Harry M. Markowitz

The Journal of FinanceVolume 7, Issue 1 p. 77-91 Original Article PORTFOLIO SELECTION* Harry Markowitz, Harry Markowitz The Rand CorporationSearch for more papers by this author Harry Markowitz, Harry Markowitz The Rand CorporationSearch for more papers by this author First published: March 1952 https://doi.org/10.1111/j.1540-6261.1952.tb01525.xCitations: 4,062 †This paper is based on work done by the author while at the Cowles Commission for Research in Economics and with the financial assistance of the Social Science Research Council. It will be reprinted as Cowles Commission Paper, New Series, No. 60. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat Citing Literature Volume7, Issue1March 1952Pages 77-91 RelatedInformation

Embracing finance, economics, operations research, and computers, this book applies modern techniques of analysis and computation to find combinations of securities that best meet the needs of private or institutional investors.

Previous articleNext article No AccessThe Utility of WealthHarry MarkowitzHarry Markowitz Search for more articles by this author PDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by Journal of Political Economy Volume 60, Number 2Apr., 1952 Article DOIhttps://doi.org/10.1086/257177 Views: 377Total views on this site Citations: 906Citations are reported from Crossref Copyright 1952 The University of Chicago PressPDF download Crossref reports the following articles citing this article:Mayank Chadha, Mukesh K. Ramancha, Manuel A. Vega, Joel P. Conte, Michael D. Todd The modeling of risk perception in the use of structural health monitoring information for optimal maintenance decisions, Reliability Engineering & System Safety 229 (Jan 2023): 108845.https://doi.org/10.1016/j.ress.2022.108845Mohammad Sahabuddin, Md. Farjin Hassan, Mosab I. Tabash, Mohammad Ahmad Al-Omari, Md. 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The Journal of FinanceVolume 46, Issue 2 p. 469-477 Nobel Lecture Foundations of Portfolio Theory HARRY M. MARKOWITZ, HARRY M. MARKOWITZMarvin Speiser Distinguished Professor of Finance and Economics, Baruch College, CUNY and Director of Research, DAIWA Security Trust Company. © The Nobel Foundation 1990Search for more papers by this author HARRY M. MARKOWITZ, HARRY M. MARKOWITZMarvin Speiser Distinguished Professor of Finance and Economics, Baruch College, CUNY and Director of Research, DAIWA Security Trust Company. © The Nobel Foundation 1990Search for more papers by this author First published: June 1991 https://doi.org/10.1111/j.1540-6261.1991.tb02669.xCitations: 394 Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Citing Literature Volume46, Issue2June 1991Pages 469-477 RelatedInformation

It is common for matrices in industrial applications of linear programming to have a large proportion of zero coefficients. While every item (raw material, intermediate material, end item, equipment item) in, say, a petroleum refinery may be indirectly related to every other, any particular process uses few of these. Thus the matrix describing petroleum technology has a small percentage of non-zeros. If spacial or temporal distinctions are introduced into the model the percentage of non-zeros generally falls further. The present paper discusses a form of inverse which is especially convenient to obtain and use for matrices with a high percentage of zeros. The application of this form of inverse in linear programming is also discussed.

Naval Research Logistics QuarterlyVolume 3, Issue 1-2 p. 111-133 Article The optimization of a quadratic function subject to linear constraints Harry Markowitz, Harry Markowitz The writer has particularly benefited from discussions with Kenneth Arrow on the subject matter of this paper. The author discusses a computational technique applicable to the determination of the set of “efficient points” for quadratic programming problems.Search for more papers by this author Harry Markowitz, Harry Markowitz The writer has particularly benefited from discussions with Kenneth Arrow on the subject matter of this paper. The author discusses a computational technique applicable to the determination of the set of “efficient points” for quadratic programming problems.Search for more papers by this author First published: March ‐ June 1956 https://doi.org/10.1002/nav.3800030110Citations: 278 The writer has particularly benefited from discussions with Kenneth Arrow on the subject matter of this paper. The author discusses a computational technique applicable to the determination of the set of “efficient points” for quadratic programming problems. AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Citing Literature Volume3, Issue1-2March ‐ June 1956Pages 111-133 RelatedInformation

ABSTRACT Levy and Markowitz showed, for various utility functions and empirical returns distributions, that the expected utility maximizer could typically do very well if he acted knowing only the mean and variance of each distribution. Levy and Markowitz considered only situations in which the expected utility maximizer chose among a finite number of alternate probability distributions. The present paper examines the same questions for a case with an infinite number of alternate distributions, namely those available from the standard portfolio constraint set.

K. J. Arrow has described five apparently reasonable properties which any voting system or other "social welfare function" should have. He has demostrated mathematically that none could possibly have all these properties. One of his requirements is questionable, but if it is modified many "voting systems" become acceptable.

It is often asserted that the application of mean–variance analysis assumes normal (Gaussian) return distributions or quadratic utility functions. This common mistake confuses sufficient versus necessary conditions for the applicability of modern portfolio theory. If one believes (as does the author) that choice should be guided by the expected utility maxim, then the necessary and sufficient condition for the practical use of mean–variance analysis is that a careful choice from a mean–variance efficient frontier will approximately maximize expected utility for a wide variety of concave (risk-averse) utility functions. This paper reviews a half-century of research on mean–variance approximations to expected utility. The many studies in this field have been generally supportive of mean–variance analysis, subject to certain (initially unanticipated) caveats.

Abstract : This paper considers optimization problems in which some or all variables must take on integral values. An ability to solve such problems would be valuable in itself and would also allow handling certain kinds of heretofore intractable 'economies of scale'. An automatic algorithm for solving such problems is not given. A general approach susceptible of individual variations, depending upon the problem and the judgment of the user is presented. Two moderate-size examples are presented to illustrate the method. (Author)

Portfolio theory and practice are surveyed from Shakespeare to Sharpe—almost.

Fifty years have passed since the publication of Harry Markowitz9s article on portfolio selection, setting forth the ground-breaking concepts that have come to form the foundation of what is now popularly referred to as Modern Portfolio Theory (MPT). In this article the authors briefly explain the theory underlying MPT and using illustrations highlight the application of MPT to the current practice of asset management and portfolio construction. The authors also survey most of the relevant research that has directly or indirectly been either an outcome of MPT or has contributed to the implementation of MPT.

Abstract We integrate appealing features of Markowitz’s mean-variance portfolio theory (MVT) and Shefrin and Statman’s behavioral portfolio theory (BPT) into a new mental accounting (MA) framework. Features of the MA framework include an MA structure of portfolios, a definition of risk as the probability of failing to reach the threshold level in each mental account, and attitudes toward risk that vary by account. We demonstrate a mathematical equivalence between MVT, MA, and risk management using value at risk (VaR). The aggregate allocation across MA subportfolios is mean-variance efficient with short selling. Short-selling constraints on mental accounts impose very minor reductions in certainty equivalents, only if binding for the aggregate portfolio, offsetting utility losses from errors in specifying risk-aversion coefficients in MVT applications. These generalizations of MVT and BPT via a unified MA framework result in a fruitful connection between investor consumption goals and portfolio production.

