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Forecasting the U.S. oil markets based on social media information during the COVID-19 pandemic
10 references·13 citing·56 second hop
Forecasting the U.S. oil markets based on social media information during the COVID-19 pandemic (2021)Forecasting the U.S. oil markets based on…Review of tourism forecasting research with internet data (2020)Review of tourism forecasting…COVID-19 pandemic, oil prices, stock market, geopolitical risk and policy uncertainty nexus in the US economy: Fresh evidence from the wavelet-based approach (2020)COVID-19 pandemic, oil prices…A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series (2020)A new forecasting model with …Machine learning in energy economics and finance: A review (2019)Machine learning in energy ec…Text-based crude oil price forecasting: A deep learning approach (2018)Text-based crude oil price fo…Online big data-driven oil consumption forecasting with Google trends (2018)Online big data-driven oil co…A hybrid model based on selective ensemble for energy consumption forecasting in China (2018)A hybrid model based on selec…A novel decomposition ensemble model with extended extreme learning machine for crude oil price forecasting (2015)A novel decomposition ensembl…User engagement with online news: Conceptualizing interactivity and exploring the relationship between online news videos and user comments (2014)User engagement with online n…Oil price dynamics and speculation (2009)Oil price dynamics and specul…MCDFN: supply chain demand forecasting via an explainable multi-channel data fusion network model (2025)MCDFN: supply chain demand fo…Novel deterministic and probabilistic forecasting methods for crude oil price employing optimized deep learning, statistical and hybrid models (2023)Novel deterministic and proba…More attention and better volatility forecast accuracy: How does war attention affect stock volatility predictability? (2023)More attention and better vol…Can multi-source heterogeneous data improve the forecasting performance of tourist arrivals amid COVID-19? 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this work references cited by second hop

References

  • 2020COVID-19 pandemic, oil prices, stock market, geopolitical risk and policy uncertainty nexus in the US economy: Fresh evidence from the wavelet-based approach1,488 cited
  • 2020A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series481 cited
  • 2019Machine learning in energy economics and finance: A review438 cited
  • 2014User engagement with online news: Conceptualizing interactivity and exploring the relationship between online news videos and user comments321 cited
  • 2018Text-based crude oil price forecasting: A deep learning approach305 cited
  • 2009Oil price dynamics and speculation274 cited
  • 2018Online big data-driven oil consumption forecasting with Google trends236 cited
  • 2015A novel decomposition ensemble model with extended extreme learning machine for crude oil price forecasting204 cited
  • 2020Review of tourism forecasting research with internet data194 cited
  • 2018A hybrid model based on selective ensemble for energy consumption forecasting in China126 cited

Cited by

  • 2021Immunizing markets against the pandemic: COVID-19 vaccinations and stock volatility around the world139 cited
  • 2022Applications of deep learning into supply chain management: a systematic literature review and a framework for future research98 cited
  • 2025MCDFN: supply chain demand forecasting via an explainable multi-channel data fusion network model

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67 cited
  • 2023More attention and better volatility forecast accuracy: How does war attention affect stock volatility predictability?66 cited
  • 2023Can multi-source heterogeneous data improve the forecasting performance of tourist arrivals amid COVID-19? Mixed-data sampling approach58 cited
  • 2022Interpretable tourism demand forecasting with temporal fusion transformers amid COVID-1947 cited
  • 2023Novel deterministic and probabilistic forecasting methods for crude oil price employing optimized deep learning, statistical and hybrid models44 cited
  • 2023A comparative study on effect of news sentiment on stock price prediction with deep learning architecture42 cited
  • 2021Forecasting Crude Oil Price Using Event Extraction41 cited
  • 2021COVID-19 Pandemic and Romanian Stock Market Volatility: A GARCH Approach40 cited
  • 2023Application of social media in supply chain 4.0 practices: a bibliometric analysis and research trends35 cited
  • 2023A Conceptual Model of Investment-Risk Prediction in the Stock Market Using Extreme Value Theory with Machine Learning: A Semisystematic Literature Review35 cited
  • 2021A Novel Deep Neural Network-Based Approach to Measure Scholarly Research Dissemination Using Citations Network12 cited