Machine learning applied in the stock market through the Moving Average Convergence Divergence (MACD) indicator
The implementation of tools such as Genetic Algorithms has not been exploited for asset price prediction despite their power, robustness, and potential application in the stock market. This paper aims to fill the gap existing in the literature on the use of Genetic Algorithms for predicting asset pr...
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| Format: | Article |
| Language: | English |
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LLC "CPC "Business Perspectives"
2020-11-01
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| Series: | Investment Management & Financial Innovations |
| Subjects: | |
| Online Access: | https://businessperspectives.org/images/pdf/applications/publishing/templates/article/assets/14191/IMFI_2020_04_Aguirre.pdf |
| _version_ | 1826908038316949504 |
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| author | Alberto Antonio Agudelo Aguirre Ricardo Alfredo Rojas Medina Néstor Darío Duque Méndez |
| author_facet | Alberto Antonio Agudelo Aguirre Ricardo Alfredo Rojas Medina Néstor Darío Duque Méndez |
| author_sort | Alberto Antonio Agudelo Aguirre |
| collection | DOAJ |
| description | The implementation of tools such as Genetic Algorithms has not been exploited for asset price prediction despite their power, robustness, and potential application in the stock market. This paper aims to fill the gap existing in the literature on the use of Genetic Algorithms for predicting asset pricing of investment strategies into stock markets and investigate its advantages over its peers Buy & Hold and traditional technical analysis. The Genetic Algorithms strategy applied to the MACD was carried out in two different validation periods and sought to optimize the parameters that generate the buy-sell signals. The performance between the machine learning-based approach, technical analysis with the MACD and B&H was compared. The results suggest that it is possible to find optimal values of the technical indicator parameters that result in a higher return on investment through Genetic Algorithms, beating the traditional technical analysis and B&H by around 4%. This study offers a new insight for practitioners, traders, and finance researchers to take advantage of Genetic Algorithms for trading rules application in forecasting financial asset returns under a more efficient and robust methodology based on historical data analysis. |
| first_indexed | 2024-12-13T03:12:27Z |
| format | Article |
| id | doaj.art-e6018d8ca3524098b4dc445f5dddf749 |
| institution | Directory Open Access Journal |
| issn | 1810-4967 1812-9358 |
| language | English |
| last_indexed | 2025-02-17T09:16:13Z |
| publishDate | 2020-11-01 |
| publisher | LLC "CPC "Business Perspectives" |
| record_format | Article |
| series | Investment Management & Financial Innovations |
| spelling | doaj.art-e6018d8ca3524098b4dc445f5dddf7492025-01-02T14:09:16ZengLLC "CPC "Business Perspectives"Investment Management & Financial Innovations1810-49671812-93582020-11-01174446010.21511/imfi.17(4).2020.0514191Machine learning applied in the stock market through the Moving Average Convergence Divergence (MACD) indicatorAlberto Antonio Agudelo Aguirre0https://orcid.org/0000-0001-6647-3482Ricardo Alfredo Rojas Medina1https://orcid.org/0000-0002-9135-2065Néstor Darío Duque Méndez2https://orcid.org/0000-0002-4608-281XPh.D. in Finance, Associate Professor, Administration Faculty, Business Administration Department, Universidad Nacional de Colombia, Sede ManizalesAssociate Professor, Administration Faculty, Business Administration Department, Universidad Nacional de Colombia, Sede ManizalesPh.D. in Engineering – Computing systems, Full Professor, Administration Faculty, Informatics and Computing Department, Universidad Nacional de Colombia, Sede ManizalesThe implementation of tools such as Genetic Algorithms has not been exploited for asset price prediction despite their power, robustness, and potential application in the stock market. This paper aims to fill the gap existing in the literature on the use of Genetic Algorithms for predicting asset pricing of investment strategies into stock markets and investigate its advantages over its peers Buy & Hold and traditional technical analysis. The Genetic Algorithms strategy applied to the MACD was carried out in two different validation periods and sought to optimize the parameters that generate the buy-sell signals. The performance between the machine learning-based approach, technical analysis with the MACD and B&H was compared. The results suggest that it is possible to find optimal values of the technical indicator parameters that result in a higher return on investment through Genetic Algorithms, beating the traditional technical analysis and B&H by around 4%. This study offers a new insight for practitioners, traders, and finance researchers to take advantage of Genetic Algorithms for trading rules application in forecasting financial asset returns under a more efficient and robust methodology based on historical data analysis.https://businessperspectives.org/images/pdf/applications/publishing/templates/article/assets/14191/IMFI_2020_04_Aguirre.pdfBuy & Holdequity investmentGenetic AlgorithmsMACDtechnical analysis |
| spellingShingle | Alberto Antonio Agudelo Aguirre Ricardo Alfredo Rojas Medina Néstor Darío Duque Méndez Machine learning applied in the stock market through the Moving Average Convergence Divergence (MACD) indicator Investment Management & Financial Innovations Buy & Hold equity investment Genetic Algorithms MACD technical analysis |
| title | Machine learning applied in the stock market through the Moving Average Convergence Divergence (MACD) indicator |
| title_full | Machine learning applied in the stock market through the Moving Average Convergence Divergence (MACD) indicator |
| title_fullStr | Machine learning applied in the stock market through the Moving Average Convergence Divergence (MACD) indicator |
| title_full_unstemmed | Machine learning applied in the stock market through the Moving Average Convergence Divergence (MACD) indicator |
| title_short | Machine learning applied in the stock market through the Moving Average Convergence Divergence (MACD) indicator |
| title_sort | machine learning applied in the stock market through the moving average convergence divergence macd indicator |
| topic | Buy & Hold equity investment Genetic Algorithms MACD technical analysis |
| url | https://businessperspectives.org/images/pdf/applications/publishing/templates/article/assets/14191/IMFI_2020_04_Aguirre.pdf |
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