A five-factor asset pricing model
10.1016/j.jfineco.2014.10.010 · doi-reference
In-sample and out-of-sample Sharpe ratios of multi-factor asset pricing models
10.1016/j.jfineco.2024.103837 · doi-reference
Hybrid approach to the Japanese candlestick method for f inancial forecasting
10.1016/j.eswa.2008.06.050 · doi-reference
Long Short-Term Memory
10.1162/neco.1997.9.8.1735 · doi-reference
Quantum-enhanced forecasting: Leveraging quantum gramian angular field and CNNs for stock return predictions
10.1016/j.frl.2024.105840 · doi-reference
CNNpred: CNN-based stock market prediction using a diverse set of variables
10.1016/j.eswa.2019.03.029 · doi-reference
10.1109/ssci.2017.8285188
10.1109/ssci.2017.8285188 · doi-reference
CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization
10.1109/tvcg.2020.3030418 · doi-reference
A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
10.1007/bf00344251 · doi-reference
Cointegration and Error Correction Representation, Estimation and Testing
10.2307/1913236 · doi-reference
Statistical analysis of cointegration vectors
10.1016/0165-1889(88)90041-3 · doi-reference
A structural VAR and VECM modeling method for open-high-low-close data contained in candlestick chart
10.1186/s40854-024-00622-6 · doi-reference
10.1371/journal.pone.0249852
10.1371/journal.pone.0249852 · doi-reference
A novel hybrid forecasting system for crude oil futures prices: A dual perspective of deterministic forecasting and uncertainty analysis
10.1016/j.heliyon.2024.e39818 · doi-reference
Crude oil future price forecasting using pretrained transformer model
10.1016/j.procs.2024.08.234 · doi-reference
Investor sentiment and machine learning: Predicting the price of China’s crude oil futures market
10.1016/j.energy.2022.123471 · doi-reference
Research on crude oil futures price prediction methods: A perspective based on quantum deep learning
10.1016/j.energy.2025.135080 · doi-reference
Text-based crude oil price forecasting: A deep learning approach
10.1016/j.ijforecast.2018.07.006 · doi-reference
Study on the impacts of Shanghai crude oil futures on global oil market and oil industry based on VECM and DAG models
10.1016/j.energy.2021.120050 · doi-reference
Intraday return predictability in China’s crude oil futures market: New evidence from a unique trading mechanism
10.1016/j.econmod.2021.01.005 · doi-reference
China’s Crude oil futures forecasting with search engine data
10.1016/j.procs.2022.11.266 · doi-reference
Forecasting the prices of crude oil using the predictor, economic and combined constraints
10.1016/j.econmod.2018.06.020 · doi-reference
Forecasting the prices of crude oil: An iterated combination approach
10.1016/j.eneco.2018.01.027 · doi-reference
The role of coronavirus news in the volatility forecasting of crude oil futures markets: Evidence from China
10.1016/j.resourpol.2021.102173 · doi-reference
Forecasting the Chinese crude oil futures volatility using jump intensity and Markov-regime switching model
10.1016/j.eneco.2024.107588 · doi-reference
Modeling crude oil volatility using economic sentiment analysis and opinion mining of investors via deep learning and machine learning models
10.1016/j.energy.2023.130017 · doi-reference
Forecasting nonlinear crude oil futures prices
10.5547/issn0195-6574-ej-vol27-no4-4 · doi-reference
Forecasting the WTI crude oil price by a hybrid-refined method
10.1016/j.eneco.2018.02.004 · doi-reference
Interval time series forecasting: A systematic literature review
10.1002/for.3024 · doi-reference
Forecasting interval-valued returns of crude oil: A novel kernel-based approach
10.1002/for.3167 · doi-reference
Point and interval prediction of crude oil futures prices based on chaos theory and multiobjective slime mold algorithm
10.1007/s10479-022-04781-6 · doi-reference
Threshold autoregressive models for interval-valued time series data
10.1016/j.jeconom.2018.06.009 · doi-reference
Interval decomposition ensemble approach for crude oil price forecasting
10.1016/j.eneco.2018.10.015 · doi-reference
Forecasting the crude oil prices with an EMD-ISBM-FNN model
10.1016/j.energy.2022.125407 · doi-reference
A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting
10.1016/j.apenergy.2015.07.025 · doi-reference
10.3390/en15061955
10.3390/en15061955 · doi-reference
Price discovery efficiency of China’s crude oil futures: Evidence from the Shanghai crude oil futures market
10.1016/j.eneco.2022.106172 · doi-reference
Convolutional neural network forecasting of European Union allowances futures using a novel unconstrained transformation method
10.1016/j.eneco.2022.106049 · doi-reference
Examining the dynamic effect of COVID-19 pandemic on dwindling oil prices using structural vector autoregressive model
10.1016/j.energy.2021.120813 · doi-reference
Do China’s macro-financial factors determine the Shanghai crude oil futures market?
10.1016/j.irfa.2021.101953 · doi-reference
