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This article, titled "Editors' Introduction: The Birth of Portfolio Theory," discusses the impact of Harry M. Markowitz on the field of investing. Markowitz's 1952 paper and subsequent book, "Portfolio Selection," introduced the concept of portfolio theory and emphasized the importance of diversification. His work revolutionized the field and laid the foundation for modern portfolio management. The article also highlights Markowitz's contributions to other disciplines, such as simulation methods and sparse matrix methods. The special issue of The Journal of Portfolio Management pays tribute to Markowitz's legacy and includes personal reflections as well as scholarly contributions that build upon his work. [Extracted from the article]

Bruce Jacobs, Ken Levy, and Harry Markowitz shared similar interests and did complementary work. This led to collaboration, debate, and building upon each other's ideas and research. They had a prodigious relationship of over 30 years, bridging the gap between theory and practice. Bruce individually, and then with Harry, distinguished between portfolio insurance and portfolio theory. Bruce and Ken estimated security expected returns using cross-sectional analysis, and Harry used that methodology for portfolio management. Bruce and Ken used Harry's methods for portfolio construction, and they jointly explored the value of using constraints in portfolio optimization and addressed the optimality and optimization of long–short portfolios. Bruce, Ken, and Harry jointly developed an asynchronous, discrete-time, dynamic market simulator, JLMSim, to explain the behavior of security prices and to find equilibrium expected returns. Bruce and Ken extended portfolio theory to account for the unique risks of leverage and applied investor volatility aversion and leverage aversion to portfolio choice. The optimal portfolio lies within an efficient region and on a three-dimensional efficient surface. Harry concurred that the mean–variance model is a special case of the mean–variance-leverage model. Bruce and Ken used the mean–variance-leverage model to address the optimal amount of leverage in 130–30-type portfolio strategies. Bruce and Ken would challenge Harry, and Harry would challenge Bruce and Ken, and out of that would often come something interesting and useful. [ABSTRACT FROM AUTHOR]

The article looks at smart beta investment strategy as compared to smart alpha investment strategy in the context of stock investments. Topics addressed include beta and alfa investing, respectively described by the author as "passive and active" in terms of stock price attitude and both strategies' possible risks. Potential yields of beta and alfa investments are also investigated.

Leverage entails a unique set of risks, such as margin calls, which can force investors to liquidate securities at adverse prices. Investors often seek to mitigate these risks by using a leverage constraint in conventional mean-variance portfolio optimization. Mean-variance optimization is unable to identify the portfolio offering the highest utility, however, because it provides the investor with little guidance as to where to set the leverage constraint. An alternative approach--the mean-variance-leverage optimization model--lets the leverage-averse investor determine the optimal leverage level (and thus the highest-utility portfolio) by balancing the portfolio's expected return against the portfolio's volatility risk and its leverage risk. [ABSTRACT FROM AUTHOR]

The article presents a comparison between the Markowitz general mean-variance portfolio selection model and the mean-variance-leverage optimization model (MVL). It mentions that the MVL offers a practical insight on the investors' aversion to leverage and on the methodology that is straightforward and ready to implement. On the other hand, Markowitz general mean-variance differs in its conventional usage.

This article proposes to augment portfolio theory and mean-variance optimization to incorporate investors' aversion to leverage. The authors suggest a specification for leverage aversion that captures the unique risks of leverage. They also introduce mean-variance leverage-efficient frontiers, compare them with conventional mean-variance-efficient frontiers, and develop a mean-variance leverage-efficient region. Their analysis shows that leverage aversion can have a large effect on an investor's portfolio choice. [ABSTRACT FROM AUTHOR]

The article presents obituaries for Peter L. Bernstein, founding editor of "The Journal of Portfolio Management," who died on June 5, 2009.

The article discusses strategies for comparing enhanced active equity portfolios against the more traditional equitiezed market-neutral long-short portfolios. The authors considers three hypothetical portfolios that include a market-neutral long-short, an equitized market-neutral long-short, and a trim equitiezed portfolio that equals an enhanced active equity portfolio. The authors also discuss ways in which a portfolio manager can trim and equitized an equitized portfolio. Information on benchmark index choices is also provided.

When they want to see how complex systems work, scientists often turn to asynchronous-time simulation models, in which processes change sporadically, typically at irregular intervals, over time. Such models are rarely used in finance now, but they may prove 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 authors describe asynchronous simulation generally, and illustrate its capabilities in terms of an asynchronous, discrete-event stock market simulator that they developed. The JLM Market Simulator allows its users to create a model of the market, using their own inputs. JLM Sim users can vary the number of investors, traders, portfolio analysts, statisticians, and securities, as well as the decisions rules used in investing and trading. Compared with most models used today, such a simulation may be able to provide a more realistic picture of complex markets. [ABSTRACT FROM AUTHOR]

Focuses on the maximization of the returns available from security selection while also achieving an asset allocation that meets desired return and risk goals. Goal of optimization; Disadvantages suffered by traditional active management; Potential benefits of alpha transport.

The article explains that the ability to sell stocks short enables the investor to benefit from stocks with negative expected returns as well as from those with positive expected returns. However, the benefits of combining long and short portfolio positions essentially depend on the way the portfolio is constructed. An optimal approach is the integrated, single-portfolio scheme that takes into account the expected returns, risks and correlations of all securities simultaneously. The procedure allows the investor to optimize his ability to trade off risk and return for the maximum possible performance.

The article discusses the residual risk. In portfolio management, excess return measures the difference between the portfolio's returns and those of an underlying benchmark and residual risk measures the volatility of those excess returns. The article shows that greater access return comes at a cost of greater residual risk. On any given slope, the optimal portfolio for an investor will depend upon the investor's level of aversion or residual risk. The more regret-averse the investor, the closer to the origin the preferred portfolio will be. Meanwhile, it also provides advice for investors. First, investors should be aware that accepting any arbitrary limit on residual risk may entail a significant sacrifice in utility. Second, investors should actively search out high-investor risk managers.

The article discusses various aspects of the three long/short equity investment strategies. The strategies include the market-neutral strategy, the equitized strategy, and the hedge strategy. It is noted that the market-neutral strategy holds long and short equity investments in equal dollar balance at all times. The equitized strategy, in addition to holding long and short investments in equal dollar balance, adds a permanent stock index futures overly in an amount equal to the invested capital. The hedge strategy, on the other hand, holds long and short investments in equal dollar balance but has a variable equity market exposure.

The stock market is a complex system. Security pricing is not merely random, nor are simple theories adequate to explain market operation. Rather, the market is permeated by a web of interrelated return effects. Substantial power is needed to disentangle and model return regularities. The pervasive and complex web of interrelated return effects must first be disentangled to distinguish real effects from mere proxies. The standard approach to measuring a return effect, such as low price per earnings ratio, first screens for a set of stocks below a given price per earnings ratio. Portfolio returns are then calculated and compared to those of the universe. Some methods used in the stock market are risk modeling, return modeling, value modeling in an efficient market, modeling empirical return regularities and Bayesian random walk forecasting.

The article emphasizes the importance of recognizing aversion in portfolio selection. Topics covered include modern portfolio theory for portfolio analysis, the use of mean-variance optimization to implement portfolio theory in practice, the implications of using leverage for investors and the proposal to augment portfolio theory's mean-variance utility function to include a term for leverage aversion.