Abstract
Stock market prediction is a hard task even with the help of advanced machine learning algorithms and computational power. Although much research has been conducted in the field, the results often are not reproducible. That is the reason why the proposed workflow is publicly available on GitHub [1] as a continuous effort to help improve the research in the field. This study explores in detail the importance of financial time series technical indicators. Exploring new approaches and technical indicators, targets, feature selection techniques, and machine learning algorithms. Using data from multiple assets and periods, the proposed model adapts to market patterns to predict the future and using multiple supervised learning algorithms to ensure the adoption of different markets. The lack of research focusing on feature importance and the premise that technical indicators can improve prediction accuracy directed this research. The proposed approach highest accuracy reaches 75% with an area under the curve (AUC) of 0.82, using historical data up to 2019 to ensure the applicability for today’s market, with more than a hundred experiments on a diverse set of assets publicly available.
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Taha, A.K., Kholief, M.H., AbdelMoez, W. (2019). Adaptive Machine Learning-Based Stock Prediction Using Financial Time Series Technical Indicators. In: Yin, H., Camacho, D., Tino, P., Tallón-Ballesteros, A., Menezes, R., Allmendinger, R. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2019. IDEAL 2019. Lecture Notes in Computer Science(), vol 11872. Springer, Cham. https://doi.org/10.1007/978-3-030-33617-2_21
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