Portfolio algorithm based on portfolio beta using genetic algorithm

Kyong Joo Oh, Tae Yoon Kim, Sung Hwan Min, Hyoung Yong Lee

Research output: Contribution to journalArticle

60 Citations (Scopus)

Abstract

The portfolio beta βp is quite an important coefficient in modern portfolio theory since it efficiently measures portfolio volatility relative to the benchmark index or the capital market. βp is usually employed for portfolio evaluation or prediction but scarcely for portfolio construction process. The main objective of this paper is to propose a portfolio algorithm that engages βp in its portfolio construction process and studies its strengths. Our portfolio algorithm termed as β-G portfolio algorithm selects stocks based on their market capitalization and optimizes them in terms of the standard deviation of βp. The optimizing process or finding optimal weights is done by the genetic algorithm. Our major findings on β-G portfolio algorithm are: (i) its performance depends on market volatility, i.e. it is expected to work well for a stable market whether it is bullish or bearish (ii) it tends to register outstanding performance for short-term applications.

Original languageEnglish
Pages (from-to)527-534
Number of pages8
JournalExpert Systems with Applications
Volume30
Issue number3
DOIs
Publication statusPublished - 2006 Apr 1

Fingerprint

Genetic algorithms

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

Cite this

Oh, Kyong Joo ; Kim, Tae Yoon ; Min, Sung Hwan ; Lee, Hyoung Yong. / Portfolio algorithm based on portfolio beta using genetic algorithm. In: Expert Systems with Applications. 2006 ; Vol. 30, No. 3. pp. 527-534.
@article{044a8ed645ac4480a6b2b12639ca5b24,
title = "Portfolio algorithm based on portfolio beta using genetic algorithm",
abstract = "The portfolio beta βp is quite an important coefficient in modern portfolio theory since it efficiently measures portfolio volatility relative to the benchmark index or the capital market. βp is usually employed for portfolio evaluation or prediction but scarcely for portfolio construction process. The main objective of this paper is to propose a portfolio algorithm that engages βp in its portfolio construction process and studies its strengths. Our portfolio algorithm termed as β-G portfolio algorithm selects stocks based on their market capitalization and optimizes them in terms of the standard deviation of βp. The optimizing process or finding optimal weights is done by the genetic algorithm. Our major findings on β-G portfolio algorithm are: (i) its performance depends on market volatility, i.e. it is expected to work well for a stable market whether it is bullish or bearish (ii) it tends to register outstanding performance for short-term applications.",
author = "Oh, {Kyong Joo} and Kim, {Tae Yoon} and Min, {Sung Hwan} and Lee, {Hyoung Yong}",
year = "2006",
month = "4",
day = "1",
doi = "10.1016/j.eswa.2005.10.010",
language = "English",
volume = "30",
pages = "527--534",
journal = "Expert Systems with Applications",
issn = "0957-4174",
publisher = "Elsevier Limited",
number = "3",

}

Portfolio algorithm based on portfolio beta using genetic algorithm. / Oh, Kyong Joo; Kim, Tae Yoon; Min, Sung Hwan; Lee, Hyoung Yong.

In: Expert Systems with Applications, Vol. 30, No. 3, 01.04.2006, p. 527-534.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Portfolio algorithm based on portfolio beta using genetic algorithm

AU - Oh, Kyong Joo

AU - Kim, Tae Yoon

AU - Min, Sung Hwan

AU - Lee, Hyoung Yong

PY - 2006/4/1

Y1 - 2006/4/1

N2 - The portfolio beta βp is quite an important coefficient in modern portfolio theory since it efficiently measures portfolio volatility relative to the benchmark index or the capital market. βp is usually employed for portfolio evaluation or prediction but scarcely for portfolio construction process. The main objective of this paper is to propose a portfolio algorithm that engages βp in its portfolio construction process and studies its strengths. Our portfolio algorithm termed as β-G portfolio algorithm selects stocks based on their market capitalization and optimizes them in terms of the standard deviation of βp. The optimizing process or finding optimal weights is done by the genetic algorithm. Our major findings on β-G portfolio algorithm are: (i) its performance depends on market volatility, i.e. it is expected to work well for a stable market whether it is bullish or bearish (ii) it tends to register outstanding performance for short-term applications.

AB - The portfolio beta βp is quite an important coefficient in modern portfolio theory since it efficiently measures portfolio volatility relative to the benchmark index or the capital market. βp is usually employed for portfolio evaluation or prediction but scarcely for portfolio construction process. The main objective of this paper is to propose a portfolio algorithm that engages βp in its portfolio construction process and studies its strengths. Our portfolio algorithm termed as β-G portfolio algorithm selects stocks based on their market capitalization and optimizes them in terms of the standard deviation of βp. The optimizing process or finding optimal weights is done by the genetic algorithm. Our major findings on β-G portfolio algorithm are: (i) its performance depends on market volatility, i.e. it is expected to work well for a stable market whether it is bullish or bearish (ii) it tends to register outstanding performance for short-term applications.

UR - http://www.scopus.com/inward/record.url?scp=30944453897&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=30944453897&partnerID=8YFLogxK

U2 - 10.1016/j.eswa.2005.10.010

DO - 10.1016/j.eswa.2005.10.010

M3 - Article

AN - SCOPUS:30944453897

VL - 30

SP - 527

EP - 534

JO - Expert Systems with Applications

JF - Expert Systems with Applications

SN - 0957-4174

IS - 3

ER -