Machine learning applied in the stock market through the Moving Average Convergence Divergence (MACD) indicator
| Autor: | Alberto Antonio Agudelo Aguirre, Ricardo Alfredo Rojas Medina, Néstor Darío Duque Méndez |
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| Rok vydání: | 2020 |
| Předmět: |
Economics and Econometrics
Index (economics) Strategy and Management 02 engineering and technology MACD technical analysis Moving average lcsh:Finance lcsh:HG1-9999 0502 economics and business 0202 electrical engineering electronic engineering information engineering Econometrics equity investment Business and International Management Publication Mathematics 050208 finance business.industry Genetic Algorithms 05 social sciences Convergence divergence Buy & Hold 020201 artificial intelligence & image processing Stock market business Finance |
| Zdroj: | Investment Management & Financial Innovations, Vol 17, Iss 4, Pp 44-60 (2020) |
| ISSN: | 1812-9358 1810-4967 |
| DOI: | 10.21511/imfi.17(4).2020.05 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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