Title :
Weight shrinkage for portfolio optimization
Author_Institution :
Sch. of Electr. & Comput. Eng., Purdue Univ., West Lafayette, IN, USA
Abstract :
The paper starts by reviewing the basics of the modern portfolio theory and its very well known drawbacks. After a brief overview of the existing literature that attempts to address these drawbacks, a novel portfolio mixing method is proposed. The method is then illustrated using US stock market data, and is shown to outperform both portfolios that it combines in a statistically significant way. Several avenues of further research are summarized to conclude the paper.
Keywords :
investment; optimisation; US stock market data; portfolio mixing method; portfolio optimization; portfolio theory; weight shrinkage; Covariance matrix; Educational institutions; Estimation; Finance; Optimization; Portfolios; Vectors; Markowitz; Portfolios; covariance; diversification; finance; market; shrinkage; stock;
Conference_Titel :
Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2011 4th IEEE International Workshop on
Conference_Location :
San Juan
Print_ISBN :
978-1-4577-2104-5
DOI :
10.1109/CAMSAP.2011.6136031
