Abstract
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.
Highlights
Understanding the behavior of the price of financial assets and its prediction for the future has historically been a major challenge faced by practitioners
Technical analysis is based on the argument that macroeconomic information and disclosed financial news is already regarded in stock prices (for a more detailed study on state of the art in the stock market forecast based on fundamentals and technical approaches (Bustos & Pomares-Quimbaya, 2020)
This study aims to develop an investment strategy in a variable income asset to evaluate the efficiency in returns that could be generated with a Genetic Algorithms (GA)-based strategy using Moving Average Convergence/Divergence (MACD) technical indicator
Summary
Understanding the behavior of the price of financial assets and its prediction for the future has historically been a major challenge faced by practitioners. This study suggested that it functions have been included with genetic prois possible to increase yields even in markets with gramming in the solution framework to capa downward trend from technical indicators’ con- ture the nonlinearity derived from this irregular struction with found parameters and different tendency. In this regard, Ding, Cui, Xiong, and from the standardized indicators used by tradi- Bai (2020) used lagged returns as predictors and tional technical analysis
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