The Journal of FinanceVolume 31, Issue 5 p. 1273-1286 Article INVESTMENT FOR THE LONG RUN: NEW EVIDENCE FOR AN OLD RULE Correction(s) for this article ERRATA Volume 32Issue 3The Journal of Finance pages: 973-973 First Published online: January 29, 2016 Harry M. Markowitz, Harry M. Markowitz IBM Thomas J. Watson Research Center, Yorktown Heights, New York.Search for more papers by this author Harry M. Markowitz, Harry M. Markowitz IBM Thomas J. Watson Research Center, Yorktown Heights, New York.Search for more papers by this author First published: December 1976 https://doi.org/10.1111/j.1540-6261.1976.tb03213.xCitations: 93 Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Citing Literature Volume31, Issue5December 1976Pages 1273-1286 RelatedInformation

Abstract : A user's and programmer's manual for SIMSCRIPT II that requires only a basic knowledge of computers and programming language translators (compilers). Sections that are unusually difficult or contain features of limited use are marked with an asterisk. The manual is divided into five chapters, corresponding to five language 'levels.' Level 1 is a teaching language designed to introduce programming concepts to nonprogrammers. Level 2 is a language roughly comparable in power to FORTRAN, but departs from it in specific features. Level 3 is comparable in power to ALGOL or PL/I, but with specific differences, and contains information on the new ALPHA mode for alpha-numeric manipulations, on writing formatted reports, and on internal editing. Level 4 contains the entity-attribute-set features of SIMSCRIPT, which have been up- dated and augmented to provide a more powerful list-processing capability. Level 5, the simulation-oriented part of SIMSCRIPT II, contains statements for time advance, event and activity processing, generation of statistical variates, and accumulation and analysis of simulation-generated data. Two new debugging routines, BEFORE and AFTER, enable the monitoring of six complex processes.

A description of SIMSCRIPT, a general programming system specially adapted to the problems of writing simulation programs. The advantages of SIMSCRIPT are that it reduces the time needed to program simulations of even moderate complexity and provides increased flexibility in modifying such models in accordance with the findings of preliminary analysis and other circumstances. Although SIMSCRIPT may be used as a computer language for non-simulation problems, the author emphasizes its application to simulation. Detailed instructions and forms for applying SIMSCRIPT are included.

This essay summarizes my views on (a) the foundations of portfolio theory and its applications to current issues, such as the choice of criteria for practical risk-return analysis, and whether some form of risk-return analysis should be used in fact; (b) hypotheses about actual financial behavior, as opposed to idealized rational behavior, including two proofs of the fact that expected-utility maximizers would never prefer a multiple-prize lottery to all single-prize lotteries, as asserted in one of my 1952 papers; and (c) a simple proof of the theorem (which was initially greeted with some skepticism, especially by referees) that investors in capital asset pricing models do not get paid for bearing risk.

AbstractWith the aid of some simplifying assumptions, the capital asset pricing model comes to dramatic conclusions about practical matters, such as how to choose an investment portfolio and how to value financial assets. As illustrated in this article, when one particular, clearly unrealistic CAPM assumption is replaced by a more real-world version, some of the dramatic, practical conclusions of CAPM no longer follow. This result has implications for financial practice, research, and pedagogy. The capital asset pricing model is an elegant theory. With the aid of some simplifying assumptions, the CAPM comes to dramatic conclusions about such practical matters as how to choose an investment portfolio, how to forecast expected returns, and how to value financial assets with suitable adjustments for risk. These practical implications of the CAPM follow from two basic CAPM conclusions: (1) that the market portfolio—that is, a portfolio that holds securities in proportion to their market capitalization—is an efficient portfolio and (2) that an asset's expected return has a simple (linear) relationship to its beta.One of the simplifying assumptions of the original CAPM is that any investor can borrow without limit at the risk-free rate. As this article illustrates, the two basic CAPM conclusions no longer follow if this assumption of unlimited borrowing is replaced with that of limited or no borrowing. When borrowing is restricted, the market portfolio can be quite inefficient. Also, expected returns are not linearly related to betas.An alternate CAPM replaces the assumption of unlimited borrowing with the assumption that the investor can sell short and use the proceeds of the sale to buy long positions, without limit. In effect, it assumes that the investor can deposit $1,000 with a broker, short $1,000,000 worth of Security A, and use the proceeds of the short plus the deposit to buy $1,001,000 worth of Security B. This assumption is no more realistic than the assumption of unlimited borrowing.This alternate CAPM, like the original CAPM, implies that the market portfolio is an efficient portfolio and that expected returns are linearly related to betas. As I explain in this article, if the assumption that short proceeds can be used to buy long positions is replaced by a more real-world description of what is permitted in long-short portfolios, again, the two basic CAPM conclusions do not follow. In other words, the market portfolio may be substantially inefficient and expected returns are not linearly related to betas. These market inefficiencies would not be arbitraged away if some investors could borrow without limit, or short and use the proceeds to buy long positions without limit, while other investors could not.Thus, some of the "well-known" "conclusions" of "modern financial theory" disappear when the other assumptions of the theory are combined with more realistic descriptions of investor constraints. In particular, commonly used rules for risk adjustment and asset valuation are called into question.

Stock selection models often use analysts’ expectations, momentum, and fundamental data. We find support for composite modeling using these sources of data for global stocks during the period 1997–2011. We also find evidence to support the use of SunGard APT and Axioma multi-factor models for portfolio construction and risk control. Three levels of testing for stock selection and portfolio construction models are developed and estimated. We create portfolios for January 1997–December 2011. We report three conclusions: (1) analysts’ forecast information was rewarded by the global market between January 1997 and December 2011; (2) analysts’ forecasts can be combined with reported fundamental data, such as earnings, book value, cash flow and sales, and also with momentum, in a stock selection model for identifying mispriced securities; and (3) the portfolio returns of the multi-factor risk-controlled portfolios allow us to reject the null hypothesis for the data mining corrections test. The earnings forecasting variable dominates our composite model in terms of its impact on stock selection.

Machine learning offers a set of powerful tools that holds considerable promise for investment management. As with most quantitative applications in finance, the danger of misapplying these techniques can lead to disappointment. One crucial limitation involves data availability. Many of machine learning’s early successes originated in the physical and biological sciences, in which truly vast amounts of data are available. Machine learning applications often require far more data than are available in finance, which is of particular concern in longer-horizon investing. Hence, choosing the right applications before applying the tools is important. In addition, capital markets reflect the actions of people, who may be influenced by the actions of others and by the findings of past research. In many ways, the challenges that affect machine learning are merely a continuation of the long-standing issues researchers have always faced in quantitative finance. Although investors need to be cautious—indeed, more cautious than in past applications of quantitative methods—these new tools offer many potential applications in finance. In this article, the authors develop a research protocol that pertains both to the application of machine learning techniques and to quantitative finance in general. <b>TOPICS:</b>Big data/machine learning, portfolio theory

This paper presents fast algorithms for calculating mean-variance efficient frontiers when the investor can sell securities short as well as buy long, and when a factor and/or scenario model of covariance is assumed. Currently, fast algorithms for factor, scenario, or mixed (factor and scenario) models exist, but (except for a special case of the results reported here) apply only to portfolios of long positions. Factor and scenario models are used widely in applied portfolio analysis, and short sales have been used increasingly as part of large institutional portfolios. Generally, the critical line algorithm (CLA) traces out mean-variance efficient sets when the investor’s choice is subject to any system of linear equality or inequality constraints. Versions of CLA that take advantage of factor and/or scenario models of covariance gain speed by greatly simplifying the equations for segments of the efficient set. These same algorithms can be used, unchanged, for the long-short portfolio selection problem provided a certain condition on the constraint set holds. This condition usually holds in practice.

