Contents -- Foreword -- Acknowledgements -- Chapter 1 Overview -- Trains of Thought -- What Do We Know? -- Probability, Utility and Quadratic Approximations -- Simulation and Systems Descriptions -- Personal Reflections -- Notes -- References -- Chapter 2 1952 -- Portfolio Selection -- The Early History of Portfolio Theory 1600-1960 -- Portfolio Theory: 1952 -- Markowitz Portfolio Theory circa 1959 -- Tobin (1958) -- Hicks (1935,1962) -- Marschak (1938) -- Williams (1938) -- Leavens (1945) -- The End of the Beginning -- Notes -- References -- The Utility of Wealth -- Chapter 3 Rand [I] and The Cowles Foundation -- Industry-wide, Multi-industry and Economy-wide Process Analysis -- I. Introduction -- 2. Subanalysis for the petroleum refining industry -- 3. Problems of economy-wide analysis -- 4· Process analysis and other models of the economy -- 5. A list of works cited. -- Alternate Methods of Analysis -- INTRODUCTION -- GROSS NATIONAL PRODUCT ANALYSIS -- REQUIREMENTS ANALYSIS -- INPUT-OUTPUT ANALYSIS -- INPUT-OUTPUT ANALYSIS (CONTINUED) -- SUMMARY -- REFERENCES -- The Elimination Form of the Inverse and its Application to Linear Programming -- Introduction -- The Elimination ForIn of Inverse -- Application to Linear Programming -- The Optimization of a Quadratic Function Subject to Linear Constraints -- 1. QUADRATIC PROBLEMS -- 2. ASSUMPTIONS -- 3. THE CRITICAL LINE :t -- 4. CRITICAL LINES ( ) -- 5. INTERSECTIONS OF CRITICAL LINES -- NON-DEGENERACY CONDITIONS -- 6. THE ALGORITHM UNDER CONDITIONS 1 THROUGH 4 -- 7. THE ALGORITHM UNDER CONDITIONS 3 AND 4 -- 8. THE ALGORITHM WHEN LE IS DEGENERATE BUT UNIQUE -- 9. THE ALGORITHM WHEN LE IS NOT UNIQUE -- 10. THE ALGORITHM, WHEN CONDITION 3 DOES NOT HOLD -- 11. THE SET OF EFFICIENT E, V COMBINATIONS -- 12. MINIMIZING A QUADRATIC -- The General Mean-variance Portfolio Selection Problem -- 1. The problem.

2. Application -- 3. Computation -- 4. Degeneracy and other problems -- References -- Chapter 4 Rand [II] and CACI -- Simulating with SIMSCRIPT -- The Simscript Method of Communication -- References -- Programming by Questionnaire -- PREFACE -- CONTENTS -- I. INTRODUCTION -- II. THE MECIfANICS OF PROGRAM GENERATION -- USING A PROGRAM GENERATOR -- THE QUESTIONNAIRE -- THE STATEMENT LIST -- DECISION TABLES -- THE EDITOR -- III. OBSERVATIONS AND DISCUSSION -- OTHER TECHNIQUES -- EXTENSIONS OF THE PROGRAM GENERATION OONCEPT -- CHANGING THE GENERATOR AND MJDIFYING GENERATED PIDGRAMS -- VARIANTS OF THE QUESTIONNAIRE -- DIFFICULTIES YET UNSOLVED -- CONCLUSION -- Appendix A -- Appendix B -- SIMSCRIPT -- HISTORY -- BASIC CONCEPTS -- LET AREA(CITy)=SQUARE.MILES -- SIMULATION APPLICATIONS -- IF FREE(MACH.GRP»O AND AVAIL(WHO.SERVES(MACH.GRP»>O -- AN ENTITY, ATTRIBUTE, SET, AND EVENT VIEW OF DATA BASE SYSTEMS -- THE SIMSCRIPT LANGUAGE WRITING LANGUAGE -- CREATE JOB CALLED J -- CREATE A JOB WITH ARR.TM = TIME. V, JTYPE = S, AND PLACE = F.ROUTING(S) -- DEFINE J AS A JOB REFERENCE -- DEFINE T AS A PERMANENT ENTITY REFERENCE -- SUMMARY -- REFERENCES -- Barriers to the Practical Use of Simulation Analysis -- 1. INTRODUCTION -- 2. PAST -- 3. PRESENT -- 4. FUTURE -- 5. POSTSCRIPT AND SUMMARY -- REFERENCES -- Chapter 5 IBM's T. J. Watson Research Center -- Comments -- References -- Approximating Expected Utility by a Function of Mean and Variance -- I. A Can of Approximations -- II. Analysis of Error Functions -- III. Empirical Results -- IV. Some Objections Reconsidered -- V. The E,V Investor -- REFERENCES -- Mean-variance Versus Direct Utility Maximization -- ABSTRACT -- I. The Problem -- ll. The Quality of the Approximation -- III. The Selected Utility Functions -- IV. The Data -- V. The Empirical Results -- VI. The Effect of Leverage -- VII. Conclusions -- Appendix.

