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Forecasting gold price using machine learning methodologies
16 references·0 citing·48 second hop
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References

  • 2017Deep learning with long short-term memory networks for financial market predictions2,827 cited
  • 2020A CNN–LSTM model for gold price time-series forecasting869 cited
  • 2015A LSTM-based method for stock returns prediction: A case study of China stock market734 cited
  • 2016Dynamic spillover effects among crude oil, precious metal, and agricultural commodity futures markets575 cited
  • 2020Is Gold a Hedge or a Safe-Haven Asset in the COVID–19 Crisis?526 cited
  • 2018Predicting failure in the U.S. banking sector: An extreme gradient boosting approach295 cited
  • 2020Gold volatility prediction using a CNN-LSTM approach274 cited
  • 2020Prediction of cryptocurrency returns using machine learning265 cited
  • 2018An Ensemble of LSTM Neural Networks for High-Frequency Stock Market Classification230 cited
  • 2022Gold or Bitcoin, which is the safe haven during the COVID-19 pandemic?205 cited
  • 2022Forecasting directional movements of stock prices for intraday trading using LSTM and random forests187 cited
  • 2021Forecasting credit ratings of decarbonized firms: Comparative assessment of machine learning models141 cited
  • 2021Financial distress prediction using a corrected feature selection measure and gradient boosted decision tree131 cited
  • 2021Volatility and Return Connectedness of Cryptocurrency, Gold, and Uncertainty: Evidence from the Cryptocurrency Uncertainty Indices

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124 cited
  • 2021The hedge asset for BRICS stock markets: Bitcoin, gold or VIX119 cited
  • 2018Precious metal returns and oil shocks: A time varying connectedness approach106 cited
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