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Mean-Risk Portfolio Optimization with Prior PCA-Based Stock Selection
Company: International Workshop on Stochastic Programming for Implementation and Advanced Applications
Year Of Publication: 2012
Month Of Publication: July
Pages: 37-42
Download Count: 3
View Count: 1531
Comment Num: 0
Language: English
Source: conference proceedings
Who Can Read: Free
Date: 9-20-2012
Publisher: Administrator
In this paper, we develop an integrated portfolio selection method with risk estimation and optimization in the mean-risk framework. The algorithm proposed follows three steps. The first one, stock selection, uses Principal Component Analysis (PCA) and clustering techniques to build classes of similar assets. In the second, Value-at-Risk measure (VaR) is used to evaluate risk and the asset with minimal VaR is selected from each class. In the third step the assets selected previously are used to build the optimal portfolio, solving a mean-VaR optimization problem. The original nature of our work stands in combining classification theory with risk estimation and optimization techniques based on VaR risk measure. The optimization process improves, because it starts with an initial portfolio composed by a wide range of assets with minimal risk. This method will be used to build an optimal portfolio of assets from Bucharest Stock Exchange and to derive the efficient frontier.
Dedu, Silvia Sign in to follow this author
Fulga, Cristinca Sign in to follow this author
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