Machine learning applied in the stock market through the Moving Average Convergence Divergence (MACD) indicator

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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.

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    • Figure 1. Traditional form of the oscillator MACD
    • Figure 2. Genetic sequencing procedure followed in this study
    • Figure 3. Cumulative performance for GA, B&H, and TA strategies in the initial validation
    • Figure 4. Cumulative performance for GA, B&H and TA strategies in the final validation
    • Figure 5. Validation of signals across limits (lower and upper) for the MACD indicator
    • Table 1. Definition and properties of gens applied to MACD and general chromosome structure
    • Table 2. Buy & Sell strategy under GA, validation stages
    • Table 3. Effective annual return1 of GA, B&H, and TA strategies
    • Table 4. Oscillator parameters and values defined by the Genetic Algorithms
    • Conceptualization
      Alberto Antonio Agudelo Aguirre, Ricardo Alfredo Rojas Medina
    • Investigation
      Alberto Antonio Agudelo Aguirre, Ricardo Alfredo Rojas Medina
    • Methodology
      Alberto Antonio Agudelo Aguirre, Ricardo Alfredo Rojas Medina
    • Project administration
      Alberto Antonio Agudelo Aguirre
    • Supervision
      Alberto Antonio Agudelo Aguirre, Ricardo Alfredo Rojas Medina
    • Visualization
      Alberto Antonio Agudelo Aguirre
    • Writing – original draft
      Alberto Antonio Agudelo Aguirre
    • Writing – review & editing
      Alberto Antonio Agudelo Aguirre, Ricardo Alfredo Rojas Medina, Néstor Darío Duque Méndez
    • Data curation
      Ricardo Alfredo Rojas Medina
    • Formal Analysis
      Ricardo Alfredo Rojas Medina, Néstor Darío Duque Méndez
    • Validation
      Ricardo Alfredo Rojas Medina, Néstor Darío Duque Méndez