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  1. Optimal allocation to private equity
    Autor*in: Giommetti, Nicola; Sørensen, Morten
    Erschienen: [2021]
    Verlag:  Tuck School of Business at Dartmouth], [Hanover, NH

    We study the asset allocation problem of an institutional investor (LP) that invests in stocks, bonds, and private equity (PE). PE investments are risky, illiquid, and long-term. The LP repeatedly commits capital to PE funds, and this capital is... mehr

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    Standort:
    Helmut-Schmidt-Universität, Universität der Bundeswehr Hamburg, Universitätsbibliothek
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    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    We study the asset allocation problem of an institutional investor (LP) that invests in stocks, bonds, and private equity (PE). PE investments are risky, illiquid, and long-term. The LP repeatedly commits capital to PE funds, and this capital is gradually called and eventually distributed back to the LP. We find that PE investments substantially affect the LP’s optimal allocations. LPs with higher and lower risk aversion follow qualitatively different investment strategies, and PE allocations are not monotonically declining in risk aversion. We extend the model with a secondary market for PE partnership interests to study the implications of trading in this market and the pricing of NAV and unfunded liabilities

     
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    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    Schriftenreihe: [Tuck School of Business working paper ; no. 3761243]
    Schlagworte: Private Equity; Limited Partner; Asset Allocation; Portfolio Problem; Illiquidity; Secondary Market
    Umfang: 1 Online-Ressource (circa 66 Seiten), Illustrationen
  2. Model of banking behavior
    specification and estimation
    Autor*in: Shibata, Tsubasa; Kosaka, Hiroyuki
    Erschienen: [2019]
    Verlag:  Institute of Developing Economies (IDE), JETRO, Chiba, Japan

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    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    VS 135
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    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    hdl: 2344/00050849
    Schriftenreihe: IDE discussion paper ; no. 755
    Schlagworte: Banking Behavior; Loan Market; Asset and Liability Management; Asset Allocation
    Umfang: 1 Online-Ressource (circa 24 Seiten), Illustrationen
  3. The jury is still out on the performance of naïve diversification (1/N rule)
    Autor*in: Elkamhi, Redouane; Salerno, Marco
    Erschienen: [2020]
    Verlag:  [University of Toronto - Rotman School of Management], [Toronto]

    DeMiguel et. al. (2009b) made a compelling case that estimation error dwarfs diversification benefits resulting in naive diversification (1/N) dominating mean-variance portfolios. We illustrate the necessary and sufficient conditions for risk-based... mehr

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    Standort:
    Helmut-Schmidt-Universität, Universität der Bundeswehr Hamburg, Universitätsbibliothek
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    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Fernleihe:
    keine Fernleihe

     

    DeMiguel et. al. (2009b) made a compelling case that estimation error dwarfs diversification benefits resulting in naive diversification (1/N) dominating mean-variance portfolios. We illustrate the necessary and sufficient conditions for risk-based allocation rules to be optimal in a mean-variance framework. We show empirically that many common datasets satisfy such conditions, making these rules preferred to mean-variance in the presence of estimation error. Our out-of-sample tests show that these rules outperform both mean-variance and 1/N. Further, we show that clustering the data using machine learning enhances the diversification benefits of these rules by making the data closer to the required conditions for optimality

     
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    Sprache: Englisch
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    Schriftenreihe: [Rotman School of Management working paper ; no. 3638713]
    Schlagworte: Portfolio Choice; Asset Allocation; Machine Learning; Clustering
    Weitere Schlagworte: Array
    Umfang: 1 Online-Ressource (circa 67 Seiten), Illustrationen
  4. Unpacking the ESG ratings
    does one size fit all?
    Autor*in: Billio, Monica; Fitzpatrick, Aoife Claire; Latino, Carmelo; Pelizzon, Loriana
    Erschienen: [2024]
    Verlag:  Leibniz Institute for Financial Research SAFE, Sustainable Architecture for Finance in Europe, [Frankfurt am Main]

