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14
The performance of mutual funds in the period 19451964
 Journal of Finance
, 1968
"... In this paper I derive a riskadjusted measure of portfolio performance (now known as "Jensen's Alpha") that estimates how much a manager's forecasting ability contributes to the fund's returns. The measure is based on the theory of the pricing of capital assets by Sharpe (1964), Lintner (1965a) and ..."
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Cited by 276 (0 self)
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In this paper I derive a riskadjusted measure of portfolio performance (now known as "Jensen's Alpha") that estimates how much a manager's forecasting ability contributes to the fund's returns. The measure is based on the theory of the pricing of capital assets by Sharpe (1964), Lintner (1965a) and Treynor (Undated). I apply the measure to estimate the predictive ability of 115 mutual fund managers in the period 19451964—that is their ability to earn returns which are higher than those we would expect given the level of risk of each of the portfolios. The foundations of the model and the properties of the performance measure suggested here are discussed in Section II. The evidence on mutual fund performance indicates not only that these 115 mutual funds were on average not able to predict security prices well enough to outperform a buythemarketandhold policy, but also that there is very little evidence that any individual fund was able to do significantly better than that which we expected from mere random chance. It is also important to note that these conclusions hold even when we measure the fund returns gross of management expenses (that is assume their bookkeeping, research, and other expenses except brokerage commissions were obtained free). Thus on average the funds apparently were not quite successful enough in their trading activities to recoup even their brokerage expenses. Keywords: Jensen's Alpha, mutual fund performance, riskadjusted returns, forecasting ability, predictive ability.
Risk reduction in large portfolios: Why imposing the wrong constraints helps
, 2002
"... Green and Hollifield (1992) argue that the presence of a dominant factor is why we observe extreme negative weights in meanvarianceefficient portfolios constructed using sample moments. In that case imposing noshortsale constraints should hurt whereas empirical evidence is often to the contrary. ..."
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Cited by 84 (3 self)
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Green and Hollifield (1992) argue that the presence of a dominant factor is why we observe extreme negative weights in meanvarianceefficient portfolios constructed using sample moments. In that case imposing noshortsale constraints should hurt whereas empirical evidence is often to the contrary. We reconcile this apparent contradiction. We explain why constraining portfolio weights to be nonnegative can reduce the risk in estimated optimal portfolios even when the constraints are wrong. Surprisingly, with noshortsale constraints in place, the sample covariance matrix performs as well as covariance matrix estimates based on factor models, shrinkage estimators, and daily data.
Risk reduction in large portfolios: a role for portfolio weight constraints
, 2001
"... Meanvariance efficient portfolios constructed using sample moments often involve taking extreme long and short positions. Hence practitioners often impose portfolio weight constraints when constructing efficient portfolios. Green and Hollifield (1992) argue that the presence of a single dominant fa ..."
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Cited by 6 (0 self)
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Meanvariance efficient portfolios constructed using sample moments often involve taking extreme long and short positions. Hence practitioners often impose portfolio weight constraints when constructing efficient portfolios. Green and Hollifield (1992) argue that the presence of a single dominant factor in the covariance matrix of returns is why we observe extreme positive and negative weights. If this were the case then imposing the weight constraint should hurt whereas the empirical evidence is often to the contrary. We reconcile this apparent contradiction. We show that constraining portfolio weights to be nonnegative is equivalent to using the sample covariance matrix after reducing its large elements and then form the optimal portfolio without any restrictions on portfolio weights. This shrinkage helps reduce the risk in estimated optimal portfolios even when they have negative weights in the population. Surprisingly, we also find that once the nonnegativity constraint is imposed, minimum variance and minimum tracking error portfolios constructed using the sample covariance matrix perform as well as
The Solution of a Class of Limited Diversification Portfolio Selection Problems
, 1997
"... The Solution of a Class of Limited Diversification Portfolio Selection Problems by Gwyneth Owens Butera A branchandbound algorithm for the solution of a class of mixedinteger nonlinear programming problems arising from the field of investment portfolio selection is presented. The problems in this ..."
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The Solution of a Class of Limited Diversification Portfolio Selection Problems by Gwyneth Owens Butera A branchandbound algorithm for the solution of a class of mixedinteger nonlinear programming problems arising from the field of investment portfolio selection is presented. The problems in this class are characterized by the inclusion of the fixed transaction costs associated with each asset, a constraint that explicitly limits the number of distinct assets in the selected portfolio, or both. Modeling either of these forms of limiting the cost of owning an investment portfolio involves the introduction of binary variables, resulting in a mathematical programming problem that has a nonconvex feasible set. Two objective functions are examined in this thesis; the first is a positive definite quadratic function which is commonly used in the selection of investment portfolios. The second is a convex function that is not continuously differentiable; this objective function, although not...
Digital Portfolio Theory
"... Abstract. The Modern Portfolio Theory of Markowitz maximized portfolio expected return subject to holding total portfolio variance below a selected level. Digital Portfolio Theory is an extension of Modern Portfolio Theory, with the added dimension of memory. Digital Portfolio Theory decomposes the ..."
