Portfolio Optimization Based on Funds Standardization and Genetic Algorithm
When investing in the stock market, the first problem and one of paramount importance which investors have to face is making the proper stock selection. Selecting the stocks that simultaneously offer high return and low risk is a difficult problem that is worth investigating. However, the traditiona...
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| Format: | Article |
| Language: | English |
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IEEE
2017-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8049455/ |
| _version_ | 1818617910833184768 |
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| author | Yao-Hsin Chou Shu-Yu Kuo Yi-Tzu Lo |
| author_facet | Yao-Hsin Chou Shu-Yu Kuo Yi-Tzu Lo |
| author_sort | Yao-Hsin Chou |
| collection | DOAJ |
| description | When investing in the stock market, the first problem and one of paramount importance which investors have to face is making the proper stock selection. Selecting the stocks that simultaneously offer high return and low risk is a difficult problem that is worth investigating. However, the traditional risk calculation based on the modern portfolio theory (MPT) of portfolios has some defects. The MPT method requires the calculations of every relationship between each pair of stocks in the portfolio, entailing high computation complexity, which grows exponentially with the increased number of stocks. Besides, the traditional calculation is unable to calculate the coefficient of variation, and merely considers the relationship between each pair of stocks, so it cannot accurately assess portfolio risk. Therefore, this paper proposes a novel method, funds standardization, and utilizes it to represent the portfolio return and calculate portfolio risk. The fluctuation of portfolio funds standardization shows not only the relationships between each pair of stocks, but also the interactions among all stocks. Hence, utilizing funds standardization can accurately assess portfolio risk and completely represent the mood swings of investors. Compared with the traditional method, the proposed method significantly reduces the computation complexity because the complexity does not increase when the portfolios stock number increases. We combine the genetic algorithm, Sharpe ratio and funds standardization to find the optimal portfolio. In addition, we utilize the sliding window to avoid the over-fitting problem, which is common in this field, and test the effect of all kinds of training and testing periods. The experimental results show that the portfolio can spread the risk effectively, and that the portfolio risk can be assessed accurately by utilizing the funds standardization. Comparing with the traditional method, our method can identify the optimal portfolio efficiently and establish a portfolio that has lower risk and stable return. |
| first_indexed | 2024-12-16T17:13:12Z |
| format | Article |
| id | doaj.art-6c62bea3921141f4bef69c1b9b8a81e0 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T17:13:12Z |
| publishDate | 2017-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-6c62bea3921141f4bef69c1b9b8a81e02022-12-21T22:23:22ZengIEEEIEEE Access2169-35362017-01-015218852190010.1109/ACCESS.2017.27568428049455Portfolio Optimization Based on Funds Standardization and Genetic AlgorithmYao-Hsin Chou0https://orcid.org/0000-0002-3254-5510Shu-Yu Kuo1Yi-Tzu Lo2National Chi Nan University, Puli, TaiwanNational Chi Nan University, Puli, TaiwanNational Chi Nan University, Puli, TaiwanWhen investing in the stock market, the first problem and one of paramount importance which investors have to face is making the proper stock selection. Selecting the stocks that simultaneously offer high return and low risk is a difficult problem that is worth investigating. However, the traditional risk calculation based on the modern portfolio theory (MPT) of portfolios has some defects. The MPT method requires the calculations of every relationship between each pair of stocks in the portfolio, entailing high computation complexity, which grows exponentially with the increased number of stocks. Besides, the traditional calculation is unable to calculate the coefficient of variation, and merely considers the relationship between each pair of stocks, so it cannot accurately assess portfolio risk. Therefore, this paper proposes a novel method, funds standardization, and utilizes it to represent the portfolio return and calculate portfolio risk. The fluctuation of portfolio funds standardization shows not only the relationships between each pair of stocks, but also the interactions among all stocks. Hence, utilizing funds standardization can accurately assess portfolio risk and completely represent the mood swings of investors. Compared with the traditional method, the proposed method significantly reduces the computation complexity because the complexity does not increase when the portfolios stock number increases. We combine the genetic algorithm, Sharpe ratio and funds standardization to find the optimal portfolio. In addition, we utilize the sliding window to avoid the over-fitting problem, which is common in this field, and test the effect of all kinds of training and testing periods. The experimental results show that the portfolio can spread the risk effectively, and that the portfolio risk can be assessed accurately by utilizing the funds standardization. Comparing with the traditional method, our method can identify the optimal portfolio efficiently and establish a portfolio that has lower risk and stable return.https://ieeexplore.ieee.org/document/8049455/PortfolioSharpe ratiostock selectionfunds standardizationlow volatilityGenetic algorithm (GA) |
| spellingShingle | Yao-Hsin Chou Shu-Yu Kuo Yi-Tzu Lo Portfolio Optimization Based on Funds Standardization and Genetic Algorithm IEEE Access Portfolio Sharpe ratio stock selection funds standardization low volatility Genetic algorithm (GA) |
| title | Portfolio Optimization Based on Funds Standardization and Genetic Algorithm |
| title_full | Portfolio Optimization Based on Funds Standardization and Genetic Algorithm |
| title_fullStr | Portfolio Optimization Based on Funds Standardization and Genetic Algorithm |
| title_full_unstemmed | Portfolio Optimization Based on Funds Standardization and Genetic Algorithm |
| title_short | Portfolio Optimization Based on Funds Standardization and Genetic Algorithm |
| title_sort | portfolio optimization based on funds standardization and genetic algorithm |
| topic | Portfolio Sharpe ratio stock selection funds standardization low volatility Genetic algorithm (GA) |
| url | https://ieeexplore.ieee.org/document/8049455/ |
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