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[null,"1.","Record Nr.",null,"UNINA9910456029303321"]
[null,null,"Titolo",null,"Advances in portfolio construction and implementation [[electronic resource] /] / edited by Stephen Satchell, Alan Scowcroft"]
[null,null,"Pubbl/distr/stampa",null,"Amsterdam ; ; Oxford, : Butterworth-Heinemann, 2003"]
[null,null,"ISBN",null,"1-280-96633-5"]
[{"$nulls":4},"9786610966332"]
[{"$nulls":4},"1-4175-0763-2"]
[{"$nulls":4},"0-08-047184-6"]
[null,null,"Edizione",null,"[1st edition]"]
[null,null,"Descrizione fisica",null,"1 online resource (384 p.)"]
[null,null,"Collana",null,"Butterworth-Heinemann finance"]
[null,null,"Disciplina",null,"332.6"]
[null,null,"Soggetti",null,"Portfolio management"]
[{"$nulls":4},"Portfolio management - Mathematical models"]
[{"$nulls":4},"Investments"]
[{"$nulls":4},"Electronic books."]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Front Cover; Advances in Portfolio Construction and Implementation; Copyright Page; Contents; List of Contributors; Introduction; Chapter 1. A review of portfolio planning: models and systems; 1.1 Introduction and Overview; 1.2 Alternative Computational Models; 1.3 Symmetric and Asymmetric Measures of Risk; 1.4 Computational Models in Practice; 1.5 Preparation of Data: Financial Data Marts; 1.6 Solution Methods; 1.7 Computational Experience; 1.8 Discussions and Conclusions; 1.9 Appendix 1: Piecewise Linear Approximation of the Quadratic Form"]
[{"$nulls":4},"1.10 Appendix 2: Comparative Computational Views of the Alternative ModelsReferences; Web References; Acknowledgements; Chapter 2. Generalized mean-variance analysis and robust portfolio diversification; 2.1 Introduction; 2.2 Generalized Mean-Variance Analysis; 2.3 The State Preference Theory Approach to Portfolio Construction; 2.4 Implementation and Simulation; 2.5 Conclusions and Suggested Further Work; References; Chapter 3. Portfolio construction from mandate to stock weight: a practitioner's perspective; 3.1 Introduction; 3.2 Allocating Tracking Error for Multiple Portfolio Funds"]
[{"$nulls":4},"3.3 Tracking Errors for Arbitrary Portfolios3.4 Active CAPM, or How Far Should a Bet be Taken?; 3.5 Implementing Ideas in Real Stock "]
[{"$nulls":4},"Portfolios; 3.6 Conclusions; References; Chapter 4. Enhanced indexation; 4.1 Introduction; 4.2 Constructing a Consistent View; 4.3 Enhanced Indexing; 4.4 An Illustrative Example: Top-down or Bottom-up?; 4.5 Conclusions; 4.6 Appendix 1: Derivation of the Theil-Goldberger Mixed Estimator; 4.7 Appendix 2: Optimization; References; Notes; Chapter 5. Portfolio management under taxes; 5.1 Introduction; 5.2 Do Taxes Really Matter to Investors and Managers?"]
[{"$nulls":4},"5.3 The Core Problems5.4 The State of the Art; 5.5 The Multi-Period Aspect; 5.6 Loss Harvesting; 5.7 After-Tax Benchmarks; 5.8 Conclusions; References; Chapter 6. Using genetic algorithms to construct portfolios; 6.1 Limitations of Traditional Mean-Variance Portfolio Optimization; 6.2 Selecting a Method to Limit the Number of Securities in the Final Portfolio; 6.3 Practical Construction of a Genetic Algorithm-Based Optimizer; 6.4 Performance of Genetic Algorithm; 6.5 Conclusions; References; Chapter 7. Near-uniformly distributed, stochastically generated portfolios"]
[{"$nulls":4},"7.1 Introduction - A Tractable N-Dimensional Experimental Control7.2 Applications; 7.3 Dynamic Constraints; 7.4 Results from the Dynamic Constraints Algorithm; 7.5 Problems and Limitations with Dynamic Constraints Algorithm; 7.6 Improvements to the Distribution; 7.7 Results of the Dynamic Constraints with Local Density Control; 7.8 Conclusions; 7.9 Further Work; 7.10 Appendix 1: Review of Holding Distribution in Low Dimensions with Minimal Constraints; 7.11 Appendix 2: Probability Distribution of Holding Weight in Monte Carlo Portfolios in N Dimensions with Minimal Constraints"]
[{"$nulls":4},"7.12 Appendix 3: The Effects of Simple Holding Constraints on Expected Distribution of Asset Holding Weights"]
[null,null,null,"Sommario/riassunto","Modern Portfolio Theory explores how risk averse investors construct portfolios in order to optimize market risk against expected returns. The theory quantifies the benefits of diversification.Modern Portfolio Theory provides a broad context for understanding the interactions of systematic risk and reward. It has profoundly shaped how institutional portfolios are managed, and has motivated the use of passive investment management techniques, and the mathematics of MPT is used extensively in financial risk management.Advances in Portfolio Construction and Implementation o"]