Paper Info
Title
Using memetic algorithms to improve portfolio performance in static and dynamic trading scenarios
Abstract
The Portfolio Optimization problem consists of the selection of a group of assets to a long-term fund in order to minimize the risk and maximize the return of the investment. This is a multi-objective (risk, return) resource allocation problem, where the aim is to correctly assign weights to the set of available assets, which determines the amount of capital to be invested in each asset. In this work, we introduce a Memetic Algorithm for portfolio optimization. Our system is based on a tree-structured genome representation which selects assets from the market and establish relationships between them, and a local hill climbing function which uses the information available from the tree-structure to calculate the weights of the selected assets. We use simulations based on historical data to test our system and compare it to previous approaches. In these experiments, our system shows that it is able to adapt to aggressive changes in the market, like the crash of 2008, with reduced trading cost.
Year
DOI
Venue
2009
10.1145/1569901.1570093
GECCO
Keywords
Field
DocType
portfolio performance,memetic algorithm,long-term fund,resource allocation problem,aggressive change,available asset,portfolio optimization problem,historical data,local hill,dynamic trading scenario,portfolio optimization,previous approach,operational research,operations research,resource allocation,tree structure,hill climbing
Memetic algorithm,Hill climbing,Crash,Mathematical optimization,Application portfolio management,Computer science,Portfolio,Portfolio optimization,Resource allocation,Black–Litterman model
Conference
Citations 
PageRank 
References 
7
0.62
9
Authors
2
Name
Order
Citations
PageRank
Claus de Castro Aranha1271.37
Hitoshi Iba21541138.51