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García, I. Marta Miranda; Segovia-Vargas, María-Jesús; … - In: Journal of forecasting 42 (2023) 5, pp. 1150-1166

Persistent link: https://www.econbiz.de/10014338829

Evaluating the effectiveness of modern forecasting models in predicting commodity futures prices in volatile economic times

Vancsura, László; Tatay, Tibor; Bareith, Tibor - In: Risks : open access journal 11 (2023) 2, pp. 1-16

calm economic environment, the estimation accuracy is higher (1.5% vs. 4%), and that the AI-based estimation methods …

Persistent link: https://www.econbiz.de/10014233184

Multistep forecast of the implied volatility surface using deep learning

Medvedev, Nikita; Wang, Zhiguang - In: The journal of futures markets 42 (2022) 4, pp. 645-667

Persistent link: https://www.econbiz.de/10013187579

Forecasting bitcoin volatility : exploring the potential of deep learning

Pratas, Tiago E.; Ramos, Filipe R.; Rubio, Lihki - In: Eurasian economic review : a journal in applied … 13 (2023) 2, pp. 285-305

Persistent link: https://www.econbiz.de/10014315637

Network-based prediction of financial cross-sector risk spillover in China : a deep learning approach

Tang, Pan; Xu, Wei; Wang, Haosen - In: The North American journal of economics and finance : a … 72 (2024), pp. 1-23

Persistent link: https://www.econbiz.de/10014534833

Explainable artificial intelligence and economic panel data : a study on volatility spillover along the supply chains

Berger, Theo - In: Finance research letters 54 (2023), pp. 1-6

Persistent link: https://www.econbiz.de/10014472730

Fast generation of implied volatility surface : optimize the traditional numerical analysis and machine learning

Yen, Jerome; Chen, Bangren; Wu, KangZahng; Yen, Joseph - In: International journal of financial engineering 8 (2021) 2, pp. 1-24

Persistent link: https://www.econbiz.de/10012662246

Market stability with machine learning agents

Georges, Christophre; Pereira, Javier - In: Journal of economic dynamics & control 122 (2021), pp. 1-19

Persistent link: https://www.econbiz.de/10012666119

Measuring China's stock market sentiment

Li, Jia; Chen, Yun; Shen, Yan; Wang, Jingyi; Huang, Zhuo - 2019

This paper develops textual sentiment measures for China's stock market by extracting the textual tone of 60 million messages posted on a major online investor forum in China from 2008 to 2018. We conduct sentiment extraction by using both conventional dictionary methods based on customized word...

Persistent link: https://www.econbiz.de/10012125620

Exploiting the low-risk anomaly using machine learning to enhance the Black-Litterman framework : evidence from South Korea

Pyo, Sujin; Lee, Jaewook - In: Pacific-Basin finance journal 51 (2018), pp. 1-12

Persistent link: https://www.econbiz.de/10012035052

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