Abstract

PurposeThe purpose of this paper is to close the transparency gap by comparing ex ante and ex post performance disclosure, thus providing important conclusions regarding the transparency of this important German market segment.Design/methodology/approachClosed-ended real estate funds (CEREFs) are one of the biggest segments of unlisted private equity funds in Germany. CEREFs have a central “profitability promise” that is based on ex ante forecasts given in the prospectus. Typically, equity is tied to these investments for up to 20-30 years, leaving investors highly insecure whether their expectations will be fulfilled and fund managers actually achieve prospected performances ex post.FindingsThe performance variance analysis of all German CEREFs outstanding during the global financial crisis reveals that prospect-performance disclosures as well as prospect-performance variances cause substantial problems in Germany due to overestimation biases of many fund managers.Research limitations/implicationsAs typical for the recent scholarly debate, also the past disclosure practice in Germany prohibits a long-term performance analysis, unless researchers apply instruments of modern investment analysis like comprehensive financial plans (“Visualisation of Financial Implications)”.Practical implicationsThe transparency developments in CEREF-reporting of the last decade deliver precise recommendations regarding the internal and external performance variance analysis, risk-profiles and stress tests for the future fund management.Social implicationsThe introduced methodology would increase transparency in the segment of CEREF and, thus, improve investor protection. Since private households in Germany mainly acquire these funds, this is a contribution to sustainability in private asset management.Originality/valueThe paper develops a new methodological framework for performance measurement of unlisted funds. It then assesses for the first time the impact of transparency and trust on fund performances by applying a performance variance analysis.

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