Seeing is believing: Forecasting crude oil price trend from the perspective of images
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DOI: 10.1002/for.3149
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- Nazemi, Abdolreza & Rezazadeh, Hani & Fabozzi, Frank J. & Höchstötter, Markus, 2022. "Deep learning for modeling the collection rate for third-party buyers," International Journal of Forecasting, Elsevier, vol. 38(1), pages 240-252.
- Kose, M. Ayhan & Prasad, Eswar S. & Terrones, Marco E., 2006.
"How do trade and financial integration affect the relationship between growth and volatility?,"
Journal of International Economics, Elsevier, vol. 69(1), pages 176-202, June.
- M. Ayhan Kose & Eswar S. Prasad & Marco E. Terrones, 2004. "How do trade and financial integration affect the relationship between growth and volatility?," Proceedings, Federal Reserve Bank of San Francisco, issue Jun.
- M. Ayhan Kose & Eswar S. Prasad & Marco E. Terrones, 2004. "How do trade and financial integration affect the relationship between growth and volatility," Working Paper Series 2004-29, Federal Reserve Bank of San Francisco.
- Kose, M. Ayhan & Prasad, Eswar & Terrones, Marco E., 2006. "How Do Trade and Financial Integration Affect the Relationship between Growth and Volatility?," IZA Discussion Papers 2252, Institute of Labor Economics (IZA).
- Mr. Eswar S Prasad & Mr. Marco Terrones & Mr. Ayhan Kose, 2005. "How Do Trade and Financial Integration Affect the Relationship Between Growth and Volatility?," IMF Working Papers 2005/019, International Monetary Fund.
- Liu, Mingxi & Li, Guowen & Li, Jianping & Zhu, Xiaoqian & Yao, Yinhong, 2021. "Forecasting the price of Bitcoin using deep learning," Finance Research Letters, Elsevier, vol. 40(C).
- Jeon, Yunho & Seong, Sihyeon, 2022. "Robust recurrent network model for intermittent time-series forecasting," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1415-1425.
- Ahmet Murat Ozbayoglu & Mehmet Ugur Gudelek & Omer Berat Sezer, 2020. "Deep Learning for Financial Applications : A Survey," Papers 2002.05786, arXiv.org.
- Gertler, Pavel & Horvath, Roman, 2018. "Central bank communication and financial markets: New high-frequency evidence," Journal of Financial Stability, Elsevier, vol. 36(C), pages 336-345.
- Parkinson, Michael, 1980. "The Extreme Value Method for Estimating the Variance of the Rate of Return," The Journal of Business, University of Chicago Press, vol. 53(1), pages 61-65, January.
- Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020.
"Empirical Asset Pricing via Machine Learning,"
The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
- Shihao Gu & Bryan T. Kelly & Dacheng Xiu, 2018. "Empirical Asset Pricing via Machine Learning," Swiss Finance Institute Research Paper Series 18-71, Swiss Finance Institute.
- Shihao Gu & Bryan Kelly & Dacheng Xiu, 2018. "Empirical Asset Pricing via Machine Learning," NBER Working Papers 25398, National Bureau of Economic Research, Inc.
- Salinas, David & Flunkert, Valentin & Gasthaus, Jan & Januschowski, Tim, 2020. "DeepAR: Probabilistic forecasting with autoregressive recurrent networks," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1181-1191.
- Lee, