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188
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 (1 ..."
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Cited by 490 (1 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.
A Test of the Efficiency of a Given Portfolio
 In Econometrica
, 1989
"... A test for the ex ante efficiency of a given portfolio of assets is analyzed. The relevant statistic has a tractable small sample distribution. Its power function is derived and used to study the sensitivity of the test to the portfolio choice and to the number of assets used to determine the ex pos ..."
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Cited by 284 (12 self)
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A test for the ex ante efficiency of a given portfolio of assets is analyzed. The relevant statistic has a tractable small sample distribution. Its power function is derived and used to study the sensitivity of the test to the portfolio choice and to the number of assets used to determine the ex post meanvariance efficient frontier. Several intuitive interpretations of the test are provided, including a simple meanstandard deviation geometric explanation. A univariate test, equivalent to our multivariatebased method, is derived, and it suggests some useful diagnostic tools which may explain why the null hypothesis is rejected. Empirical examples suggest that the multivariate approach can lead to more appropriate conclusions than those based on traditional inference which relies on a set of dependent univariate statistics.
The capital asset pricing model: Some empirical tests
, 1972
"... Considerable attention has recently been given to general equilibrium models of the pricing of capital assets. Of these, perhaps the best known is the meanvariance formulation originally ..."
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Cited by 271 (2 self)
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Considerable attention has recently been given to general equilibrium models of the pricing of capital assets. Of these, perhaps the best known is the meanvariance formulation originally
Universal Portfolios
, 1996
"... We exhibit an algorithm for portfolio selection that asymptotically outperforms the best stock in the market. Let x i = (x i1 ; x i2 ; : : : ; x im ) t denote the performance of the stock market on day i ; where x ij is the factor by which the jth stock increases on day i : Let b i = (b i1 ; b i2 ..."
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Cited by 196 (5 self)
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We exhibit an algorithm for portfolio selection that asymptotically outperforms the best stock in the market. Let x i = (x i1 ; x i2 ; : : : ; x im ) t denote the performance of the stock market on day i ; where x ij is the factor by which the jth stock increases on day i : Let b i = (b i1 ; b i2 ; : : : ; b im ) t ; b ij 0; P j b ij = 1 ; denote the proportion b ij of wealth invested in the jth stock on day i : Then S n = Q n i=1 b t i x i is the factor by which wealth is increased in n trading days. Consider as a goal the wealth S n = max b Q n i=1 b t x i that can be achieved by the best constant rebalanced portfolio chosen after the stock outcomes are revealed. It can be shown that S n exceeds the best stock, the Dow Jones average, and the value line index at time n: In fact, S n usually exceeds these quantities by an exponential factor. Let x 1 ; x 2 ; : : : ; be an arbitrary sequence of market vectors. It will be shown that the nonanticipating sequence ...
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 153 (4 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.
Honey, I shrunk the sample covariance matrix
 The Journal of Portfolio Management
, 2004
"... The central message of this paper is that nobody should be using the sample covariance matrix for the purpose of portfolio optimization. It contains estimation error of the kind most likely to perturb a meanvariance optimizer. In its place, we suggest using the matrix obtained from the sample cova ..."
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Cited by 48 (1 self)
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The central message of this paper is that nobody should be using the sample covariance matrix for the purpose of portfolio optimization. It contains estimation error of the kind most likely to perturb a meanvariance optimizer. In its place, we suggest using the matrix obtained from the sample covariance matrix through a transformation called shrinkage. This tends to pull the most extreme coecients towards more central values, thereby systematically reducing estimation error where it matters most. Statistically, the challenge is to know the optimal shrinkage intensity, and we give the formula for that. Without changing any other step in the portfolio optimization process, we show on actual stock market data that shrinkage reduces tracking error relative to a benchmark index, and substantially increases the realized information ratio of the active portfolio manager.
Portfolio choice problems
 Handbook of Financial Econometrics, forthcoming
, 2004
"... After years of relative neglect in academic circles, portfolio choice problems are again at the forefront of financial research. The economic theory underlying an investor’s optimal ..."
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Cited by 42 (2 self)
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After years of relative neglect in academic circles, portfolio choice problems are again at the forefront of financial research. The economic theory underlying an investor’s optimal
Asset Pricing Models: Implications for Expected Returns and Portfolio Selection
, 1999
"... Implications of factorbased asset pricing models for estimation of expected returns and for portfolio selection are investigated. In the presence of model mispricing due to a missing risk factor, the mispricing and the residual covariance matrix are linked together. Imposing a strong form of this l ..."
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Cited by 37 (0 self)
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Implications of factorbased asset pricing models for estimation of expected returns and for portfolio selection are investigated. In the presence of model mispricing due to a missing risk factor, the mispricing and the residual covariance matrix are linked together. Imposing a strong form of this link leads to expected return estimates that are more precise and more stable over time than unrestricted estimates. Optimal portfolio weights that incorporate the link when no factors are observable are proportional to expected return estimates, effectively using an identity matrix as a covariance matrix. The resulting portfolios perform well both in simulations and in outofsample comparisons.
Portfolio optimization and hedge fund style allocation decisions
 EDHECACT RISK AND ASSET MANAGEMENT RESEARCH
, 2002
"... This paper attempts to evaluate the outofsample performance of an improved estimator of the covariance structure of hedge fund index returns, focusing on its use for optimal portfolio selection. Using data from CSFBTremont hedge fund indices, we …nd that expost volatility of minimum variance por ..."
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Cited by 28 (6 self)
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This paper attempts to evaluate the outofsample performance of an improved estimator of the covariance structure of hedge fund index returns, focusing on its use for optimal portfolio selection. Using data from CSFBTremont hedge fund indices, we …nd that expost volatility of minimum variance portfolios generated using implicit factor based estimation techniques is between 1.5 and 6 times lower than that of a valueweighted benchmark, such differences being both economically and statistically significant. This strongly indicates that optimal inclusion of hedge funds in an investor portfolio can potentially generate a dramatic decrease in the portfolio volatility on an outofsample basis. Differences in mean returns, on the other hand, are not statistically significant, suggesting that the improvement in terms of risk control does not necessarily come at the cost of lower expected returns.
Testing for MeanVariance Spanning: A Survey
 JOURNAL OF EMPIRICAL FINANCE
, 2001
"... In this paper we present a survey on the various approaches that can be used to test whether the meanvariance frontier of a set of assets spans or intersects the frontier of a larger set of assets. We analyze the restrictions on the return distribution that are needed to have meanvariance spanning ..."
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Cited by 20 (2 self)
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In this paper we present a survey on the various approaches that can be used to test whether the meanvariance frontier of a set of assets spans or intersects the frontier of a larger set of assets. We analyze the restrictions on the return distribution that are needed to have meanvariance spanning or intersection. The paper explores the duality between meanvariance frontiers and volatility bounds, analyzes regression based test procedures for spanning and intersection, and shows how these regression based tests are related to tests for meanvariance efficiency, performance measurement, optimal portfolio choice, and specification error bounds.