LEADER 05741nam 2200757Ia 450 001 9910823089103321 005 20200520144314.0 010 $a1-280-96633-5 010 $a9786610966332 010 $a1-4175-0763-2 010 $a0-08-047184-6 035 $a(CKB)111090529102694 035 $a(EBL)288847 035 $a(OCoLC)171114053 035 $a(SSID)ssj0000099137 035 $a(PQKBManifestationID)11109066 035 $a(PQKBTitleCode)TC0000099137 035 $a(PQKBWorkID)10006641 035 $a(PQKB)11085803 035 $a(MiAaPQ)EBC288847 035 $a(CaSebORM)9780750654487 035 $a(Au-PeEL)EBL288847 035 $a(CaPaEBR)ebr10169918 035 $a(CaONFJC)MIL96633 035 $a(OCoLC)824148855 035 $a(PPN)170267032 035 $a(OCoLC)ocn824148855 035 $a(EXLCZ)99111090529102694 100 $a20021231d2003 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aAdvances in portfolio construction and implementation /$fedited by Stephen Satchell, Alan Scowcroft 205 $a1st edition 210 $aAmsterdam ;$aOxford $cButterworth-Heinemann$d2003 215 $a1 online resource (384 p.) 225 0 $aButterworth-Heinemann finance 300 $aDescription based upon print version of record. 311 $a0-7506-5448-1 320 $aIncludes bibliographical references and index. 327 $aFront 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 327 $a1.10 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 327 $a3.3 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? 327 $a5.3 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 327 $a7.1 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 327 $a7.12 Appendix 3: The Effects of Simple Holding Constraints on Expected Distribution of Asset Holding Weights 330 $aModern Portfolio Theory explores how risk averse investors construct portfolios in order to optimize market risk against expected returns. The theory quantifies the benefits of diversification.Modern Portfolio Theory provides a broad context for understanding the interactions of systematic risk and reward. It has profoundly shaped how institutional portfolios are managed, and has motivated the use of passive investment management techniques, and the mathematics of MPT is used extensively in financial risk management.Advances in Portfolio Construction and Implementation o 410 0$aQuantitative finance series 606 $aPortfolio management 606 $aPortfolio management$xMathematical models 606 $aInvestments 615 0$aPortfolio management. 615 0$aPortfolio management$xMathematical models. 615 0$aInvestments. 676 $a332.6 701 $aSatchell$b S$g(Stephen)$01156060 701 $aScowcroft$b Alan$0151564 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910823089103321 996 $aAdvances in portfolio construction and implementation$94201705 997 $aUNINA