Prize Lecture to the memory of Alfred Nobel, December 7, 1990.(This abstract was borrowed from another version of this item.)

ABSTRACT Recently there has been a growing interest in the scenario model of covariance as an alternative to the one‐factor or many‐factor models. We show how the covariance matrix resulting from the scenario model can easily be made diagonal by adding new variables linearly related to the amounts invested; note the meanings of these new variables; note how portfolio variance divides itself into “within scenario” and “between scenario” variances; and extend the results to models in which scenarios and factors both appear where factor distributions and effects may or may not be scenario sensitive.

About the Editors. Contributing Authors. Foreword. PART ONE: Instruments, Asset Allocation, Portfolio Selection, and Asset Pricing. CHAPTER 1: Overview of Investment Management (Frank J. Fabozzi and Harry M. Markowitz). Setting Investment Objectives. Establishing an Investment Policy. Selecting a Portfolio Strategy. Constructing the Portfolio. Measuring and Evaluating Performance. Key Points. CHAPTER 2: Asset Classes, Alternative Investments, Investment Companies, and Exchange-Traded Funds (Mark J. P. Anson, Frank J. Fabozzi, and Frank J. Jones). Asset Classes. Overview of Alternative Asset Products. Investment Companies. Exchange-Traded Funds. Mutual Funds vs. ETFs: Relative Advantages. Key Points. Questions. CHAPTER 3: Portfolio Selection (Frank J. Fabozzi, Harry M. Markowitz, Petter N. Kolm, and Francis Gupta). Some Basic Concepts. Measuring a Portfolio's Expected Return. Measuring Portfolio Risk. Portfolio Diversification. Choosing a Portfolio of Risky Assets. Issues in Portfolio Selection. Key Points. Questions. CHAPTER 4: Capital Asset Pricing Models (Frank J. Fabozzi and Harry M. Markowitz). Sharpe-Lintner CAPM. Roy CAPM. Confusions Regarding the CAPM. Meanings of Market Efficiency. CAPM Investors Do Not Get Paid for Bearing Risk. The Two Beta Trap. Key Points. Questions. CHAPTER 5: Factor Models (Guofu Zhou and Frank J. Fabozzi). Arbitrage Pricing Theory. Types of Factor Models. Factor Model Estimation. Keypoints. Appendix: Principal Component Analysis in Finance. Questions. CHAPTER 6: Modeling Asset Price Dynamics (Dessislava A. Pachamanova and Frank J. Fabozzi). Financial Time Series. Binomial Trees. Arithmetic Random Walks. Geometric Random Walks. Mean Reversion. Advanced Random Walk Models. Stochastic Processes. Key Points. Questions. CHAPTER 7: Asset Allocation and Portfolio Construction (Noel Amenc, Felix Goltz, Lionel Martellini, and Vincent Milhau). Asset Allocation and Portfolio Construction Decisions in the Optimal Design of the Performance-Seeking Portfolio. Asset Allocation and Portfolio Construction Decisions in the Optimal Design of the Liability-Hedging Portfolio. Dynamic Allocation Decisions to the Performance-Seeking and Liability-Hedging Portfolios. Key Points. Appendix. Questions. PART TWO: Equity Analysis and Portfolio Management. CHAPTER 8: Fundamentals of Common Stock (Frank J. Fabozzi, Frank J. Jones, Robert R. Johnson, and Pamela P. Drake). Earnings. Dividends. The U.S. Equity Markets. Trading Mechanics. Trading Costs. Stock Market Indicators. Key Points. Questions. CHAPTER 9: Common Stock Portfolio Management Strategies (Frank J. Fabozzi, James L. Grant, and Raman Vardharaj). Integrating the Equity Portfolio Management Process. Capital Market Price Efficiency. Tracking Error and Related Measures. Active vs. Passive Portfolio Management. Equity Style Management. Passive Strategies. Active Investing. Performance Evaluation. Key Points. Questions. CHAPTER 10: Approaches to Common Stock Valuation (Pamela P. Drake, Frank J. Fabozzi, and Glen A. Larsen Jr.). Discounted Cash Flow Models. Relative Valuation Methods. Key Points. Questions. CHAPTER 11: Quantitative Equity Portfolio Management (Andrew Alford, Robert Jones, and Terence Lim). Traditional and Quantitative Approaches to Equity Portfolio Management. Forecasting Stock Returns, Risks, and Transaction Costs. Constructing Portfolios. Trading. Evaluating Results and Updating the Process. Key Points. Questions. CHAPTER 12: Long-Short Equity Portfolios (Bruce I. Jacobs and Kenneth N. Levy). Constructing a Market-Neutral Portfolio. The Importance of Integrated Optimization. Adding Back a Market Return. Some Concerns Addressed. Evaluating Long-Short. Key Points. Questions. CHAPTER 13: Multifactor Equity Risk Models (Frank J. Fabozzi, Raman Vardharaj, and Frank J. Jones). Model Description and Estimation. Risk Decomposition. Applications in Portfolio Construction and Risk Control. Key Points. Questions. CHAPTER 14: Fundamentals of Equity Derivatives (Bruce M. Collins and Frank J. Fabozzi). The Role of Derivatives. Listed Equity Options. Futures Contracts. Pricing Stock Index Futures. OTC Equity Derivatives. Structured Products. Key Points. Questions. CHAPTER 15: Using Equity Derivatives in Portfolio Management (Bruce M. Collins and Frank J. Fabozzi). Equity Investment Management. Portfolio Applications of Listed Options. Portfolio Applications of Stock Index Futures. Applications of OTC Equity Derivatives. Risk and Expected Return of Option Strategies. Key Points. Questions. PART THREE: Bond Analysis and Portfolio Management. CHAPTER 16: Bonds, Asset-Backed Securities, and Mortgage-Backed Securities (Frank J. Fabozzi). General Features of Bonds. U.S. Treasury Securities. Federal Agency Securities. Corporate Bonds. Municipal Securities. Asset-Backed Securities. Residential Mortgage-Backed Securities. Commercial Mortgage-Backed Securities. Key Points. Questions. CHAPTER 17: Bond Analytics (Frank J. Fabozzi). Basic Valuation of Option-Free Bonds. Conventional Yield Measures. Total Return. Measuring Interest Rate Risk. Key Points. Questions. CHAPTER 18: Bond Analytics (Frank J. Fabozzi and Steven V. Mann). Arbitrage-Free Bond Valuation. Yield Spread Measures. Forward Rates. Overview of the Valuation of Bonds with Embedded Options. Lattice Model. Valuation of MBS and ABS. Key Points. Questions. CHAPTER 19: Bond Portfolio Strategies for Outperforming a Benchmark (Bulent Baygun and Robert Tzucker). Selecting the Benchmark Index. Creating a Custom Index. Beating the Benchmark Index. Key Points. Questions. CHAPTER 20: The Art of Fixed Income Portfolio Investing (Chris P. Dialynas and Ellen J. Rachlin). The Global Fixed Income Portfolio Manager. The Global Challenge. Portfolio Parameters. Regulatory Changes, Demographic Trends, and Institutional Bias. Information in the Markets. Duration and Yield Curve. Volatility. International Corporate Bonds. International Investing and Political Externalities. Foreign Investment Selection. Currency Selection. Key Points. Questions. CHAPTER 21: Multifactor Fixed Income Risk Models and Their Applications (Anthony Lazanas, Antonio Baldaque da Silva, Radu Gabudean, and Arne D. Staal). Approaches Used to Analyze Risk. Applications of Risk Modeling. Key Points. Questions. CHAPTER 22: Interest Rate Derivatives and Risk Control (Frank J. Fabozzi). Interest Rate Futures and Forward Contracts. Interest Rate Swaps. Interest Rate Options. Interest Rate Agreements (Caps and Floors). Key Points. Questions. CHAPTER 23: Credit Default Swaps and the Indexes (Stephen J. Antczak, Douglas J. Lucas, and Frank J. Fabozzi). What Are Credit Default Swaps? Credit Default Swaps Indexes. Key Points. Questions. About the Web Site. Index.