REFERENCES -- The Value of a Blank Check -- CONCEPTS OF A BLANK CHECK LOTTERY -- EXHIBIT 1 -- EXHIBIT 2 RRA VERSUS R. FOR TWO UTILITY FUNCTIONS -- A SURVEY -- EXHIBIT 3 -- A PROBLEM WITH EXPONENTIAL UTlllTY -- EXHIBIT 4 -- THE EFFECTS OF HUMAN CAPITAL -- APPliCATION TO EVALUATING MEAN-VARIANCE APPROXIMATIONS -- EXHIBIT 5 -- SUMMARY AND CONCLUSION -- APPENDIX -- REFERENCES -- The "Two beta" Trap -- Portfolio Analysis with Factors and Scenarios -- ABSTRACT -- I. The Model -- A. The Usual States of the World Model -- B. Modeling the E'S as being Conditionally Uncorrelated -- C. Combining Scenarios and Factors -- II. DiagonaIization -- III. Conclusion -- REFERENCES -- Sparsity and Piecewise Linearity in Large Portfolio Optimization Problems -- ABSTRACT -- 1. INTRODUCTION -- 2. MODELS FOR THE ESTIMATION OF COVARIANCES -- 3. EXISTING OPTIMIZATION METHODS -- 4. REVIEW OF THE COMPLEMENTARY PIVOT ALGORITHM -- 4.1 Getting started -- 4.2 Basis changes -- 5. EXPLOITING THE STRUCTURE OF C -- 5.1 Use with a sparse matrix package -- 5.2 Implementation out-oE-core -- 6. IMPLICIT TREATMENT OF UPPER AND LOWER BOUNDS AND TRANSACTIONS COSTS -- 7. CONCLUSIONS AND SUMMARY -- REFERENCES -- DISCUSSION -- The ER and EAS Formalisms for System Modeling and the EAS-E Language -- 1. INTRODUCTION -- 2. THE EAS WORLDVIEW -- 3. EAS and ER -- 4. THE EAS-E LANGUAGE -- 5. THE REST OF THE ICEBERG -- 6.THEEASPRINCIPLE. -- 7. APPLICABILITY TO ER -- 7 . REFERENCES -- EAS-E: An Integrated Approach to Application Development -- 1. INTRODUCTION -- 2. THE EAS MODEL -- 3. THE EAS-E PROGRAMMING LANGUAGE -- 3.1 Sets as Standard Data Structures -- 3.2 Integrated Language -- 3.3 Some Examples of EAS-E Syntax -- 3.4 Entity, Attribute, and Set Definitions -- 3.5 A PL-I/SQL Program and an Equivalent EAS-E Program -- 3.6 A PLAIN Program and an Equivalent EAS-E Program -- 4. THE BROWSE FACILITY.

