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Risk-Return Analysis, Volume 2: The Theory and Practice of Rational Investing

Book Description

The Nobel Prize-winning Father of Modern Portfolio Theory returns with new insights on his classic work to help you build a lasting portfolio today


Contemporary investing as we know it would not exist without these two words: “Portfolio selection.” Though it may not seem revolutionary today, the concept of examining and purchasing many diverse stocks—creating a portfolio—changed the face of finance when Harry M. Markowitz devised the idea in 1952.

In the past six decades, Markowitz has risen to international acclaim as the father of Modern Portfolio Theory (MPT), with his evaluation of the impact of asset risk, diversification, and correlation in the risk-return tradeoff. In defending the idea that portfolio risk was essential to strategic asset growth, he showed the world how to invest for the long-run in the face of any economy.

In Risk Return Analysis, this groundbreaking four-book series, the legendary economist and Nobel Laureate returns to revisit his masterpiece theory, discuss its developments, and prove its vitality in the ever-changing global economy. Volume 2 picks up where the first volume left off, with Markowitz’s personal reflections and current strategies. In this volume, Markowitz focuses on the relationship between single-period choices—now—and longer run goals. He discusses dynamic systems and models, the asset allocation “glide-path,” inter-generational investment needs, and financial decision support systems.

Written with both the academic and the practitioner in mind, this richly illustrated volume provides investors, economists, and financial advisors with a refined look at MPT, highlighting the rational decision-making and probability beliefs that are essential to creating and maintaining a successful portfolio today.


Table of Contents

  1. Cover Page
  2. Title Page
  3. Copyright Page
  4. Addendum to Volume I
  5. Contents
  6. Preface
  7. Acknowledgments
  8. Chapter 6. The Portfolio Selection Context
    1. Introduction
    2. Temporal Structure and Today’s Choice
    3. Stakeholders Versus “the Investor”
    4. Investor Roles
    5. Diversification Needs and Opportunities: Recognized and Unrecognized
    6. Agenda: Analysis, Judgment, and Decision Support Systems
  9. Chapter 7. Modeling Dynamic Systems
    1. Introduction
    2. Definitions
    3. The EAS-E Worldview
    4. The Modeling Process
    5. An EAS Example
    6. Graphical Depiction of Attributes
    7. Graphical Depiction of Sets
    8. Further Specifications
    9. Describing Time
    10. Simultaneity
    11. Endogenous Events Versus Endogenous Phenomena
    12. JLMSim Events
    13. Simplicity, Complexity, Reality
    14. The SIMSCRIPT Advantage
    15. GuidedChoice and the Game of Life
    16. The GC DSS Database
    17. Simulator Versus DSS Modeling
    18. Issues and Alternatives
    19. The SIMSCRIPTs
    20. The Process View
    21. Subsidiary Entities
    22. SIMSCRIPT III Features
    23. Continued in Chapter 12
  10. Chapter 8. Game Theory and Dynamic Programming
    1. Introduction
    2. PRWSim (a Possible Real-World Simulator)
    3. Concepts from Game Theory
    4. Non-“Theory of Games” Games
    5. Randomized Strategies
    6. The Utility of a Many-Period Game
    7. Dynamic Programming
    8. Solving Tic-Tac-Toe
    9. Conditional Expected Value: An Example
    10. Generalization
    11. Partitions, Information, and DP Choice: An Example
    12. Generalization: Two Types of Games
    13. The Curse of Dimensionality
    14. Factorization, Simplification, Exploration, and Approximation
  11. Chapter 9. The Mossin-Samuelson Model
    1. Introduction
    2. The MS Model and Its Solution
    3. Markowitz Versus Samuelson: Background
    4. Glide-Path Strategies and Their Rationales
    5. Relative Risk Aversion
    6. The GuidedSavings Utility Function
    7. The Well-Funded Case
    8. A Game-of-Life Utility Function
  12. Chapter 10. Portfolio Selection as a Social Choice
    1. Introduction
    2. Arrow’s Paradox
    3. The Goodman and Markowitz (1952) (GM) Theorems
    4. Social Ordering for RDMs
    5. Hildreth’s Proposal
    6. Markowitz and Blay (MB) Axioms
    7. Arithmetic Versus Geometric Mean Utility
    8. Symmetry Revisited
    9. Rescaling Ploys
    10. Voting Blocks
    11. The Luce, Raiffa, and Nash (LRN) Choice Rule
    12. Nash Symmetry
    13. A Proposal
    14. Liberté, Égalité, Prospérité
  13. Chapter 11. Judgment and Approximation
    1. Introduction
    2. EU Maximization: Exact, Approximate; Explicit, Implicit
    3. The Household as Investor
    4. The Markowitz and van Dijk Methodology
    5. The Blay-Markowitz NPV Analysis
    6. The TCPA Process
    7. Estimating PV Means, Variances, and Covariances
    8. Displaying the Efficient Frontier
    9. Resampled AC/LOC Portfolios
    10. TCPA 1.0 Assumptions
    11. Beyond Markowitz
    12. “Buckets”: A Brief Literature Review
    13. The “Answer Game”
    14. First the Question, Then the Answer
  14. Chapter 12. The Future
    1. Introduction
    2. JSSPG
    3. Proposals
    4. Current Practice
    5. Agenda
    6. Level 6
    7. SIMSCRIPT Facilities
    8. IBM EAS-E Features
    9. Like the Phoenix
    10. Level 7
    11. SIMSCRIPT M Enhancements
    12. Computing: Past, Present, and Future
    13. Von Neumann (1958): The Computer and the Brain
    14. The Computer and the Brain, Revisited
    15. Emulation, Not Replication
    16. Event Invocation of a Third Kind
    17. Processes That Process Processes
    18. Easily Parallelized Processes (EPPs)
    19. Local Resource Groups
    20. Micro Versus Macro Parallelization
    21. Epilogue
  15. Notes
  16. References
  17. Index