Comparing Japanese and U.S. securities market, the paper shows that survivor bias affecting quantitative analysis is relatively minor in Japan and substantial in the U.S. The realized average returns vs. standard derivation curve for the two countries is also quite different. But the paper suggests that the constraint levels and estimation procedures that did best in both countries in the past will do well in the future.

In a mean—variance portfolio analysis (Markowitz, 1959) an n-component vector (portfolio) X is called feasible if it satisfies where A is an m x n matrix of constraint coefficients, and b an m-component constant vector. An EV combination is called feasible if for some feasible portfolio. Here E is the expected return of the portfolio, V the variance of the portfolio, μ the vector of expected returns on securities, and C a positive semidefinite covariance matrix of returns among securities.KeywordsPortfolio SelectionExcess ReturnCapital Asset Price ModelPortfolio AnalysisEfficient PortfolioThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

A basic cause of the current financial crisis was the mandate from the U.S. Congress to the Federal National Mortgage Association (Fannie Mae) to increase its support of low-income housing. This mandate led to a lowering of lending standards, which encouraged home buyers to spend beyond their means. The problem was aggravated by novel, obscure, highly leveraged financial instruments that were not well understood by either lenders or borrowers. At least two steps are required for the solution to this crisis: (1) Congress should instruct Fannie Mae that the safety of the financial system must take priority over the objective of providing low-income housing, and (2) this article’s “modest” proposal for bringing transparency to the tangle of financial instruments should be implemented.

Naval Research Logistics QuarterlyVolume 10, Issue 1 p. 375-379 News and Memoranda A note on shortest path, assignment, and transportation problems A. J. Hoffman, A. J. Hoffman IBM Corporation, Thomas J. Watson Research Center Yorktown Heights, New YorkSearch for more papers by this authorH. M. Markowitz, H. M. Markowitz The RAND Corporation Santa Monica, CaliforniaSearch for more papers by this author A. J. Hoffman, A. J. Hoffman IBM Corporation, Thomas J. Watson Research Center Yorktown Heights, New YorkSearch for more papers by this authorH. M. Markowitz, H. M. Markowitz The RAND Corporation Santa Monica, CaliforniaSearch for more papers by this author First published: March 1963 https://doi.org/10.1002/nav.3800100132Citations: 25AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat References 1 Ford, L. R., Jr. and Fulkerson, D. R., Flows in Networks (Princeton University Press, Princeton, N. J., 1962). 10.1515/9781400875184 Google Scholar Citing Literature Volume10, Issue1March 1963Pages 375-379 ReferencesRelatedInformation

The following sections are included:BACKGROUNDTHE SEQUENCE OF GAMESALTERNATE SEQUENCE-OF-GAMES FORMALIZATIONSUNENDING GAMESCONCLUSIONSAPPENDIXREFERENCES

When they want to see how complex systems work, scientists often turn to asynchronous-time simulation models, which allow processes to change sporadically over time, typically at irregular intervals. While rarely used in finance today, such models may turn out to be valuable tools for understanding how markets respond to changes in the participation rates of different types of investors, for example, or to changes in regulatory or investment policies. The asynchronous, discrete-event, stock market simulator described here allows users to create a model of the market, using their own inputs. Users can vary the numbers of investors, traders, portfolio analysts, and securities, as well as their own investing and trading decision rules. Such a simulation may be able to provide a more realistic picture of complex markets.

The Distribution System Simulator (DSS) is a software system designed to overcome the difficulties inherent in the construction and use of simulation models for large-scale, physical distribution systems. The central difficulties associated with the simulation of a distribution system are: (1) defining a suitable model, (2) programming the model for a computer, (3) obtaining appropriately indicative output reports from the distribution simulation, (4) ordering the model repetitively to respond to the implications of the output reports so that the practical consequences of theoretical changes can be determined. The Distribution Systems Simulator provides a means for accomplishing these ends without programming effort on the part of the user. The Distribution System Simulator is a modelling tool which produces a mathematical representation of a firm's distribution system. The user of DSS responds to a questionnaire which contains the options that he can use to develop a model of his distribution system. The user specifies the characteristics of the desired model by answering true or false to questions expressed in English. The options allow the analyst to take into account each of the major factors involved in the operation of a distribution system: the characteristics of customers' demand for products, buying patterns of customers, order filling policies, replenishment policies, emergency replenishment policies, redistribution policies, transportation policies, distribution channels, factory locations, production capabilities, and other significant elements. These options are essentially inventory and product movement oriented-beyond this, DSS provides the capability, through user functions, to incorporate other vehicle scheduling algorithms, forecasting techniques, production schedules, and pricing mechanisms which are outside the scope of the options. Since DSS emulates the essential parts of the actual distribution system, it permits the distribution system to be modelled in such a way that a “total system approach” can be taken to the problem by OR personnel as well as by executives. DSS is more than a distribution system model generator. It also generates the computer program for running the model on a computer. As indicated above, the user of DSS specifies the characteristics of the desired model by answering a questionnaire consisting of a set of true or false questions. These answers are submitted to a computer, along with the DSS program. The result is: (1) the generation of a PL/I program whose logic is described by the options chosen on the questionnaire, (2) a complete specification of the data required by the simulation program generated, and (3) a complete specification of the information required for the output analysis available for the simulation generated. The user need not be familiar with any elements of computer programming in order to obtain the simulator, instructions for its use, and output analyses; however, the requirements of a thorough understanding of distribution systems, modelling, and management science are imposed on the user.

