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 Models -- References -- 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 Portfolios -- 3.4 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 Problems -- 5.4 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 Control -- 7.2 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 -- 7.12 Appendix 3: The Effects of Simple Holding Constraints on Expected Distribution of Asset Holding Weights -- 7.13 Appendix 4: Properties of Hyper-Solids -- References -- Notes -- Chapter 8. Modelling directional hedge funds-mean, variance and correlation with tracker funds -- 8.1 Introduction -- 8.2 Mean and Variance of Directional Strategies -- 8.3 Correlation with Tracker Fund -- 8.4 Parameters Estimation -- 8.5 Optimal Allocation -- 8.6 An Empirical Application to the Currency Markets -- 8.7 Conclusions.

8.8 Appendix 1: Mean and Variance of Directional Strategies -- 8.9 Appendix 2: Correlation with Tracker Fund -- 8.10 Appendix 3: Optimal Allocation -- References -- Notes -- Acknowledgements -- Chapter 9. Integrating market and credit risk in fixed income portfolios -- 9.1 Introduction -- 9.2 How to Measure Market and Credit Risk -- 9.3 The Ways of Constructing Loss Distributions -- 9.4 Components of Credit Risk -- 9.5 Portfolio Approach -- 9.6 Conclusions -- 9.7 Appendix -- References -- Notes -- Chapter 10. Incorporating skewness and kurtosis in portfolio optimization: a multidimensional efficient set -- 10.1 Introduction -- 10.2 The Algebra of Multivariate Moments -- 10.3 The Portfolio Frontier: Expected Return, Skewness and Kurtosis -- 10.4 Conclusion -- References -- Notes -- Chapter 11. Balancing growth and shortfall probability in continuous time active portfolio management -- 11.1 Introduction -- 11.2 Some Basics -- 11.3 Active Portfolio Management -- 11.4 Trading off Risk and Return in Active Portfolio Management: Fractional Objectives -- 11.5 Risk-Constrained Minimal Time -- References -- Chapter 12. Assessing the merits of rank-based optimization for portfolio construction -- 12.1 Introduction -- 12.2 Optimal Portfolio with Ranks -- 12.3 Empirical Tests -- 12.4 Conclusions -- References -- Notes -- Chapter 13. The mean-downside risk portfolio frontier: a non-parametric approach -- 13.1 Introduction -- 13.2 The Mean-DSR Portfolio Frontier: The Traditional Approach -- 13.3 The Multivariate Case -- 13.4 A Kernel Approach -- 13.5 The Kernel Approach to the Multivariate Case -- 13.6 The Mean-DSR Portfolio Frontier Using Kernel Estimates -- 13.7 Asset Pricing -- 13.8 Conclusion -- References.

Chapter 14. Some exact results for efficient portfolios with given returns -- 14.1 Introduction -- 14.2 Properties of the Risk Estimator -- 14.3 Properties of the Estimated Portfolio Weights -- 14.4 The Riskless Asset Case -- 14.5 Conclusions -- 14.6 Appendix: The Unconditional Mean of α -- References -- Notes -- Chapter 15. Optimal asset allocation for endowments: A large deviations approach -- 15.1 Introduction -- 15.2 The Asset Allocation Model -- 15.3 An Illustrative Example -- 15.4 Conclusions -- References -- Notes -- Acknowledgements -- Chapter 16. Methods of relative portfolio optimization -- 16.1 Introduction -- 16.2 Some Background on Relative Portfolio Optimization -- 16.3 Model Approaches for Relative Portfolio Optimization -- 16.4 Discussion of the Models -- 16.5 Conclusion -- References -- Notes -- Chapter 17. Predicting portfolio returns using the distributions of efficient set portfolios -- 17.1 Introduction -- 17.2 Efficient Set Mathematics for Given µ and V -- 17.3 The Effect of Forecasts -- 17.4 Model and Process -- 17.5 Data and Empirical Results -- 17.6 Conclusions -- 17.7 Appendix: Effect of Estimation Error in µ -- References -- Notes -- Acknowledgements -- Index -- Advances in Portfolio Construction and Implementation.