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Advances in portfolio construction and implementation /
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...
| Clasificación: | Libro Electrónico |
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| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Amsterdam ; Oxford :
Butterworth-Heinemann,
2003.
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| Colección: | Cambridge elements. Elements in quantitative finance.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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.
- 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.
- 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 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?
- 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.
- 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.