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A1497
Title: Hybrid graphical least square estimation and its application in portfolio selection Authors:  Saeed Aldahmani - United Arab Emirates University (United Arab Emirates) [presenting]
Hongsheng Dai - University of Essex (United Kingdom)
Qiaozhen Zhang - Nankai university (China)
Abstract: A novel regression method based on the idea of graphical models is proposed to deal with the portfolio optimisation problem within the Markowitz mean-variance framework, when the number of assets $V$ is larger than the sample size $N$. Unlike the regularisation methods such as ridge regression, LASSO and LARS, which give biased estimates,the newly proposed method can yield unbiased estimates for important variables, which contributes to improving the portfolio's Sharpe ratio by increasing its expected returns and decreasing its risk. Another characteristic of the new approach is that it produces a non-sparse portfolio that is more diversified in terms of stocks and reduces the stock-specific risk. To assess the proposed method, analysis of real data from London Stock Exchange and three simulation scenarios were carried out and the results were compared with those obtained from ridge regression. It is revealed that the proposed method significantly outperforms ridge regression in constructing portfolios with a higher Sharpe ratio for in-and-out-of-sample performance.