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Text-based crude oil price forecasting: A deep learning approach
11 references·16 citing·47 second hop
Text-based crude oil price forecasting: A deep learning approach (2018)Text-based crude oil price forecasting: A…Sentiment analysis on social media for stock movement prediction (2015)Sentiment analysis on social …Textual analysis of stock market prediction using breaking financial news (2009)Textual analysis of stock mar…Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market (2009)Not All Oil Price Shocks Are …Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm (2008)Forecasting crude oil price w…Giving Content to Investor Sentiment: The Role of Media in the Stock Market (2007)Giving Content to Investor Se…Oil prices and real exchange rates (2006)Oil prices and real exchange …A Practitioner's Guide to Lag Order Selection For VAR Impulse Response Analysis (2005)A Practitioner's Guide to Lag…Oil price shocks and stock market activity (1999)Oil price shocks and stock ma…Postwar U.S. Business Cycles: An Empirical Investigation (1997)Postwar U.S. Business Cycles:…Distribution of the Estimators for Autoregressive Time Series with a Unit Root (1979)Distribution of the Estimator…Investigating Causal Relations by Econometric Models and Cross-spectral Methods (1969)Investigating Causal Relation…Price forecasting through neural networks for crude oil, heating oil, and natural gas (2024)Price forecasting through neu…Financial Time Series Forecasting with the Deep Learning Ensemble Model (2023)Financial Time Series Forecas…Investor sentiment and machine learning: Predicting the price of China's crude oil futures market (2022)Investor sentiment and machin…Forecasting crude oil market volatility using variable selection and common factor (2022)Forecasting crude oil market …Forecasting crude oil futures market returns: A principal component analysis combination approach (2022)Forecasting crude oil futures…The role of news sentiment in oil futures returns and volatility forecasting: Data-decomposition based deep learning approach (2021)The role of news sentiment in…Forecasting the U.S. oil markets based on social media information during the COVID-19 pandemic (2021)Forecasting the U.S. oil mark…Forecasting crude oil prices based on variational mode decomposition and random sparse Bayesian learning (2021)Forecasting crude oil prices …Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization (2021)Crude oil time series predict…Crude oil price forecasting incorporating news text (2021)Crude oil price forecasting i…A combined architecture of multivariate LSTM with Mahalanobis and Z-Score transformations for oil price forecasting (2021)A combined architecture of mu…The role of text-extracted investor sentiment in Chinese stock price prediction with the enhancement of deep learning (2020)The role of text-extracted in…Energy price prediction using data-driven models: A decade review (2020)Energy price prediction using…Effective crude oil price forecasting using new text-based and big-data-driven model (2020)Effective crude oil price for…Analyzing Crude Oil Prices under the Impact of COVID-19 by Using LSTARGARCHLSTM (2020)Analyzing Crude Oil Prices un…A novel text-based framework for forecasting agricultural futures using massive online news headlines (2020)A novel text-based framework …The Adjustment of Stock Prices to New Information (1969)Efficient Capital Markets: II (1991)Twitter mood predicts the stock market (2011)The impact of microblogging data for stock market prediction: Using Twitter to predict returns, volatility, trading volume and survey sentiment indices (2016)Stock Market Analysis: A Review and Taxonomy of Prediction Techniques (2019)The predictive power of public Twitter sentiment for forecasting cryptocurrency prices (2020)A Monetary History of the United States 1867-1960 (1964)Time Series Analysis Forecasting and Control. 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(1936)Macroeconomics and Reality (1980)The Band Pass Filter (1999)On Adjusting the Hodrick-Prescott Filter for the Frequency of Observations (2002)Measuring Uncertainty (2015)A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test (1999)Unit root tests in panel data: asymptotic and finite-sample properties (2002)Testing for unit roots in heterogeneous panels (2003)Spillover effect of US dollar exchange rate on oil prices (2008)Modelling oil price and exchange rate co-movements (2011)How COVID-19 drives connectedness among commodity and financial markets: Evidence from TVP-VAR and causality-in-quantiles techniques (2020)Atheoretical macroeconometrics: A critique (1985)Systematic Monetary Policy and the Effects of Oil Price Shocks (1997)THE ROLE OF INVENTORIES AND SPECULATIVE TRADING IN THE GLOBAL MARKET FOR CRUDE OIL (2013)Common risk factors in the returns on stocks and bonds (1993)More Than Words: Quantifying Language to Measure Firms' Fundamentals (2008)In Search of Attention (2011)Textual Analysis in Accounting and Finance: A Survey (2016)Oil and the Macroeconomy since World War II (1983)Testing for Granger non-causality in heterogeneous panels (2012)
this work references cited by second hop

References

  • 1979Distribution of the Estimators for Autoregressive Time Series with a Unit Root23,183 cited
  • 1969Investigating Causal Relations by Econometric Models and Cross-spectral Methods22,971 cited
  • 1997Postwar U.S. Business Cycles: An Empirical Investigation7,358 cited
  • 2007Giving Content to Investor Sentiment: The Role of Media in the Stock Market4,822 cited
  • 2009Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market4,637 cited
  • 1999Oil price shocks and stock market activity1,798 cited
  • 2009Textual analysis of stock market prediction using breaking financial news777 cited
  • 2008Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm705 cited
  • 2015Sentiment analysis on social media for stock movement prediction626 cited
  • 2006Oil prices and real exchange rates584 cited
  • 2005A Practitioner's Guide to Lag Order Selection For VAR Impulse Response Analysis311 cited

Cited by

  • 2021Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization256 cited
  • 2024Price forecasting through neural networks for crude oil, heating oil, and natural gas239 cited
  • 2020A novel text-based framework for forecasting agricultural futures using massive online news headlines

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179 cited
  • 2021The role of news sentiment in oil futures returns and volatility forecasting: Data-decomposition based deep learning approach143 cited
  • 2023Financial Time Series Forecasting with the Deep Learning Ensemble Model137 cited
  • 2020The role of text-extracted investor sentiment in Chinese stock price prediction with the enhancement of deep learning123 cited
  • 2021Forecasting the U.S. oil markets based on social media information during the COVID-19 pandemic120 cited
  • 2022Forecasting crude oil market volatility using variable selection and common factor113 cited
  • 2021Forecasting crude oil prices based on variational mode decomposition and random sparse Bayesian learning104 cited
  • 2021A combined architecture of multivariate LSTM with Mahalanobis and Z-Score transformations for oil price forecasting102 cited
  • 2020Effective crude oil price forecasting using new text-based and big-data-driven model101 cited
  • 2020Energy price prediction using data-driven models: A decade review91 cited
  • 2021Crude oil price forecasting incorporating news text84 cited
  • 2022Forecasting crude oil futures market returns: A principal component analysis combination approach80 cited
  • 2020Analyzing Crude Oil Prices under the Impact of COVID-19 by Using LSTARGARCHLSTM77 cited
  • 2022Investor sentiment and machine learning: Predicting the price of China's crude oil futures market74 cited