@inproceedings{bd8d61ebc49d4c7ba8817b72733523a6,
title = "Portfolio Optimisation via Graphical Least Squares Estimation",
abstract = "In this paper, an unbiased estimation method called GLSE (proposed by Aldahmani and Dai[1]) for solving the linear regression problem in high-dimensional data is applied to portfolio optimisation under the linear regression framework and compared to the ridge method. The unbiasedness of method helps in improving the portfolio performance by increasing its expected return and decreasing the associated risk when, thus leading to a maximisation of the Sharpe ratio. The verification of this method is achieved through conducting simulation and data analysis studies and comparing the results with those of ridge regression. It is found that GLSE outperforms ridge in portfolio optimisation when.",
keywords = "Graphical model, Linear regression, Ridge regression",
author = "Saeed Aldahmani and Hongsheng Dai and Zhang, \{Qiao Zhen\} and Marialuisa Restaino",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 4th Conference of the International Society for Nonparametric Statistics, ISNPS 2018 ; Conference date: 11-06-2018 Through 15-06-2018",
year = "2020",
doi = "10.1007/978-3-030-57306-5\_1",
language = "English",
isbn = "9783030573058",
series = "Springer Proceedings in Mathematics and Statistics",
publisher = "Springer",
pages = "1--9",
editor = "\{La Rocca\}, Michele and Brunero Liseo and Luigi Salmaso",
booktitle = "Nonparametric Statistics - 4th ISNPS 2018",
}