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Investor sentiment and machine learning: Predicting the price of China's crude oil futures market
16 references·2 citing·39 second hop
Investor sentiment and machine learning: Predicting the price of China's crude oil futures market (2022)Investor sentiment and machine learning: …Machine learning in the Chinese stock market (2021)Machine learning in the Chine…Generación de series de tiempo financieras sintéticas para "data augmentation" usando redes neuronales generativas adversarias (GAN) (2021)Generación de series de tiemp…Integración de los mercados financieros Latinoamericanos por medio de MILA (2020)Integración de los mercados f…Time-varying relation between black and green bond price benchmarks: Macroeconomic determinants for the first decade (2019)Time-varying relation between…Machine learning in energy economics and finance: A review (2019)Machine learning in energy ec…Forecasting crude oil prices with a large set of predictors: Can LASSO select powerful predictors? (2019)Forecasting crude oil prices …Text-based crude oil price forecasting: A deep learning approach (2018)Text-based crude oil price fo…The Effects of Twitter Sentiment on Stock Price Returns (2015)The Effects of Twitter Sentim…Oil price shocks and agricultural commodity prices (2014)Oil price shocks and agricult…The short of it: Investor sentiment and anomalies (2011)The short of it: Investor sen…The economic value of co-movement between oil price and exchange rate using copula-based GARCH models (2011)The economic value of co-move…Giving Content to Investor Sentiment: The Role of Media in the Stock Market (2007)Giving Content to Investor Se…Investor Sentiment and the Cross‐Section of Stock Returns (2006)Investor Sentiment and the Cr…Is All That Talk Just Noise? The Information Content of Internet Stock Message Boards (2004)Is All That Talk Just Noise? …Comparing Predictive Accuracy (2002)Comparing Predictive AccuracyInvestigating Causal Relations by Econometric Models and Cross-spectral Methods (1969)Investigating Causal Relation…Transformer-based forecasting for intraday trading in the Shanghai crude oil market: Analyzing open-high-low-close prices (2023)Transformer-based forecasting…Novel deterministic and probabilistic forecasting methods for crude oil price employing optimized deep learning, statistical and hybrid models (2023)Novel deterministic and proba…The General Theory of Employment, Interest and Money. (1936)CAPITAL ASSET PRICES: A THEORY OF MARKET EQUILIBRIUM UNDER CONDITIONS OF RISK* (1964)Common risk factors in the returns on stocks and bonds (1993)Global, local, and contagious investor sentiment (2011)Investor Sentiment Aligned: A Powerful Predictor of Stock Returns (2014)Illiquidity and Stock Returns II: Cross-Section and Time-Series Effects (2018)Comparing Predictive Accuracy (1994)A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting (1997)Time series forecasting using a hybrid ARIMA and neural network model (2003)A machine learning approach on the relationship among solar and wind energy production, coal consumption, GDP, and CO2 emissions (2020)Does Good ESG Lead to Better Financial Performances by Firms? Machine Learning and Logistic Regression Models of Public Enterprises in Europe (2020)A systematic review of machine learning in logistics and supply chain management: current trends and future directions (2021)A Stochastic Approximation Method (1951)Strictly Proper Scoring Rules, Prediction, and Estimation (2007)Deep learning with long short-term memory networks for financial market predictions (2017)Deep learning for financial applications : A survey (2020)Applications of deep learning in stock market prediction: Recent progress (2021)Financial time series forecasting with multi-modality graph neural network (2021)Risk, Return, and Equilibrium: Empirical Tests (1973)The Cross-Section of Expected Stock Returns (1992)Illiquidity and stock returns: cross-section and time-series effects (2002)Noise Trader Risk in Financial Markets (1990)Investor Psychology and Security Market Under‐ and Overreactions (1998)Policy Uncertainty and Corporate Investment (2015)Expected stock returns and volatility (1987)On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks (1993)Media Coverage and the Cross‐section of Stock Returns (2009)The Econometrics of Financial Markets (2012)The Limits of Arbitrage (1995)Law, finance, and economic growth in China (2005)Explainable artificial intelligence modeling to forecast bitcoin prices (2023)Option Return Predictability with Machine Learning and Big Data (2023)FinTech: a literature review of emerging financial technologies and applications (2025)Oil and the Macroeconomy since World War II (1983)Testing for Granger non-causality in heterogeneous panels (2012)More Than Words: Quantifying Language to Measure Firms' Fundamentals (2008)In Search of Attention (2011)Textual Analysis in Accounting and Finance: A Survey (2016)Does Anything Beat 5-Minute RV? A Comparison of Realized Measures Across Multiple Asset Classes (2012)
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References

  • 1969Investigating Causal Relations by Econometric Models and Cross-spectral Methods22,971 cited
  • 2006Investor Sentiment and the Cross‐Section of Stock Returns6,042 cited
  • 2007Giving Content to Investor Sentiment: The Role of Media in the Stock Market4,822 cited
  • 2020Integración de los mercados financieros Latinoamericanos por medio de MILA3,764 cited
  • 2004Is All That Talk Just Noise? The Information Content of Internet Stock Message Boards2,369 cited
  • 2011The short of it: Investor sentiment and anomalies1,781 cited
  • 2002Comparing Predictive Accuracy1,745 cited
  • 2021Generación de series de tiempo financieras sintéticas para "data augmentation" usando redes neuronales generativas adversarias (GAN)1,463 cited
  • 2021Machine learning in the Chinese stock market593 cited
  • 2019Machine learning in energy economics and finance: A review438 cited
  • 2015The Effects of Twitter Sentiment on Stock Price Returns394 cited
  • 2019Time-varying relation between black and green bond price benchmarks: Macroeconomic determinants for the first decade337 cited
  • 2018Text-based crude oil price forecasting: A deep learning approach305 cited
  • 2019Forecasting crude oil prices with a large set of predictors: Can LASSO select powerful predictors?290 cited

Only edges whose both ends are imported exist here. Widen the year window, or show the whole map. Back to the work

  • 2011The economic value of co-movement between oil price and exchange rate using copula-based GARCH models257 cited
  • 2014Oil price shocks and agricultural commodity prices251 cited
  • Cited by

    • 2023Novel deterministic and probabilistic forecasting methods for crude oil price employing optimized deep learning, statistical and hybrid models44 cited
    • 2023Transformer-based forecasting for intraday trading in the Shanghai crude oil market: Analyzing open-high-low-close prices42 cited