5. SOME IMPLEMENTATION DETAILS -- 5.1 Database Ranked Sets -- 5.2 Reference and Identifier Variables -- 5.3 Recording and Unlocking -- 6. THE EAS-E DATABASE MANAGEMENT SYSTEM -- 6.1 Concurrency Control -- 6.2 Protecting Against Software Crashes -- 6.3 Protecting Against Physical Damage -- 7. SUMMARY AND STATUS -- REFERENCES -- The System Architecture of EAS-E: An Integrated -- The System Architecture of EAS-E: An Integrated Programming and Database Language -- Very little is required to go from the conceptual model to the application program. -- To write a program that works with the data base, the user must specify which entity types are to be maniplated. -- Simple queries can be written as small EAS·E programs. -- The problem of passing the selection information at execution time to the loop-searching mechanism has been addressed by designing an EAS structure to contain that information. -- EAS- E has been designed to accommodate data bases of arbitrary size, from very small to very large. -- Samuelson and Investment for the Long Run -- Background -- The Expected Log Rule in General and Particular -- First Argument For Max E log -- Argument Against Max E log -- Example -- Another Argument For Max E log -- Summary -- References -- Investment for the Long Run: New Evidence for an Old Rule -- I. BACKGROUND -- II. THE SEQUENCE OF GAMES -- III. ALTERNATE SEQUENCE-Of-GAMES FORMALIZATIONS -- IV. UNENDING GAMES -- V. CONCLUSIONS -- APPENDIX -- REFERENCES -- Chapter 6 Baruch College (CUNY) and Daiwa Securities -- Investment Rules, Margin and Market Volatility -- THE SIMULATED MARKET -- EFFECT OF VARYING THE NUMBER OF PORTFOLIO INSURERS -- CONCLUSIONS, CAVEATS, AND CONJECTURES -- APPENDIX -- REFERENCES -- Risk Adjustment -- Traditional CAPMs -- Risk Adjustment in the Standard, Homogeneous Model -- Observations and Extensions -- Epilogue -- REFERENCES.

Normative Portfolio Analysis: Past, Present and Future -- I. Normative Portfolio Analysis as of 1959 -- II. Normative versus Positive Portfolio Analysis -- III. Progress and Opportunity in Normative Analysis -- References -- Individual versus Institutional Investing -- THESIS -- ANTITHESIS -- SYNTHESIS -- NOTES -- REFERENCES -- Foundations of Portfolio Theory -- REFERENCES -- Fast Computation of Mean-variance Efficient Sets Using Historical Covariances -- ABSTRACT -- I. INTRODUCTION -- II. REFORMULATION OF THE PROBLEM -- Ill. IMPLEMENTATION OF THE CRITICAL LINE ALGORITHM -- IV. PERFORMANCE -- V. APPENDIX -- NOTES -- REFERENCES -- Computation of Mean-semivariance Efficient Sets by the Critical Line Algorithm -- 1. Introduction -- 2. Review of mean-variance model -- 2.1. THE MEAN-VARIANCE PROBLEM -- 2.2. THE CRITICAL LINE ALGORITHM -- 3. Mean-semivariance model -- 3.1. THE MEAN·SEMIVARIANCE PROBLEM -- 3.2. REFORMULATION OF THE PROBLEM -- 4. Implementation of the Critical Line Algorithm -- 5. Performance -- References -- Data Mining Corrections -- THE MODELS -- ESTIMATION OF ~ FOR MODEL I -- ESTIMATION OF P FOR MODEL n -- TESTS OF SIGNIFICANCE -- ESTIMATION FOR MODEL m -- A BAYESIAN VIEW OF THE METHODS -- EXPERIENCE WITH MODELS I, II, AND Ill -- WHY NO HOIDOUT PERIOD? -- SUMMARY -- ENDNOTES -- REFERENCES -- Comments -- References -- Chapter 7 Harry Markowitz Company -- Comments -- References -- The Likelihood of Various Stock Market Return Distributions: Part 1: Principles of Inference -- 1. Financial research supports financial decision making: An example -- 2. Rational (coherent) decision making is Bayesian -- 3. Classical statistics is an unreliable indicator of how Bayesians should shift beliefs -- 4. Remote Bayesian clients -- 5. Human approximation to an RDM -- 6. Summary -- Acknowledgments -- Notes -- References.

The Likelihood of Various Stock Market Return Distributions: Part 2: Empirical Results.