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

59 Citations (Scopus)

Abstract

The portfolio beta β p is quite an important coefficient in modern portfolio theory since it efficiently measures portfolio volatility relative to the benchmark index or the capital market. β p 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 β p 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 β p . 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

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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.
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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

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