A Genetic Algorithm (GA) Approach to the Portfolio Design Based on Market Movements and Asset Valuations

Vulnerable nature of price forecasts, such as an unpredictability of future and numbers of socio-economic factors that affect market stability, often makes investment risky. Earlier studies in Finance suggested that constructing a portfolio can promise risk-spread gains. While Fund Standardization i...

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Main Authors: Sangmin Lim, Man-Je Kim, Chang Wook Ahn
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9152957/
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spelling doaj-f2c3facf33c641a4953a5e8d3eeec8242021-03-30T04:29:08ZengIEEEIEEE Access2169-35362020-01-01814023414024910.1109/ACCESS.2020.30130979152957A Genetic Algorithm (GA) Approach to the Portfolio Design Based on Market Movements and Asset ValuationsSangmin Lim0https://orcid.org/0000-0002-6393-7082Man-Je Kim1https://orcid.org/0000-0002-6402-2861Chang Wook Ahn2https://orcid.org/0000-0002-9902-5966School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South KoreaSchool of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South KoreaSchool of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South KoreaVulnerable nature of price forecasts, such as an unpredictability of future and numbers of socio-economic factors that affect market stability, often makes investment risky. Earlier studies in Finance suggested that constructing a portfolio can promise risk-spread gains. While Fund Standardization improved the traditional theories by reducing the computational complexity and by associating every interaction in the portfolio, such a method still cannot become a winning strategy because it does not measure the current value or the relative price of each asset. Inspired by the works of finding returns per risk, we attempt to design an optimal portfolio by searching products that have potential to grow further. More specifically, we first analyze risk-adjusted returns in the previous periods and use their inertia as a momentum. However, because historic movements alone do not fully elucidate future changes nor guarantee positive returns, we scored the relative values of each stock to make more informed estimations. Using the Capital Asset Pricing Model, we measured the values of each stock and determined those undervalued. In this study, we applied a Genetic Algorithm to optimize portfolios while incorporating the momentum strategy and the asset valuations. The proposed GA model was tested in two separate markets, S&P500 and KOSPI200, and projected greater profits than that from both the previous method with momentum method and the market indexes. From the experimental results, the proposed CAPM+ method was found to be very effective in financial data analysis and to lay a groundwork for a sustainable investment execution.https://ieeexplore.ieee.org/document/9152957/Genetic algorithmmachine learningportfolio optimizationmodern portfolio theoryinvestment strategySharpe ratio
collection DOAJ
language English
format Article
sources DOAJ
author Sangmin Lim
Man-Je Kim
Chang Wook Ahn
spellingShingle Sangmin Lim
Man-Je Kim
Chang Wook Ahn
A Genetic Algorithm (GA) Approach to the Portfolio Design Based on Market Movements and Asset Valuations
IEEE Access
Genetic algorithm
machine learning
portfolio optimization
modern portfolio theory
investment strategy
Sharpe ratio
author_facet Sangmin Lim
Man-Je Kim
Chang Wook Ahn
author_sort Sangmin Lim
title A Genetic Algorithm (GA) Approach to the Portfolio Design Based on Market Movements and Asset Valuations
title_short A Genetic Algorithm (GA) Approach to the Portfolio Design Based on Market Movements and Asset Valuations
title_full A Genetic Algorithm (GA) Approach to the Portfolio Design Based on Market Movements and Asset Valuations
title_fullStr A Genetic Algorithm (GA) Approach to the Portfolio Design Based on Market Movements and Asset Valuations
title_full_unstemmed A Genetic Algorithm (GA) Approach to the Portfolio Design Based on Market Movements and Asset Valuations
title_sort genetic algorithm (ga) approach to the portfolio design based on market movements and asset valuations
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2020-01-01
description Vulnerable nature of price forecasts, such as an unpredictability of future and numbers of socio-economic factors that affect market stability, often makes investment risky. Earlier studies in Finance suggested that constructing a portfolio can promise risk-spread gains. While Fund Standardization improved the traditional theories by reducing the computational complexity and by associating every interaction in the portfolio, such a method still cannot become a winning strategy because it does not measure the current value or the relative price of each asset. Inspired by the works of finding returns per risk, we attempt to design an optimal portfolio by searching products that have potential to grow further. More specifically, we first analyze risk-adjusted returns in the previous periods and use their inertia as a momentum. However, because historic movements alone do not fully elucidate future changes nor guarantee positive returns, we scored the relative values of each stock to make more informed estimations. Using the Capital Asset Pricing Model, we measured the values of each stock and determined those undervalued. In this study, we applied a Genetic Algorithm to optimize portfolios while incorporating the momentum strategy and the asset valuations. The proposed GA model was tested in two separate markets, S&P500 and KOSPI200, and projected greater profits than that from both the previous method with momentum method and the market indexes. From the experimental results, the proposed CAPM+ method was found to be very effective in financial data analysis and to lay a groundwork for a sustainable investment execution.
topic Genetic algorithm
machine learning
portfolio optimization
modern portfolio theory
investment strategy
Sharpe ratio
url https://ieeexplore.ieee.org/document/9152957/
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