Portfolio algorithm based on portfolio beta using genetic algorithm

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

doi:10.1016/j.eswa.2005.10.010

Portfolio algorithm based on portfolio beta using genetic algorithm

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

Department of Industrial Engineering

Research output: Contribution to journal › Article › peer-review

20 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 language	English
Pages (from-to)	527-534
Number of pages	8
Journal	Expert Systems with Applications
Volume	30
Issue number	3
DOIs	https://doi.org/10.1016/j.eswa.2005.10.010
Publication status	Published - 2006 Apr

Bibliographical note

Funding Information:
We are very grateful to two anonymous referees for their valuable comments which led to significant improvement of our manuscript. This work is supported by Korea Science and Engineering Fund (KOSEF R01-2003-000- 105890-0).

All Science Journal Classification (ASJC) codes

Engineering(all)
Computer Science Applications
Artificial Intelligence

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10.1016/j.eswa.2005.10.010

Cite this

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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}",

note = "Funding Information: We are very grateful to two anonymous referees for their valuable comments which led to significant improvement of our manuscript. This work is supported by Korea Science and Engineering Fund (KOSEF R01-2003-000- 105890-0).",

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AU - Oh, Kyong Joo

AU - Kim, Tae Yoon

AU - Min, Sung Hwan

AU - Lee, Hyoung Yong

N1 - Funding Information: We are very grateful to two anonymous referees for their valuable comments which led to significant improvement of our manuscript. This work is supported by Korea Science and Engineering Fund (KOSEF R01-2003-000- 105890-0).

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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.

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Portfolio algorithm based on portfolio beta using genetic algorithm

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

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