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Text‐based soybean futures price forecasting: A two‐stage deep learning approach
16 references·0 citing·57 second hopsampled
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References

  • 2014Beyond the hype: Big data concepts, methods, and analytics4,409 cited
  • 2021Generación de series de tiempo financieras sintéticas para "data augmentation" usando redes neuronales generativas adversarias (GAN)1,559 cited
  • 2013Every tweet counts? How sentiment analysis of social media can improve our knowledge of citizens’ political preferences with an application to Italy and France511 cited
  • 2018High-frequency volatility connectedness between the US crude oil market and China's agricultural commodity markets219 cited
  • 2018Which online reviews do consumers find most helpful? A multi-method investigation193 cited
  • 2020A novel text-based framework for forecasting agricultural futures using massive online news headlines179 cited
  • 2019Using social network and semantic analysis to analyze online travel forums and forecast tourism demand156 cited
  • 2021Forecasting the U.S. oil markets based on social media information during the COVID-19 pandemic120 cited
  • 2015A combination method for interval forecasting of agricultural commodity futures prices116 cited
  • 2013How far do shocks move across borders? Examining volatility transmission in major agricultural futures markets111 cited
  • 2019Volatility forecasting in commodity markets using macro uncertainty96 cited
  • 2021Deep long short-term memory based model for agricultural price forecasting88 cited
  • 2022Forecasting Agricultural Commodity Prices Using Dual Input Attention LSTM

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87 cited
  • 2018Long-term stock index forecasting based on text mining of regulatory disclosures86 cited
  • 2018A combined neural network model for commodity price forecasting with SSA79 cited
  • 2016Forecasting food prices: The case of corn, soybeans and wheat75 cited
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