as-if-risk-free due to its unclear final value at the time of investment. In addition, problems with the regression technique (so-called error-in-variables problems) became apparent when deriving the beta factor. 

We were commissioned by an institutional investor to develop a model to take customer concentration risk into account when deriving a company’s cost of capital. The aim was not only to qualitatively identify the influence of a high dependency on a small number of customers, but also to quantitatively transfer it to the risk-adjusted cost of capital – as a basis for an appropriate company valuation and performance measurement. 

In the first step, we analysed the cash flow components separately and isolated their specific risk contributions. The focus here was on identifying the revenues-related risk in which a customer concentration essentially makes itself felt. On the basis of a hypothetically fully diversified customer portfolio, we then modelled the effect of a real existing concentration by specifically reversing the diversification effect, i.e. by adjusting the customer portfolio correlation coefficients. The procedure largely followed the ideas of the portfolio selection theory (Markowitz 1952, 1959) familiar from the investment sector.  

The cash flow components were then recombined into an aggregate cash flow, taking into account their adjusted risk contributions. The result was a practical, differentiated cost of capital model that allows customer concentration risk to be directly integrated into valuation and performance measurement. It therefore makes an important contribution to better risk identification in company-related cash flow models – particularly in sectors with a highly asymmetrical customer structure.