Please use this identifier to cite or link to this item:
|Scopus||Web of Science®||Altmetric|
|Title:||Evaluation of Intelligent Quantitative Hedge Fund Management|
|Citation:||Proceedings of the IEEE Congress on Evolutionary Computation, 2009: pp.2135-2142|
|Series/Report no.:||IEEE Congress on Evolutionary Computation|
|Conference Name:||IEEE Congress on Evolutionary Computation (2009 : Trondheim, Norway)|
|Muneer Buckley, Adam Ghandar, Zbigniew Michalewicz, Ralf Zurbruegg|
|Abstract:||This paper examines an intelligent recommendation strategy implementation for managing a long short hedge fund and reports on performance during market conditions at the onset of the liquidity crisis. A hedge fund utilizes long and short trading to manage an investment portfolio consisting of allocations to cash and share equity positions. This results in a combined long short portfolio that is leveraged to obtain a potentially greater market exposure with borrowed cash from short selling and is also hedged to protect against market downturns. The paper also examines effects of parameters for fuzzy rule base specification on trading performance.|
|Rights:||© 2009 IEEE|
|Appears in Collections:||Computer Science publications|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.