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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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Markus Frohmann, Manuel Karner, Said Khudoyan, Robert Wagner and Markus Schedl
Recently, various methods to predict the future price of financial assets have emerged. One promising approach is to combine the historic price with sentiment scores derived via sentiment analysis techniques. In this article, we focus on predicting the f...
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Apostolos Ampountolas
Over the past years, cryptocurrencies have drawn substantial attention from the media while attracting many investors. Since then, cryptocurrency prices have experienced high fluctuations. In this paper, we forecast the high-frequency 1 min volatility of...
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Nouha Dkhili, Julien Eynard, Stéphane Thil and Stéphane Grieu
In a context of accelerating deployment of distributed generation in power distribution grid, this work proposes an answer to an important and urgent need for better management tools in order to ?intelligently? operate these grids and maintain quality of...
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Jying-Nan Wang,Yuan-Teng Hsu,Hung-Chun Liu
Pág. 651 - 656
Given the rapid growth of financial markets over the past 20 years, along with the explosive development of financial derivatives, an ever-growing need for accurate and efficient volatility forecasting has emerged. Such forecasts have numerous financial ...
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Paulo Sérgio Ceretta,Fernanda Galvão de Barba,Kelmara Mendes Vieira,Fernando Casarin
Pág. 209 - 226
Volatility forecasting has been of great interest both in academic and professional fields all over the world. However, there is no agreement about the best model to estimatevolatility. New models include measures of skewness, changes of regimes and diff...
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