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Abstract. The Modern Portfolio Theory of Markowitz maximized portfolio expected return subject to holding total portfolio variance below a selected level. Digital Portfolio Theory is an extension of Modern Portfolio Theory, with the added dimension of memory. Digital Portfolio Theory decomposes the portfolio variance into independent components using the signal processing decomposition of variance. The risk or variance of each security’s return process is represented by multiple periodic components. These periodic variance components are further decomposed into systematic and unsystematic parts relative to a reference index. The Digital Portfolio Theory model maximizes portfolio expected return subject to a set of linear constraints that control systematic, unsystematic, calendar and noncalendar variance. The paper formulates a single period, digital signal processing, portfolio selection model using crosscovariance constraints to describe covariance and autocorrelation characteristics. Expected calendar effects can be optimally arbitraged by controlling the memory or autocorrelation characteristics of the efficient portfolios. The Digital Portfolio Theory optimization model is compared to the Modern Portfolio Theory model and is used to find efficient portfolios with zero calendar risk for selected periods. Key words: portfolio optimization, portfolio theory, digital signal processing, calendar anomalies 1.
Criteria, Models and Strategies in Portfolio Selection
, 2000
"... In this paper, we survey ideas and principles of modeling the investment decision process of economic agents. We start with the criteria of Markowitz of formulating return and risk as mean and variance, and also its extensions. We then look into other related criteria which are based on probability ..."
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In this paper, we survey ideas and principles of modeling the investment decision process of economic agents. We start with the criteria of Markowitz of formulating return and risk as mean and variance, and also its extensions. We then look into other related criteria which are based on probability assumptions on future prices of securities. We also present methodologies which, instead of assuming probability distributions, rely on the best solution for the worst case scenario or in the average. A few multiple stage optimization models are discussed. Finally we give a few remarks on some interesting topics for further investigations.
AN EMPIRICAL ANALYSIS OF MARKET AND INDUSTRY FACTORS IN STOCK RETURNS OF U.S. AEROSPACE INDUSTRY
"... This paper applies multiindex CAPMs to explore the relationships of U.S. aerospace company stock returns to selected market and industry variables. This paper finds that the market returns represented by the S&P 500 index and Department of Defense expenditures are significantly positively related t ..."
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This paper applies multiindex CAPMs to explore the relationships of U.S. aerospace company stock returns to selected market and industry variables. This paper finds that the market returns represented by the S&P 500 index and Department of Defense expenditures are significantly positively related to aerospace stock returns. The regression results on other variables are mixed; in particular, aircraft shipments are positively related to aerospace stock returns, but the relations are not significant. Additional regression analysis of employing unanticipated changes in independent variables provides confirmatory evidence. The results of this paper suggest that a multiindex CAPM using selected economic and industry variables provides additional power in explaining the variability of U.S. aerospace stock returns over a single index model using the market index alone.
Modern portfolio theory, 1950 to date
"... In this article we have reviewed ``Modern Portfolio Analysis' ' and outlined some important topics for further research. Issues discussed include the history and future of portfolio theory, the key inputs necessary to perform portfolio optimization, speci®c problems in applying portfolio theory to ® ..."
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In this article we have reviewed ``Modern Portfolio Analysis' ' and outlined some important topics for further research. Issues discussed include the history and future of portfolio theory, the key inputs necessary to perform portfolio optimization, speci®c problems in applying portfolio theory to ®nancial institutions, and the methods for evaluating how well portfolios are managed. Emphasis is placed on both the history of major concepts and where further research is needed in each of these areas. Ó 1997
PITFALLS IN USING THE S&P BOGEY FOR FINANCIAL ANALYSIS AND PORTFOLIO MANAGEMENT
"... The S&P 500 Index is a benchmark that is widely accepted and used though much maligned. In this study we add a chapter to the index’s detractors by investigating and revealing an industry bias builtin to the index. By the nature of the construction of the S&P there exists sectoral imbalances. By co ..."
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The S&P 500 Index is a benchmark that is widely accepted and used though much maligned. In this study we add a chapter to the index’s detractors by investigating and revealing an industry bias builtin to the index. By the nature of the construction of the S&P there exists sectoral imbalances. By constructing ‘S&P Comparable ’ indexes we establish first that such an industry bias does exist and then we probe deeper to find the sectors that are favored or disfavored. We find that the problem is not insevere and neither does it go away with time.
Where did the Smart Money go? Evidence on fundselection ability amongst UK investors
"... Studies have shown mixed results in testing fundselection ability amongst investors as a possible explanation to the mutual fund puzzle proposed by Gruber (1996). While most studies focus on the US mutual fund market, Keswani & Stolin (2008) propose that the smart money effect is empirically eviden ..."
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Studies have shown mixed results in testing fundselection ability amongst investors as a possible explanation to the mutual fund puzzle proposed by Gruber (1996). While most studies focus on the US mutual fund market, Keswani & Stolin (2008) propose that the smart money effect is empirically evident in the UK market, using data on funds from 19912000. This study aims to evaluate their hypothesis on the latest dataset from 2000 – 2010. The motivation behind doing so lies in the tremendous growth facing the U.K. mutual fund industry in the last decade or so. Most of the growth in the industry’s history has taken place during this time. Hence the argument of smart money as an explanation to the growth of the mutual fund industry dictates that fundselection ability should be especially prominent during our sample period. However we find that this is not the case. Possible explanations for the failure to find the smart money effect include excessive risktaking by fund managers and increased search costs for investors, amongst other reasons.