US Patent 7,251,627 - Identifying stocks for inclusion in a portfolio

Publication number US7251627 B1
Publication type Grant
Application number US 09/406,394
Publication date Jul 31, 2007
Filing date Sep 27, 1999
Inventors Thomas E. Vass
Original Assignee Vass Thomas E

https://www.google.com/patents/US7251627

Portfolio Theory and Management - Baker and Filbeck - 2013 - Oxford - Book Information

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This 30-chapter book consists of seven sections. These chapters are: (1) portfolio theory and asset pricing, (2) the investment policy statement and fiduciary duties, (3) asset allocation and portfolio construction, (4) risk management, (V) portfolio execution, monitoring, and rebalancing, (6) evaluating and reporting portfolio performance, and (7) special topics.

Handbook of Portfolio Construction - John B. Guerard - 2010 - Book Information

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Springer - Publisher book link - has  a sample chapter for download

http://www.springer.com/economics/financial+economics/book/978-0-387-77438-1




Contents
Part I Markowitz for the Masses: Portfolio Construction Techniques
1 Markowitz for the Masses: The Risk and Return of Equity
and Portfolio Construction Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
John B. Guerard, Jr.
2 Markowitz and the Expanding Definition of Risk:
Applications of Multi-factor Risk Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
John B. Guerard, Jr.
3 Markowitz Applications in the 1990s and the New
Century: DataMining Corrections and the 130/30 .. . . . . . . . . . . . . . . . . . . . . . 61
John B. Guerard, Jr.
4 Markowitz’s Mean–Variance Rule and the Talmudic
Diversification Recommendation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
Haim Levy and Ran Duchin
5 On the Himalayan Shoulders of HarryMarkowitz . . . . . . . . . . . . . . . . . . . . . . .125
Paul A. Samuelson
6 Models for Portfolio Revision with Transaction Costs
in the Mean–Variance Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .133
Andrew H. Chen, Frank J. Fabozzi, and Dashan Huang
7 Principles for Lifetime Portfolio Selection: Lessons
from Portfolio Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .153
James H. VanderWeide
8 Harry Markowitz and the Early History
of Quadratic Programming.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .179
Richard W. Cottle and Gerd Infanger
9 Ideas in Asset and Asset–Liability Management
in the Tradition of H.M. Markowitz .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .213
William T. Ziemba
10 Methodologies for Isolating and Assessing the Portfolio
Performance Potential of Stock Return Forecast Models
with an Illustration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .259
Bernell K. Stone and John B. Guerard, Jr.
11 Robust Portfolio Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .337
R. Douglas Martin, Andrew Clark, and Christopher G. Green
Part II Owitz and the Expanding Definition of Risk: Applications
of Multi-Factor Risk Models
12 Applying Markowitz’s Critical Line Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . .383
Andras Niedermayer and Daniel Niedermayer
13 FactorModels in Portfolio and Asset Pricing Theory . . . . . . . . . . . . . . . . . . . .401
Gregory Connor and Robert A. Korajczyk
14 Applications of Markowitz Portfolio
Theory To Pension Fund Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .419
Edwin J. Elton, Martin J. Gruber, and Christopher R. Blake
15 Global Equity Risk Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .439
Jose Menchero, Andrei Morozov, and Peter Shepard
16 What Matters Most in Portfolio Construction? . . . . . . . . . . . . . . . . . . . . . . . . . . .481
Dean M. Petrich and Ronald N. Kahn
17 Risk Management and Portfolio Optimization for Volatile
Markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .493
Svetlozar T. Rachev, Borjana Racheva-Iotova, Stoyan
V. Stoyanov, and Frank J. Fabozzi
Part III Applications of Portfolio Construction, Performance
Measurement, and Markowitz DataMining Corrections Tests
18 Linking Momentum Strategies with Single-Period
Portfolio Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .511
John M. Mulvey,Woo Chang Kim, and Mehmet Bilgili
19 Reflections on Portfolio Insurance, Portfolio Theory,
and Market Simulation with HarryMarkowitz. . . . . . . . . . . . . . . . . . . . . . . . . . .529
Bruce I. Jacobs and Kenneth N. Levy
20 Evaluating Hedge Fund Performance: A Stochastic
Dominance Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .551
Sheng Li and Oliver Linton
21 Multiportfolio Optimization: A Natural Next Step . . . . . . . . . . . . . . . . . . . . . . .565
Martin W.P. Savelsbergh, Robert A. Stubbs, and Dieter
Vandenbussche
22 Alternative Model to Evaluate Selectivity
and Timing Performance of Mutual Fund Managers:
Theory and Evidence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .583
Cheng-few Lee, Alice C. Lee, and Nathan Liu
23 Case Closed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .601
Robert A. Haugen and Nardin L. Baker
24 Stock-Selection Modeling and Data Mining Corrections:
Long-Only Versus 130/30Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .621
John B. Guerard, Jr., Sundaram Chettiappan, and GanLin Xu
25 Distortion Risk Measures in Portfolio Optimization . . . . . . . . . . . . . . . . . . . . .649
Ekaterina N. Sereda, EfimM. Bronshtein, Svetozar T. Rachev,
Frank J. Fabozzi,Wei Sun, and Stoyan V. Stoyanov
26 A Benefit from the Modern Portfolio Theory
for Japanese Pension Investment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .675
Makoto Suzuki
27 Private Valuation of Contingent Claims
in a Discrete Time/State Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .691
Alan J. King, Olga Streltchenko, and Yelena Yesha
28 Volatility Timing and Portfolio Construction
Using Realized Volatility for the S&P500 Futures Index. . . . . . . . . . . . . . . . .711
Dimitrios D. Thomakos and Tao Wang
29 The Application of Modern Portfolio
Theory to Real Estate: A Brief Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .733
TimothyW. Viezer
About the Editor and Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .761
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 777

CAIA Level I: An Introduction to Core Topics in Alternative Investments - Book Information

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Advances in Portfolio Construction and Implementation - By: Alan Scowcroft; Stephen Satchell - Book Information

Publisher Butterworth Heinemann
2003


Contents



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
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 ﾵ 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 ﾵ
References
Notes
Acknowledgements
Index
Advances in Portfolio Construction and Implementation