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Showing 1 to 20 of 73 for “"Portfolio optimization"”.
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Robust portfolio optimization
The Markowitz mean-variance portfolio optimization is a well known and also widely used investment theory in allocating the assets. However, this theory is also familiar with the extremely sensitive outcome by the small changes in the data. Ben-Tal and Nemirovski [3] therefore introduced the robust …
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Heuristic Approaches to Portfolio Optimization.
… areas in finance is the classical mean-variance portfolio selection model pioneered by Harry Markowitz; which is also, undoubtedly recognized as the foundation of modern portfolio theory. The model in its basic form deals with the selection of portfolio of assets such that a reasonable trade-off …
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Advances in Risk Parity Portfolio Optimization
… contributions of the constituent assets in a portfolio. The resulting portfolio is fully diversified from a risk perspective. However, like other asset allocation strategies, risk parity is susceptible to estimation errors. Moreover, its mathematical formulation imposes some fundamental …
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On the Topic of Portfolio Optimization
… as a risk measure for the purpose of investment portfolio optimization and selection. First, we present an improved method of applying entropy as a risk in portfolio optimization. A new family of portfolio optimization problems called the return-entropy portfolio optimization (REPO) is introduced …
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QAOA applied to the portfolio optimization problem
… there is one called QAOA (quantum approximate optimization algorithm). As the name suggests, this is a quantum algorithm that approximates the solution of optimization problems. The objective of this work was to apply this algorithm to solve an optimization problem in the finance area known as …
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On front-running momentum and portfolio optimization
… formation and holding period of winner and loser portfolio. This research paper studies momentum using a weekly approach and examines strategies that are more flexible than the crowded month-end approach. In particular, this paper is interested in analyzing the legal front-running of month-end …
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Modern portfolio optimization using robust estimation techniques
… input parameters for the Markowitz and Sharpe portfolio models. The main goal is to ascertain whether or not the input parameters determined, using the robust procedures, yield better results than the Ordinary Least Squares (OLS) procedure.
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An Analytical Framework for Planogram Portfolio Optimization
… an analytical framework for an end-to-end optimization of portfolios of planograms within Target, focusing on the optimal trade-off between planogram-store personalization and standardization. The study utilizes retail data from Target to develop mathematical frameworks partly based on …
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Portfolio optimization with quantile-based risk measures
In this thesis we analyze Portfolio Optimization risk-reward theory, a generalization of the mean-variance theory, in the cases where the risk measures are quantile-based (such as the Value at Risk (V aR) and the shortfall). We show, using multicriteria theory arguments, that if the measure of risk …
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Portfolio optimization using non-Gaussian return distributions
Thesis (M.S.)--Massachusetts Institute of Technology, Sloan School of Management, 1996.
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Essays on Portfolio Optimization, Simulation and Option Pricing
… analysis of a four asset mean variance portfolio optimization problem. The first paper studies different efficient simulation methods to price options with different characters such as moneyness and maturity times. The incomplete market environments are also been considered. The second …
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Essays in dynamic portfolio optimization and diffusion estimations
Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, 1990.
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Derivative pricing and logarithmic portfolio optimization in incomplete markets
… pricing in incomplete markets and the problem of portfolio optimization for logarithmic utility. <br>In an incomplete market the no arbitrage criterion does not suffice to value contingent claims any more. Each equivalent martingale measure yields a possible price. Therefore additional criteria …
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Portfolio optimization with transaction costs and preconceived portfolio weights
… they apply this information to rebalancing their portfolios is often ad-hoc, trading off between rebalancing their assets into an allocation that generates the greatest expected return based on the generated signals and the incurred transaction costs that the reallocation will require. In this …
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Multiperiod portfolio optimization in the presence of transaction costs
Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, 1998.
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Robust estimation, regression and ranking with applications in portfolio optimization
… covariance estimation problem, we design an optimization model with a loss function on the weighted Mahalanobis distances and show that the problem is equivalent to a system of equations and can be solved using the Newton-Raphson method. The problem can also be transformed into an SDP problem …
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Portfolio Optimization Using a Hybrid Machine Learning Stock Selection Model
Portfolio Optimization can be challenging due to the uncertainty about the value of a future asset. With recent developments in machine learning, there are significant prediction tools available that can be applied to portfolio selection. Financial markets are known to be dynamic and complex, but …
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Multi-objective evolutionary methods for time-changing portfolio optimization problems
… with the use of evolutionary algorithms. The portfolio optimization problem is a multi-objective optimization problem for the conflicting criteria of risk and expected return. Furthermore the nonstationary nature of the market makes it a time-changing problem in which the optimal solution is …
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Efficient estimation for Markowitz's portfolio optimization by using random matrix theory
… the traditional (plug-in) return for the MV optimization is square of gamma times bigger than the theoretical optimal return, while under situations, the plug-in return is bigger than but may not be same times larger than its theoretic value with gamma......
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Application of Regime Switching and Random Matrix Theory for Portfolio Optimization
… of regime switches for stock market returns and portfolio optimisation. The key stylized facts regarding regime switching for stock index returns is that boom periods with positive mean stock returns are associated with low volatility, while bear markets with negative mean returns have high …
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