How can investors construct portfolios that help them reach their goals, such as a comfortable retirement for themselves, a bequest for their children, and college education for their grandchildren? Are such portfolios on the mean–variance efficient frontier? How can advisors discuss investment risk with clients whose risk tolerance varies by goal—low for retirement and higher for bequests? These are the questions answered in this article. The authors show that advisors can help clients construct portfolios that take clients to their goals while also leaving them on the mean–variance efficient frontier. <b>TOPICS:</b>Portfolio construction, performance measurement

This paper uses two databases to test the ability of six functions of arithmetic mean and variance to approximate geometric mean return or, equivalently, Bernoulli's expected log utility. The two databases are: (1) a database of returns on frequently used asset classes, and (2) that of real returns on the equity markets of sixteen countries, 1900–2000. Three of the functions of arithmetic mean and variance do quite well, even for return series with large losses. The other three do less well.

In a recent paper ( Jin, Yan, and Zhou 2005 ), it is proved that efficient strategies of the continuous‐time mean–semivariance portfolio selection model are in general never achieved save for a trivial case. In this note, we show that the mean–semivariance efficient strategies in a single period are always attained irrespective of the market condition or the security return distribution. Further, for the below‐target semivariance model the attainability is established under the arbitrage‐free condition. Finally, we extend the results to problems with general downside risk measures.

The experiment reported here compares two methods for handling uncertain inputs to a mean-variance analysis. Specifically, it compares Michaud's resampled frontier versus Bayesian inference with diffuse prior. A simulated referee generates ten truths about 8 asset classes. For each truth it randomly generates one hundred histories. A simulated Bayes Player and Player process each history according to their respective methodologies, seeking portfolios to maximize given expected utility functions. Players are scored according to the actual utility achieved and their own estimates of this utility. The authors were surprised to find that, on average, the Michaud player won.

The price of a stock may differ from its fundamental value by a random noise. In this case, small-capitalization and value stocks are more likely to have negative noise, while large-capitalization and growth stocks are likely to have positive noise. Negative price noise implies that small-capitalization and value stocks are more likely undervalued and thus have higher expected return than justified by risk, while the large-capitalization and growth stocks are more likely overvalued. We formally verify and explore this intuition by using a standard noise-in-price model.

Stock selection models often use analysts’ expectations, momentum, and fundamental data. The authors found support for composite modeling using these sources of data for global stocks from 1997 through 2011. They found additional evidence to support the use of SunGard APT multifactor models for portfolio construction and risk control. Three levels of testing of stock selection and portfolio construction models are developed and estimated. They create portfolios for January 1997 through December 2011. They report three conclusions: 1) Analysts’ forecast information has been rewarded by the global market from January 1997 through December 2011; 2) analysts’ forecasts can be combined with reported fundamental data, such as earnings, book value, cash flow, and sales, and momentum, in a stock selection model to identify mispriced securities; and 3) the portfolio returns of the multifactor risk-controlled portfolios allow the rejection of the null hypothesis for the data-mining corrections test. <b>TOPICS:</b>Fundamental equity analysis, portfolio construction

Chief executive officers and chief financial officers seek to implement corporate financial decisions that maximize the stock price and stockholder wealth. Real assets may be tangible, such as machinery or factories, or intangible, such as technical expertise or patents. These real assets must be paid for. To finance the real assets, the financial manager makes decisions regarding the magnitude of common stock issuance and stock repurchases, long-term debt issuance, and debt repurchases. Cash flows are generated by real assets, and the stockholders receive dividend payments based on the net income generated by the corporation. Empirical evidence shows that the dividend payment decision and stock and debt repurchase decisions can increase stock prices and returns. A variable, denoted as corporate exports—which incorporates the amount of dividends, net stock issuances, and net debt repurchases—is used as an expected return to create an initial efficient frontier. A stock selection model is used as a constraint in the portfolio process in conjunction with the corporate exports variable to further increase returns. We use a data mining corrections test for establishing the statistical significance of our portfolio returns.

Financial anomalies have been studied in the United States. Recent evidence suggests that financial anomalies have diminished in the United States and possibly in non-US portfolios. Have the anomalies changed, or are they persistent? Have historical and earnings forecasting data been a consistent, and highly statistically significant, source of excess returns? The authors test many financial anomalies of the 1980s and 1990s and report that several models and strategies continue to produce statistically significant excess returns. The authors test a large set in US and non-US markets over the past 25 years. They report that many of these fundamentals, earnings forecasts, revisions, and breadth and momentum strategies maintained their statistical significance during the 1996–2020 time period. Moreover, the earnings forecasting model and robust regression estimated composite model excess returns are greater in non-US and global markets than in US markets. <b>TOPICS:</b>Security analysis and valuation, emerging, performance measurement <b>Key Findings</b> ▪ The authors verify the continuity of financial anomalies in the post-publication period. ▪ The authors use composite modeling methodology to estimate expected returns. ▪ The authors use robust regression to address the outliers and issues in the data.

In a mean–variance portfolio analysis (Markowitz 1959) an n-component vector (portfolio) X is called feasible if it satisfies $$ AX=b\kern2em X\ge O $$ where A is an m × n matrix of constraint coefficients, and b an m-component constant vector. An EV combination is called feasible if $$ \begin{array}{c}E={\mu}^TX\\ {}V={X}^TCX\end{array} $$ for some feasible portfolio. Here E is the expected return of the portfolio, V the variance of the portfolio, μ the vector of expected returns of securities, and C a positive semidefinite covariance matrix of returns among securities.

Harry M Markowitz received the Nobel Prize in Economics in 1990 for his pioneering work in portfolio theory. He also received the von Neumann Prize from the Institute of Management Science and the Operations Research Institute of America in 1989 for his work in portfolio theory, sparse matrices and the SIMSCRIPT computer language. While Dr Markowitz is well-known for his work on portfolio theory, his work on sparse matrices remains an essential part of linear optimization calculations. In addition, he designed and developed SIMSCRIPT ??? a computer programming language. SIMSCRIPT has been widely used for simulations of systems such as air transportation and communication networks. This book consists of a collection of Dr Markowitz's most important works in these three fields.

Abstract : Programming by Questionnaire, or the Program Generator technique, is a method by which many computer programs can be produced at considerable savings in time, effort, and cost. A Program Generator consists of four parts: (1) A Questionnaire, written in English, defining the scope and logic of all of the programs that can be generated. (2) A Statement List containing all the computer commands needed to construct any of the many programs. (3) A set of Decision Tables specifying the commands required from the Statement List as a function of the Questionnaire choices. (4) The Editor program for processing the Questionnaire, Statement List, and Decision Tables, thus building the desired program and providing the user with a list of the data he must supply to use the program. (Author)

The production of a digital simulator program, or of any program for that matter, involves two steps: creating the model, then writing the program. Fundamentally, the writing of the program is a technical detail which must of necessity wait upon the creation of the model. Nevertheless, the nature of the machinery available for producing simulation programs is bound to exercise an influence on the nature of the model. This is true because effective modeling requires abstraction of the essence of the system under investigation, the direction taken in the abstraction being determined by the goals of the investigation. For complex systems it is very often not clear which of many possible abstractions is most valid for the purposes at hand.

