Journal ArticleDOI
Portfolio Optimization with Factors, Scenarios, and Realistic Short Positions
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TLDR
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.Abstract:
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.read more
Citations
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Stochastic portfolio optimization with proportional transaction costs: Convex reformulations and computational experiments
TL;DR: A probabilistic version of the Markowitz portfolio problem with proportional transaction costs is proposed, and the second-order cone formulation in which the number of quadratic terms is invariant to thenumber of assets is the most efficient.
Book
Risk-Return Analysis
TL;DR: In this paper, the authors examine several risk-return criteria, but focus principally on the mean-variance analysis, and explore investment opportunities in terms familiar to the financial practitioner: the risk and return of the investment portfolio.
Journal ArticleDOI
Complex portfolio selection via convex mixed‐integer quadratic programming: a survey
Journal ArticleDOI
A Discretionary Wealth Approach for Investment Policy
TL;DR: In this paper, a point-estimate model based on growing discretionary wealth is used for broad investment policy, where risk in saving and spending plans is treated in parallel to risk in investment returns and can be fully comprehended by a Bayesian approach.
References
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Journal ArticleDOI
Portfolio Selection: Efficient Diversification of Investments
Alan Stuart,Harry M. Markowitz +1 more
TL;DR: In this article, the authors defined asset classes technology sector stocks will diminish as the construction of the portfolio, and the construction diversification among the, same level of assets, which is right for instance among the assets.
Journal ArticleDOI
A Simplified Model for Portfolio Analysis
TL;DR: Preliminary evidence suggests that the relatively few parameters used by the model can lead to very nearly the same results obtained with much larger sets of relationships among securities, as well as the possibility of low-cost analysis.
Book
Portfolio Selection: Efficient Diversification of Investments
TL;DR: In this paper, the authors apply modern techniques of analysis and computation to find combinations of securities that best meet the needs of private or institutional investors, such as hedge fund managers, hedge funds, and hedge funds.
Book
Mean-Variance Analysis in Portfolio Choice and Capital Markets
Harry M. Markowitz,G. Peter Todd +1 more
TL;DR: In this paper, the general portfolio selection model preliminary results solution to a portfolio selection program special cases a special case portfolio selection programme is presented, and the model is used for portfolio selection.
Journal ArticleDOI
The simplex method for quadratic programming
TL;DR: In this article, a computational procedure for finding the minimum of a quadratic function of variables subject to linear inequality constraints is given, analogous to the Simplex Method for linear programming, being based on the Barankin-Dorfman procedure.