Abstract
The stock’s closing price is the standard benchmark used by investors to track the stock performance over time. In particular, understanding the trend of the stock’s closing prices is of fundamental importance to choose investments carefully. In this paper, we address the task of forecasting closing prices of Exprivia S.p.A.’s stock by comparing the performance of both traditional and deep machine learning methods. Preliminary experiments show that the multi-variate setting can significantly outperform the univariate one and that deep learning can gain accuracy compared to traditional machine learning methods in the considered task.
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Preliminary experiments have been performed to set the size of this layer.
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Acknowledgement
We acknowledge Digital Factory Fintech and Insurtech of Exprivia S.p.A for funding the work of Michele Spagnoletta. The research of Matteo Greco has been performed during his stage in Exprivia S.p.A. The research of Annalisa Appice and Donato Malerba is in partial fulfilment of theresearch objectives of the research project “Modelli e tecniche di data science per la analisi di dati strutturati” (Models and techniques of data science for the analysis of structured data) funded by University of Bari Aldo Moro.
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Greco, M., Spagnoletta, M., Appice, A., Malerba, D. (2021). Applying Machine Learning to Predict Closing Prices in Stock Market: A Case Study. In: Bitetta, V., Bordino, I., Ferretti, A., Gullo, F., Ponti, G., Severini, L. (eds) Mining Data for Financial Applications. MIDAS 2020. Lecture Notes in Computer Science(), vol 12591. Springer, Cham. https://doi.org/10.1007/978-3-030-66981-2_3
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