The purpose of this article is to introduce the Gerber statistic, a robust co-movement measure for covariance matrix estimation for the purpose of portfolio construction. The Gerber statistic extends Kendall’s Tau by counting the proportion of simultaneous co-movements in series when their amplitudes exceed data-dependent thresholds. Because the statistic is not affected by extremely large or extremely small movements, it is especially well suited for financial time series, which often exhibit extreme movements and a great amount of noise. Operating within the mean–variance portfolio optimization framework of Markowitz, we consider the performance of the Gerber statistic against two other commonly used methods for estimating the covariance matrix of stock returns: the sample covariance matrix (also called the <i>historical covariance matrix</i>) and shrinkage of the sample covariance matrix given by Ledoit and Wolf. Using a well-diversified portfolio of nine assets over a 30-year period (January 1990–December 2020), we find, empirically, that for almost all investment scenarios considered, the Gerber statistic’s returns dominate those achieved by both historical covariance and by the shrinkage method of Ledoit and Wolf.

The <i>trkC</i> locus encodes several receptors for neurotrophin-3, including the well studied full-length tyrosine kinase isoform, in addition to receptor isoforms lacking the kinase active domain. TrkC receptors are widely expressed throughout mouse development in many different organs. To investigate the function of truncated receptors <i>in vivo</i> and to identify cell types that are biologically responsive to this gene product, we have overexpressed a physiological truncated trkC isoform in the mouse. Mice overexpressing this receptor develop to term but die in the first postnatal days. High levels of transgene expression result in severe developmental defects in the peripheral nervous system and in the heart. The severity of neuronal losses observed in these animals suggests that truncated receptors may act by sequestering neurotrophin, thus, closely relating this mouse model to the neurotrophin-3-deficient one. Lower levels of exogenous truncated receptor in transgenic mice result in a more modest phenotype and, in some neuronal populations, do not cause neural deficits. Taken together, these data suggest that truncated trkC receptor isoforms may have modulatory functions in development.

This paper first describes the analytic approach that Markowitz used in developing his portfolio theory. Developing a game-of-life simulation is a parallel approach for modelling individual financial management. To develop a realistic simulator will require deciding what goals are essential to the family planning process, formulating optimizable subproblems, using technology to interpret and record decisions, and developing decision rules which prove robust in the model and can be implemented in practice.

In 1989 I was pleased and honored to be awarded the ORSA/TIMS (now INFORMS) John von Neumann Theory Prize for my work in portfolio theory, sparse matrices, and SIMSCRIPT. The following is a retrospective on my work in these fields.

Ideally, financial analysts would like to be able to optimize a consumption–investment game with many securities, many time periods, transaction costs, and changing probability distributions. We cannot. For a small optimizable version of such a game, we consider in this article how much would be lost by following one or another heuristic that could be easily scaled to handle large games. For the games considered, a particular mean–variance heuristic does almost as well as the optimum strategy.

The relation between the excess return of each security and its beta, where beta is defined as its regression against the return on the market portfolio, is linear in the Sharpe-Lintner capital asset pricing model. This linear relation is often interpreted to mean that CAPM investors are paid for bearing systematic risk. In fact, this is not a correct interpretation, because two securities may have different excess returns even though they have identical risk structures in terms of their covariances with other securities in the market. If the parameters of the CAPM are generated in a natural way, securities with the same risk structure almost surely will have different expected returns. <b>TOPICS:</b>Factor-based models, statistical methods, portfolio management/multi-asset allocation

If the price of a stock differs from its intrinsic value by a random noise, then value stocks are more likely to have negative noise; they are thus more likely undervalued and have higher expected return than justified by risk. The same intuition applies to small capitalization stocks. We formally verify and explore this intuition by using a standard noise-in-price model. This intuition is different from the Jensen’s inequality effect studied by Blume and Stambaugh [Blume ME, Stambaugh RF (1983) Biases in computed returns: An application to the size effect. J. Financial Econom. 12(3):387–404]. Our model is parsimonious: the value premium as well as size premium are computed in closed form and depend on only four parameters: mean of stock return, volatility of stock return, volatility of the price-to-dividend ratio, and noise volatility. We emphasize that only a moderate volatility of price noise is needed to generate the observed value premium. However, the model cannot generate the observed size premium. This paper was accepted by Wei Jiang, finance.

The global exploration of snakebites requires the use of quantitative omics approaches to characterize snake venom as it enters into the systemic circulation. These omics approaches give insights into the venom proteome, but a further exploration is warranted to analyze the venom-reactome for the identification of snake venom biomarkers. The recent discovery of extracellular vesicles (EVs), and their critical cellular functions, has presented them as intriguing sources for biomarker discovery and disease diagnosis. Herein, we purified EV's from the snake venom (svEVs) of Crotalus atrox and C. oreganus helleri, and from plasma of BALB/c mice injected with venom from each snake using EVtrap in conjunction with quantitative mass spectrometry for the proteomic identification and quantification of svEVs and plasma biomarkers. Snake venom EVs from C. atrox and C. o. helleri were highly enriched in 5' nucleosidase, L-amino acid oxidase, and metalloproteinases. In mouse plasma EVs, a bioinformatic analysis for revealed upregulated responses involved with cytochrome P450, lipid metabolism, acute phase inflammation immune, and heat shock responses, while downregulated proteins were associated with mitochondrial electron transport, NADH, TCA, cortical cytoskeleton, reticulum stress, and oxidative reduction. Altogether, this analysis will provide direct evidence for svEVs composition and observation of the physiological changes of an envenomated organism.

In 1940, in context of choosing optimum reinsurance levels, Bruno de Finetti essentially proposed mean-variance analysis with correlated risks. It was not until 1952 that Markowitz and Roy introduced mean-variance analysis with correlated risks into financial literature. De Finetti solved of computing mean-variance efficient frontiers for a particular constraint set (one that describes reinsurance problem) assuming uncorrelated risks. While he understood and explained importance of case with correlated risks, he did not provide an algorithm for this case. In fact, one of his conjectures concerning its solution was incorrect. The present article summarizes de Finetti's contribution, presents an algorithm for solving the de Finetti problem when risks are correlated, and illustrates these matters with an easily visualized two-policy reinsurance problem.

An asynchronous discrete-time model run in “dynamic mode” can model the effects on market prices of changes in strategies, leverage, and regulations, or the effects of different return estimation procedures and different trading rules. Run in “equilibrium mode,” it can be used to arrive at equilibrium expected returns.

