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Krzysztof Drachal and Michal Pawlowski
This study firstly applied a Bayesian symbolic regression (BSR) to the forecasting of numerous commodities? prices (spot-based ones). Moreover, some features and an initial specification of the parameters of the BSR were analysed. The conventional approa...
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Mattia Pellegrino, Gianfranco Lombardo, George Adosoglou, Stefano Cagnoni, Panos M. Pardalos and Agostino Poggi
With the recent advances in machine learning (ML), several models have been successfully applied to financial and accounting data to predict the likelihood of companies? bankruptcy. However, time series have received little attention in the literature, w...
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Adriano Mancini, Francesco Solfanelli, Luca Coviello, Francesco Maria Martini, Serena Mandolesi and Raffaele Zanoli
Yield prediction is a crucial activity in scheduling agronomic operations and in informing the management and financial decisions of a wide range of stakeholders of the organic durum wheat supply chain. This research aims to develop a yield forecasting s...
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Suguru Sakuma and Tomoyuki Furutani
This study focuses on digital operational knowledge belonging to natural persons and proposes a greenfield approach to differentiate the value of intangibles from that of human intellectual capital. Our research approach involves two assessments. Assessm...
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Ilia Zaznov, Julian Martin Kunkel, Atta Badii and Alfonso Dufour
This paper introduces a novel deep learning approach for intraday stock price direction prediction, motivated by the need for more accurate models to enable profitable algorithmic trading. The key problems addressed are effectively modelling complex limi...
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Enrique González-Núñez, Luis A. Trejo and Michael Kampouridis
This research aims at applying the Artificial Organic Network (AON), a nature-inspired, supervised, metaheuristic machine learning framework, to develop a new algorithm based on this machine learning class. The focus of the new algorithm is to model and ...
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Caosen Xu, Jingyuan Li, Bing Feng and Baoli Lu
Financial time-series prediction has been an important topic in deep learning, and the prediction of financial time series is of great importance to investors, commercial banks and regulators. This paper proposes a model based on multiplexed attention me...
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Apostolos Ampountolas
This study analyzes the transmission of market uncertainty on key European financial markets and the cryptocurrency market over an extended period, encompassing the pre-, during, and post-pandemic periods. Daily financial market indices and price observa...
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Francisco J. Soltero, Pablo Fernández-Blanco and J. Ignacio Hidalgo
Technical indicators use graphic representations of datasets by applying various mathematical formulas to financial time series of prices. These formulas comprise a set of rules and parameters whose values are not necessarily known and depend on many fac...
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Amal Al Ali, Ahmed M. Khedr, Magdi El Bannany and Sakeena Kanakkayil
Despite the obvious benefits and growing popularity of Machine Learning (ML) technology, there are still concerns regarding its ability to provide Financial Distress Prediction (FDP). An accurate FDP model is required to avoid financial risk at the lowes...
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