Results 1  10
of
10
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 ..."
Abstract

Cited by 286 (0 self)
 Add to MetaCart
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.
A priori optimization
 Operations Research
, 1990
"... Algorithm for cardinalityconstrained quadratic ..."
A Brief History of Downside Risk Measures
 Journal of Investing
, 1999
"... Introduction There has been a controversy in this journal about using downside risk measures in portfolio analysis. The downside risk measures supposedly are a major improvement over traditional portfolio theory. That is where the battle lines clashed when Rom and Ferguson (1993, 1994b) and Kaplan ..."
Abstract

Cited by 16 (1 self)
 Add to MetaCart
Introduction There has been a controversy in this journal about using downside risk measures in portfolio analysis. The downside risk measures supposedly are a major improvement over traditional portfolio theory. That is where the battle lines clashed when Rom and Ferguson (1993, 1994b) and Kaplan and Siegel (1994a, 1994b) engaged in a "tempest in a teapot". I should confess that I am strong supporter of downside risk measures and have used them in my teaching, research and software for the past two decades. Therefore, you should keep that bias in mind as you read this article. One of the best means to understand a concept is to study the history of its development. Understanding the issues facing researchers during the development of a concept results in better knowledge of the concept. The purpose of this paper is to provide an understanding of the measurement of downside risk. First, it helps to define terms. Portfolio theory is the application of decisionmaking tools unde
Tailoring Asset Allocation To The Individual Investor
 International Review of Economics and Business
, 1990
"... Asset allocation has typically used optimization algorithms to determine security allocations within a portfolio in order to obtain the best tradeoff between risk and return. These techniques, by using the variance as a measure of risk restrict the investor to one level of risk aversion (utility fun ..."
Abstract

Cited by 8 (5 self)
 Add to MetaCart
Asset allocation has typically used optimization algorithms to determine security allocations within a portfolio in order to obtain the best tradeoff between risk and return. These techniques, by using the variance as a measure of risk restrict the investor to one level of risk aversion (utility function) which has to fit all investors. Since individual investors have different levels of risk aversion, this paper proposes a heuristic portfolio selection technique that can match the risk measure to the specific level of risk aversion of the investor. The technique is tested with 34 years of monthly data to demonstrate its use.
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 ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
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...
Earnings Announcements and Portfolio Selection. Do They Add Value?
"... A number of articles have explored analysts' earnings estimates, analysts' forecasting ability and the reaction of stock prices to earnings announcements. The concern of this paper is the group of stocks whose earnings announcements constitute positive "earnings surprise" given a consensus of analys ..."
Abstract
 Add to MetaCart
A number of articles have explored analysts' earnings estimates, analysts' forecasting ability and the reaction of stock prices to earnings announcements. The concern of this paper is the group of stocks whose earnings announcements constitute positive "earnings surprise" given a consensus of analysts' expected earnings. The consensus derives from a twenty quarter, seasonally adjusted earnings trend and the median analysts' forecasts from I/B/E/S, Inc. The quantitative research department of a major securities firm (Prudential Securities) has identified and published a list of "earnings surprise" stocks for the past 9 years. The major issue addressed in this paper is whether the information contained in earnings announcements is useful as a security screening technique for a portfolio selection process, i.e., Do earnings announcements add value to the performance of a portfolio? Previous studies focus on individual securities. There have been no attempts at integrating earnings surprise into a systematic portfolio approach. The study conducts a comprehensive backtest of whether there is new investment information in earnings surprise data when used with a portfolio selection algorithm. A unique feature of this study is that it uses economic return performance to evaluate its results rather than the more commonly used statistical methodology. The results indicate that using earnings surprise information in a periodic revision of a portfolio does not add value. Any value added derives from the portfolio selection algorithm not from the fact that the stocks in the analysis are "earnings surprise" stocks. In addition, the earnings surprise stocks are a source of increased volatility when used in 1530 asset portfolios. I.
AMO { Advanced Modeling and Optimization, Volume 4, Number 1, 2002
"... This paper presents a simplex algorithm for solving portfolio selection problem with transaction costs. The transaction cost function is assumed to be a Vshaped function of the dierence between an existing portfolio and a new one. Under some assumptions on the variancecovariance matrix of returns, ..."
Abstract
 Add to MetaCart
This paper presents a simplex algorithm for solving portfolio selection problem with transaction costs. The transaction cost function is assumed to be a Vshaped function of the dierence between an existing portfolio and a new one. Under some assumptions on the variancecovariance matrix of returns, we transform the nondierentiable and biobjective programming problem to a mathematical programming problem which can be eciently solved by a revised simplex algorithm. This method can be used by mutual funds managers in selecting their portfolios.
1 Robust distributed linear programming
"... This paper presents a robust, distributed algorithm to solve general linear programs. The algorithm design builds on the characterization of the solutions of the linear program as saddle points of a modified Lagrangian function. We show that the resulting continuoustime saddlepoint algorithm is pr ..."
Abstract
 Add to MetaCart
This paper presents a robust, distributed algorithm to solve general linear programs. The algorithm design builds on the characterization of the solutions of the linear program as saddle points of a modified Lagrangian function. We show that the resulting continuoustime saddlepoint algorithm is provably correct but, in general, not distributed because of a global parameter associated with the nonsmooth exact penalty function employed to encode the inequality constraints of the linear program. This motivates the design of a discontinuous saddlepoint dynamics that, while enjoying the same convergence guarantees, is fully distributed and scalable with the dimension of the solution vector. We also characterize the robustness against disturbances and link failures of the proposed dynamics. Specifically, we show that it is integralinputtostate stable but not inputtostate stable. The latter fact is a consequence of a more general result, that we also establish, which states that no algorithmic solution for linear programming is inputtostate stable when uncertainty in the problem data affects the dynamics as a disturbance. Our results allow us to establish the resilience of the proposed distributed dynamics to disturbances of finite variation and recurrently disconnected communication among the agents. Simulations in an optimal control application illustrate our results. I.
Stocks Portfolio Optimization Using Classification and Genetic Algorithms
"... In this paper we present an approach based on the classification and genetic algorithms to obtain an optimal stock portfolio of a reduced size from an initial portfolio, which leads to a financial gain surplus in terms of cost and taxes reduction, and a performance at reduced design loads. This appr ..."
Abstract
 Add to MetaCart
In this paper we present an approach based on the classification and genetic algorithms to obtain an optimal stock portfolio of a reduced size from an initial portfolio, which leads to a financial gain surplus in terms of cost and taxes reduction, and a performance at reduced design loads. This approach takes place in two steps: The first step is to classify the actions of this portfolio into classes, known as under portfolios, with the expected returns and VaR’s close to each other by using the KMeans method, then we apply an algorithm dynamic optimization called MinVaRMaxVaL to the portfolio that has the highest expected return and the lowest average VaR obtained by this classification. The algorithm proposed MinVaRMaxVaL for the selection of optimal actions portfolio is based on genetic algorithms and Value at Risk (VaR). The objective of our algorithm is to minimize risk and maximize portfolio value at the same time through two stages. The first step is to minimize the risk measured by the value at risk (VaR) for a given value of portfolio. While the second step, a dynamically maximizes the value of portfolio as the result is greater than the4674 M. El hachloufi, Z. Guennoun and F. Hamza portfolio value set at the first stage and the risk resulting from the second stage is lower than that obtained from the first. The proportions of shares in the portfolio are the optimal portfolio.