Preface xiii About the Editors xxiii Contributing Authors xxv CHAPTER 1: An Introduction to Quantitative Equity Investing 1 Paul Bukowski CHAPTER 2: Equity Analysis Using Traditional and Value-Based Metrics 25 James L. Grant and Frank J. Fabozzi CHAPTER 3: A Franchise Factor Approach to Modeling P/E Orbits 71 Stanley Kogelman and Martin L. Leibowitz CHAPTER 4: Relative Valuation Methods for Equity Analysis 105 Glen A. Larsen Jr., Frank J. Fabozzi, and Chris Gowlland CHAPTER 5: Valuation over the Cycle and the Distribution of Returns 125 Anders Ersbak Bang Nielsen and Peter C. Oppenheimer CHAPTER 6: An Architecture for Equity Portfolio Management 147 Bruce I. Jacobs and Kenneth N. Levy CHAPTER 7: Equity Analysis in a Complex Market 171 Bruce I. Jacobs and Kenneth N. Levy CHAPTER 8: Survey Studies of the Use of Quantitative Equity Management 189 Frank J. Fabozzi, Sergio M. Focardi, and Caroline L. Jonas CHAPTER 9: Implementable Quantitative Equity Research 231 Frank J. Fabozzi, Sergio M. Focardi, and K. C. Ma CHAPTER 10: Tracking Error and Common Stock Portfolio Management 251 Raman Vardharaj, Frank J. Fabozzi, and Frank J. Jones CHAPTER 11: Factor-Based Equity Portfolio Construction and Analysis 265 Petter N. Kolm, Joseph A. Cerniglia, and Frank J. Fabozzi CHAPTER 12: Cross-Sectional Factor-Based Models and Trading Strategies 291 Joseph A. Cerniglia, Petter N. Kolm, and Frank J. Fabozzi CHAPTER 13: Multifactor Equity Risk Models and Their Applications 339 Anthony Lazanas, Antonio Baldaque da Silva, Arne D. Staal, and Cenk Ural CHAPTER 14: Dynamic Factor Approaches to Equity Portfolio Management 373 Dorsey D. Farr CHAPTER 15: A Factor Competition Approach to Stock Selection 397 Joseph Mezrich and Junbo Feng CHAPTER 16: Avoiding Unintended Country Bets in Global Equity Portfolios 413 Michele Aghassi, Cliff Asness, Oktay Kurbanov, and Lars N. Nielsen CHAPTER 17: Modeling Market Impact Costs 425 Petter N. Kolm and Frank J. Fabozzi CHAPTER 18: Equity Portfolio Selection in Practice 441 Dessislava A. Pachamanova and Frank J. Fabozzi CHAPTER 19: Portfolio Construction and Extreme Risk 483 Jennifer Bender, Jyh-Huei Lee, and Dan Stefek CHAPTER 20: Working with High-Frequency Data 497 Irene Aldridge CHAPTER 21: Statistical Arbitrage 521 Brian J. Jacobsen About the Website 535 Index 537

Machine learning offers a set of powerful tools that holds considerable promise for investment management. As with most quantitative applications in finance, the danger of misapplying these techniques can lead to disappointment. One crucial limitation involves data availability. Many of machine learning’s early successes originated in the physical and biological sciences, in which truly vast amounts of data are available. Machine learning applications often require far more data than are available in finance, which is of particular concern in longer-horizon investing. Hence, choosing the right applications before applying the tools is important. In addition, capital markets reflect the actions of people, which may be influenced by others’ actions and by the findings of past research. In many ways, the challenges that affect machine learning are merely a continuation of the long-standing issues researchers have always faced in quantitative finance. While investors need to be cautious—indeed, more cautious than in past applications of quantitative methods—these new tools offer many potential applications in finance. In this article, the authors develop a research protocol that pertains both to the application of machine learning techniques and to quantitative finance in general.

Foreword: Harry M. Markowitz, Nobel Laureate. Introduction. Part I: From Ideas into Products: . 1. Options and Option Replication. Options. How Options Took Off. Replicating Options. Real vs. Synthetic Options. A Risk Posed. Summary. 2. Synthetic Portfolio Insurance: The Sell. Asset Protection. Enhanced Returns. Unleashing the Aggressive Investor. Locking in Gains. Pension Fund Benefits. Beyond Equity. Job Security. Unhappy Surprises. Summary. 3. A Free Lunch? Sacrificing Wealth. Implementation Pitfalls. i. Falling Through the Floor. ii. Missing the Upside. iii. Unexpected Transaction Costs. Job Insecurity. Summary. 4. Who Needs It? An Alternative: Buy Low and Sell High. Strategies in Practice. New and Improved. Summary. Part II: The Crash of 1987: A Reality Check:. 5. The Fall of a Reigning Paradigm. An Efficient Crash. The Fundamental Things. The Psychic Crash. Summary. 6. Animal Spirits. Patterns. Noise. Overoptimism. Feedback Trading. Summary. 7. Bubbles, Cascades and Chaos. Bubbles. Informational Cascades. Chaos. Summary. 8. Futures and Index Arbitrage. The Futures -- Stock Interface. The Mixed Evidence. Arbitrage and the Crash. A Massive Liquidity Event. Summary. Part III: How Dynamic Hedging Moved Markets: . 9. Synthetic Puts and the 1987 Crash: Theory. A Fad. An Informational Cascade. Insurance, Arbitrage and Liquidity. Summary. 10. Synthetic Puts and the 1987 Crash: Evidence. Before the Crash. Black Monday. Roller Coaster Tuesday. Brady Commission and SEC Views. Summary. 11. Alibis I: The U. S. Crash. No Bounce Back. Insurers Far from Only Sellers. Investors Would Have Sold Anyway. Insurance Sales Insufficient. Insurance Trades not Correlated with Market Moves. Summary. 12. Alibis II: Across Time and Space. Explaining the 1929 Crash. i. Margins Calls in 1929. Stocks Not in the S&P 500 Crashed. Explaining the International Crash. ii. The Synchronization of World Markets. iii. Contagion Effects. Summary. 13. Did Insurance Live up to Its Name? Crash Conditions. Whipsaws. A Retreat. Why It Failed. Summary. Part IV: Option Replication Resurrected:. 14. i. Mini--Crashes of 1989, 1991 and 1997, Friday the 13th, October 1989. ii. Effect of Circuit Breakers. iii. OTC Puts. November 15, 1991. Testing the Brakes: October 27, 1997. Summary. 15. Sons of Portfolio Insurance. Sunshine Trading. Supershares. Options Reborn. Expanding the Listed Option Menu. Synthetic Warrants, Swaps and Guaranteed Equity. i. Warrants. ii. Swaps. iii. Guaranteed Equity. Summary. 16. The Enduring Risks of Synthetic Options Risks to Buyers. Risks to Dealers. Risks to Markets. Summary. 17. Living with Investment Risk. Predicting Market Moves. A Long--Run Perspective. A Premium for Patience. Summary. 18. Late Developments: Awful August 1998 and the long--term Capital Fallout. Behind the Price Moves. long--term Capital: A Hedge Fund in Need of a Hedge. A Frenzied Fall. Deja vu. Summary. Epilogue. Appendix A: The Continuing Debate. Appendix B: Option Basics. Appendix C: Option Replication. Appendix D: Synthetic Options vs. Static--Allocation Portfolios. Glossary. Bibliography. Index.

This paper states the ‘general mean-variance portfolio analysis problem’ and its solution, and briefly discusses its use in practice.

EAS-E (pronounced EASY) is an experimental programming language integrated with a database management system now running on VM/370 at the IBM Thomas J. Watson Research Center. The EAS-E programming language is built around the entity, attribute, and set ( EAS ) view of application development. It provides a means for translating operations on EAS structures directly into executable code. EAS-E commands have an English-like syntax, and thus EAS-E programs are easy to read and understand. EAS-E programs are also more compact than equivalent programs in other database languages. The EAS-E database management system allows many users simultaneous access to the database. It supports locking and deadlock detection and is capable of efficiently supporting network databases of various sizes including very large databases, consisting of several millions of entities stored on multiple DASD extends. Also available is a nonprocedural facility that allows a user to browse and update the database without writing programs.

