Separating the signal from the noise -- financial machine learning for Twitter

DOI10.1016/j.jedc.2020.103895OpenAlexW2917431595MaRDI QIDQ2191464

Thomas G. Fischer, Christopher Krauss, Matthias Schnaubelt

Publication date: 25 June 2020

Published in: Journal of Economic Dynamics \& Control (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/10419/191256

zbMATH Keywords

natural language processingfinancemachine learningstatistical arbitrage

Mathematics Subject Classification ID

Game theory, economics, finance, and other social and behavioral sciences (91-XX)

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Uses Software

• NLTK
• R
• NumPy
• Scikit
• ElemStatLearn
• Python
• PostgreSQL
• Adam

Cites Work

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• Heterogeneous trading strategies with adaptive fuzzy actor-critic reinforcement learning: a behavioral approach
• Deep learning with long short-term memory networks for financial market predictions
• Moment matching machine learning methods for risk management of large variable annuity portfolios
• Deep neural networks, gradient-boosted trees, random forests: statistical arbitrage on the S\&P 500
• Large data sets and machine learning: applications to statistical arbitrage
• A random forest guided tour
• Statistical arbitrage in the US equities market
• Exploiting social media with higher-order Factorization Machines: statistical arbitrage on high-frequency data of the S&P 500
• Random forests

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