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|a The Predictive Power of the J-Curve
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|a In: Published in ACRN Journal of Finance and Risk Perspectives Vol. 3, Issue 4, Dec 2014, ISSN 2305-7394
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|a Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments July 10, 2013 erstellt
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|a Dealing with a recurring low level of data quality in private equity, we propose a novel approach of understanding the behavior of private equity funds (PEFs): we use PEFs' illiquidity as a factor of analysis. To do so, we measure the distance between PEF cash-flows (“J-Curves”) and return categories (“ideal-types”) that we have identified through our novel reasoning. As a result, our model excludes the attribution of a given fund from certain return categories in early years. It then attributes a fund to a specific category with a high level of confidence. By doing so, this model could help reducing solvency costs of investing in PEFs, as well as support the analysis of existing PEFs either by their current investor or for new investors on the secondary market
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Dealing with a recurring low level of data quality in private equity, we propose a novel approach of understanding the behavior of private equity funds (PEFs): we use PEFs' illiquidity as a factor of analysis. To do so, we measure the distance between PEF cash-flows (“J-Curves”) and return categories (“ideal-types”) that we have identified through our novel reasoning. As a result, our model excludes the attribution of a given fund from certain return categories in early years. It then attributes a fund to a specific category with a high level of confidence. By doing so, this model could help reducing solvency costs of investing in PEFs, as well as support the analysis of existing PEFs either by their current investor or for new investors on the secondary market |
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In: Published in ACRN Journal of Finance and Risk Perspectives Vol. 3, Issue 4, Dec 2014, ISSN 2305-7394, Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments July 10, 2013 erstellt |
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[S.l.], SSRN, [2015] |
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Demaria, Cyril aut, The Predictive Power of the J-Curve, [S.l.] SSRN [2015], 1 Online-Ressource (48 p), Text txt rdacontent, Computermedien c rdamedia, Online-Ressource cr rdacarrier, In: Published in ACRN Journal of Finance and Risk Perspectives Vol. 3, Issue 4, Dec 2014, ISSN 2305-7394, Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments July 10, 2013 erstellt, Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 unrestricted online access, Dealing with a recurring low level of data quality in private equity, we propose a novel approach of understanding the behavior of private equity funds (PEFs): we use PEFs' illiquidity as a factor of analysis. To do so, we measure the distance between PEF cash-flows (“J-Curves”) and return categories (“ideal-types”) that we have identified through our novel reasoning. As a result, our model excludes the attribution of a given fund from certain return categories in early years. It then attributes a fund to a specific category with a high level of confidence. By doing so, this model could help reducing solvency costs of investing in PEFs, as well as support the analysis of existing PEFs either by their current investor or for new investors on the secondary market, https://ssrn.com/abstract=2296967 X:ELVSSRN Verlag kostenfrei, https://doi.org/10.2139/ssrn.2296967 X:ELVSSRN Resolving-System kostenfrei, https://doi.org/10.2139/ssrn.2296967 LFER, https://ssrn.com/abstract=2296967 LFER, LFER epn:4199258973 2022-10-19T17:26:06Z |
| spellingShingle |
Demaria, Cyril, The Predictive Power of the J-Curve, Dealing with a recurring low level of data quality in private equity, we propose a novel approach of understanding the behavior of private equity funds (PEFs): we use PEFs' illiquidity as a factor of analysis. To do so, we measure the distance between PEF cash-flows (“J-Curves”) and return categories (“ideal-types”) that we have identified through our novel reasoning. As a result, our model excludes the attribution of a given fund from certain return categories in early years. It then attributes a fund to a specific category with a high level of confidence. By doing so, this model could help reducing solvency costs of investing in PEFs, as well as support the analysis of existing PEFs either by their current investor or for new investors on the secondary market |
| title |
The Predictive Power of the J-Curve |
| title_auth |
The Predictive Power of the J-Curve |
| title_full |
The Predictive Power of the J-Curve |
| title_fullStr |
The Predictive Power of the J-Curve |
| title_full_unstemmed |
The Predictive Power of the J-Curve |
| title_short |
The Predictive Power of the J-Curve |
| title_sort |
the predictive power of the j-curve |
| title_unstemmed |
The Predictive Power of the J-Curve |
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https://ssrn.com/abstract=2296967, https://doi.org/10.2139/ssrn.2296967 |
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