In SIMSCRIPT I, the status of a system to be simulated is described in terms of how many of various types of entities exist; what is the value of the attributes for each entity; and what sets does it belong to and who are the members of the sets it owns. Status changes at points in time are called events. Events occur either exogenously or endogenously caused by the prior occurrences of events within the system. Events are described in event routines programmed in the SIMSCRIPT I source programming language. The language includes commands to change status, to cause or cancel event occurrences, to process decision rules, and to accumulate system performance over time and a report generator facility. The decision processing commands include FORTRAN as a subset. The SIMSCRIPT I translator reads SIMSCRIPT I source statements and, for the most part, writes FORTRAN statements allowing FORTRAN to compile these into machine language. At a language level, SIMSCRIPT 1.5* is a slightly cleaner version of SIMSCRIPT I. Internally, however, the SIMSCRIPT 1.5 translator is completely different. It is built on an entity, attribute, and set view of the translation process, a view that was also used in the building of the SIMSCRIPT II translator. The focus of SIMSCRIPT is the characterization of the status of a system in terms of its entities, attributes, and sets. This characterization has been applied to—but is not limited to—simulated systems, database systems, and the translation process itself.

This paper proposes a scalable heuristic for portfolio selection problems in which investments are illiquid and return distributions change over time.

In this analysis of the risk and return of stocks in global and Chinese markets, we build a reasonably large number of models for stock selection and create optimized portfolios to outperform a global benchmark. We apply robust regression techniques in producing stock-selection models and Markowitz-based optimization techniques in portfolio construction in a global stock universe and two Chinese stock universes. We report the results of applying a data mining corrections test to the global and Chinese stock universes. We find that (1) robust regression applications are appropriate for modeling stock returns in global and Chinese stock markets; (2) mean-variance techniques continue to produce portfolios capable of generating returns that exceed transactions costs; and (3) our global portfolio selection models pass data mining tests, such that the models produce statistically significant asset selection for global and MSCI-China universes, but not for China A-shares.

Abstract : The paper defines and applies certain concepts helpful in the analysis of such X sub ij models; it presents special computing techniques for the C sub i and C sub ij analyses; and discusses the properties, solution and application of 'embedded' X sub ij models.

Abstract The editors have invited me to contribute an essay on normative portfolio analysis to this issue of the Journal of Economics and Business . My comments are divided into three parts: 1) normative portfolio theory as of 1959, 2) a comparison of positive and normative theories, and 3) progress in normative analysis from 1959 to date, and beyond.

This material comments on “Fundamentally Flawed Indexing.”

Some analysts view risk as the Achilles Heel of employee ownership and to some extent variable pay plans such as profit sharing and gainsharing.Workers in such "shared capitalist" firms may invest too much of their wealth in the firm, contrary to the principle of diversification.This paper addresses whether the risk in shared capitalism makes it unwise for most workers or whether the risk can be managed to limit much of the loss of utility from holding the extra risk.We create an index of financial security based on worker pay and wealth, and find that workers who feel financially insecure exhibit fewer of the positive outcomes associated with shared capitalism, and are less interested than other workers in receiving more employee ownership or even more profit sharing in their workplaces.This response is substantially lessened, however, when accounting for worker empowerment, good employee relations, and high-performance work bundles that appear to buffer worker response toward risk and increase interest in shared capitalism plans.We also discuss portfolio theory which suggests that any risky investment --including stock in one's company --can be part of an efficient portfolio as long as the overall portfolio is properly diversified.We show that given estimates of risk aversion parameters, workers could prudently hold reasonable proportions of their assets in employee stock ownership of their firm with only a modest loss in utility due to risk.A good strategy for firms is to personalize individual portfolios on the basis of worker characteristics and preferences, developing investment strategies that would diversify each worker's entire portfolio in ways consistent with individual risk preferences.

1. Harry M. Markowitz 1. is the principal of Harry Markowitz Company, San Diego, CA. (harryhmm{at}aol.com) TOPICS: [Portfolio construction][1], [technical analysis][2], [analysis of individual factors/risk premia][3] [Jacobs and Levy [2013]][4] state that “conventional mean-variance

A computing technique for generating several efficient sets of combinations of the expected value, and the variance of the payoff. While this study discusses only minimization problems involving a quadratic form whose matrix is positive semidefinite, this technique may be adapted for problems of maximizing or minimizing quadratic forms (with the right properties) subject to linear constraints.

Abstract This chapter examines a debate with Samuelson regarding which criteria the long-run investor should maximize in their portfolio. It provides the example of receiving either 6% per year with certainty or a lottery with an equal chance of 200% gain or 100% log of 1 plus the returns is negative infinity (-∞). Therefore, the investor would choose the certain prospect. The chapter considers whether the long-run investor should follow the arithmetic mean or the log arithmetic (geometric mean) criteria in maximizing its portfolio. It also makes the case for the log model, although Samuelson has argued that because a prospect offers an almost certain probability that does not mean that it must yield a better expected value of utility.

A description of the Job Shop Simulation Program Generator, an application of the programming by questionnaire technique developed at the RAND Corporation to reduce the cost and time required to produce large computer programs, particularly those required for simulations of portions of the Air Force logistics system.

EAS-E is based on the entity-attribute-set view of system description—a useful formalism for system modeling and planning even when programming is done in languages other than EAS-E.

SIMSCRIPT III is a programming language for discrete-event simulation. It is a major extension of its predecessor, SIMSCRIPT II.5, providing full support for object-oriented programming and modular software development.

The programming burden has often impeded computer application, but programming time and cost have been considerably reduced by the development of advanced programming languages such as FORTRAN, COBOL, and SIMSCRIPT. The objective of the technique discussed here is to further reduce the time and effort required to produce large computer programs within specified areas.

The theory of portfolio selection together with capital asset pricing theory provides the foundation and the building blocks for the management of portfolios. The goal of portfolio selection is the construction of portfolios that maximize expected returns consistent with individually acceptable levels of risk. Using both historical data and investor expectations of future returns, portfolio selection uses modeling techniques to quantify expected portfolio returns and acceptable levels of portfolio risk and provides methods to select an optimal portfolio.

On 8 November and 6 December 2016, Mark Kritzman, CFA, interviewed Harry M. Markowitz to discuss his background at the University of Chicago, the Cowles Commission, and the RAND Corporation; his many contributions not only to modern portfolio theory but also to other fields; and his views on the 2008 global financial crisis.

Recently there has been a growing interest in the scenario model of covariance as an alternative to the one-factor or many-factor models. We show how the covariance matrix resulting from the scenario model can easily be made diagonal by adding new variables linearly related to the amounts invested; note the meanings of these new variables; note how portfolio variance divides itself into “within scenario” and “between scenario” variances; and extend the results to models in which scenarios and factors both appear where factor distributions and effects may or may not be scenario sensitive.