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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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Anuwat Boonprasope and Korrakot Yaibuathet Tippayawong
Following the COVID-19 pandemic, the healthcare sector has emerged as a resilient and profitable domain amidst market fluctuations. Consequently, investing in healthcare securities, particularly through mutual funds, has gained traction. Existing researc...
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Ive Botunac, Jurica Bosna and Maja Matetic
Investment decision-makers increasingly rely on modern digital technologies to enhance their strategies in today?s rapidly changing and complex market environment. This paper examines the impact of incorporating Long Short-term Memory (LSTM) models into ...
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Kang Cao, Yongjie Zhang and Jianfei Feng
As aviation technology advances, numerous new aircraft enter the market. These not only offer airlines technological and fuel efficiency advantages but also present the challenge of how to conduct pilots? aircraft-type transition training efficiently and...
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Kittipob Saetia and Jiraphat Yokrattanasak
Machine learning for stock market prediction has recently been popular for identifying stock selection strategies and providing market insights. In this study, we adopted machine learning algorithms to analyze technical indicators, and Google Trends sear...
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Abdellilah Nafia, Abdellah Yousfi and Abdellah Echaoui
In recent years, a great deal of attention has been devoted to the use of neural networks in portfolio management, particularly in the prediction of stock prices. Building a more profitable portfolio with less risk has always been a challenging task. In ...
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George Fragiadakis, Evangelia Filiopoulou, Christos Michalakelis, Thomas Kamalakis and Mara Nikolaidou
When exploring alternative cloud solution designs, it is important to also consider cost. Thus, having a comprehensive view of the cloud market and future price evolution allows well-informed decisions to choose between alternatives. Cloud providers offe...
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Silvia Golia, Luigi Grossi and Matteo Pelagatti
In this paper we assess how intra-day electricity prices can improve the prediction of zonal day-ahead wholesale electricity prices in Italy. We consider linear autoregressive models with exogenous variables (ARX) with and without interactions among pred...
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Alamir Labib Awad, Saleh Mesbah Elkaffas and Mohammed Waleed Fakhr
Stock value prediction and trading, a captivating and complex research domain, continues to draw heightened attention. Ensuring profitable returns in stock market investments demands precise and timely decision-making. The evolution of technology has int...
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Nika Nizharadze, Arash Farokhi Soofi and Saeed Manshadi
Predicting the price gap between the day-ahead Market (DAM) and the real-time Market (RTM) plays a vital role in the convergence bidding mechanism of Independent System Operators (ISOs) in wholesale electricity markets. This paper presents a model to pre...
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