Abstract
This paper introduces an innovative paradigm in cryptocurrency market analysis and prediction by exploiting the potency of the gradient boosting neural network (GBNN). This pioneering machine learning model amalgamates neural networks and gradient boosting techniques to offer a robust methodology. To enhance the GBNN's predictive capabilities, we enriched its input data with a spectrum of technical indicators. Moreover, we employed the support vector regressor for feature engineering, contributing to the exclusion of insignificant variables. We coined the term "hybrid approach" to describe our pipeline, employing it to train the GBNN model using historical cryptocurrency data. A multitude of experiments were conducted to demonstrate the superior performance of our approach in terms of model accuracy and error on previously unseen data. Notably, our proposed method outperformed state-of-the-art machine learning models, showcasing its effectiveness.
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Data Availability
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. The data underpinning the conclusions of this study can be accessed through publicly available sources, namely [finance.yahoo.com/–coinmarketcap.com/]. For interested parties, the methods, data, and codes utilized in this research can be provided upon request. The data that support the findings of this study are available from the corresponding author upon request. Data citation: The data underpinning the conclusions of this study can be accessed through publicly available sources, namely [finance.yahoo.com/]. For those interested, the implementation, data, and codes used in this research are accessible to the public on GitHub. Could be found them at https://github.com/tararjc/GBNN_crypto.
Notes
The implanted code utilized in this research will be provided upon request.
statsmodels.org/.
github.com/GAA-UAM/GBNN/.
scikit-learn.org/stable/.
github.com/tararjc/GBNN_crypto.
GridSearchCV.
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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Taraneh Shahin, María Teresa Ballestar de las Heras, and Ismael Sanz Labrador. The first draft of the manuscript was written by Taraneh Shahin, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Shahin, T., Ballestar de las Heras, M.T. & Sanz, I. Enhancing Stock Market Prediction Using Gradient Boosting Neural Network: A Hybrid Approach. Comput Econ (2024). https://doi.org/10.1007/s10614-024-10671-9
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DOI: https://doi.org/10.1007/s10614-024-10671-9