    In this study, we unpack the ESG ratings of four prominent agencies in Europe and find that (i) each single E, S, G pillar explains the overall ESG score differently, (ii) there is a low co-movement between the three E, S, G pillars and (iii) there... mehr

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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    DS 431
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    In this study, we unpack the ESG ratings of four prominent agencies in Europe and find that (i) each single E, S, G pillar explains the overall ESG score differently, (ii) there is a low co-movement between the three E, S, G pillars and (iii) there are specific ESG Key Performance Indicators (KPIs) that are driving these ratings more than others. We argue that such discrepancies might mislead firms about their actual ESG status, potentially leading to cherry-picking areas for improvement, thus raising questions about the accuracy and effectiveness of ESG evaluations in both explaining sustainability and driving capital toward sustainable companies.

     
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    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    hdl: 10419/284398
    Schriftenreihe: SAFE working paper ; no. 415 (February 2024)
    Schlagworte: ESG Investing; ESG ratings; Asset Allocation; Portfolio Management; Sustainable Finance
    Umfang: 1 Online-Ressource (circa 18 Seiten), Illustrationen
  5. Pension funds interconnections and herd behavior
    Autor*in: Bauer, Rob; Bonneti, Matteo; Broeders, Dirk
    Erschienen: [2018]
    Verlag:  De Nederlandsche Bank NV, Amsterdam, the Netherlands

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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Schriftenreihe: Working paper / De Nederlandsche Bank NV ; no. 612 (October 2018)
    Schlagworte: Herd Behavior; Pension Funds; Asset Allocation; Alternative Asset Classes; Spatial Econometrics; Interconnection
    Umfang: 1 Online-Ressource (circa 67 Seiten), Illustrationen
  6. Reinforcement learning and portfolio allocation
    challenging traditional allocation methods?
    Autor*in: Lavko, Matus Jan; Klein, Tony; Walther, Thomas
    Erschienen: 2 February 2023
    Verlag:  Queen's University, Belfast, Management School, [Belfast]

    We test the out-of-sample trading performance of model-free reinforcement learning (RL) agents and compare them with the performance of equally-weighted portfolios and traditional mean-variance (MV) optimization benchmarks. By dividing European and... mehr

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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    DS 843
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    We test the out-of-sample trading performance of model-free reinforcement learning (RL) agents and compare them with the performance of equally-weighted portfolios and traditional mean-variance (MV) optimization benchmarks. By dividing European and U.S. indices constituents into factor datasets, the RL-generated portfolios face different scenarios defined by these factor environments. The RL approach is empirically evaluated based on a selection of measures and probabilistic assessments. Training these models only on price data and features constructed from these prices, the performance of the RL approach yields better risk-adjusted returns as well as probabilistic Sharpe ratios compared to MV specifications. However, this performance varies across factor environments. RL models partially uncover the nonlinear structure of the stochastic discount factor. It is further demonstrated that RL models are successful at reducing left-tail risks in out-of-sample settings. These results indicate that these models are indeed useful in portfolio management applications.

     
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    Sprache: Englisch
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    Weitere Identifier:
    hdl: 10419/271267
    Schriftenreihe: QMS working paper ; 2023, 01
    Schlagworte: Asset Allocation; Reinforcement Learning; Machine Learning; Portfolio Theory; Diversification
    Umfang: 1 Online-Ressource (circa 49 Seiten), Illustrationen
  7. Portfolio allocation, income uncertainty and households’ flight from risk
    Autor*in: Brown, Sarah; Gray, Dan; Harris, Mark N.
    Erschienen: December 2016
    Verlag:  The University of Sheffield, Department of Economics, Sheffield

    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    VS 202 (2016,012)
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    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Schriftenreihe: Sheffield economic research paper series ; SERPS no. 2016012
    Schlagworte: Asset Allocation; Background Risk; Flight from Risk; Fractional Models
    Umfang: 1 Online-Ressource (circa 40 Seiten), Illustrationen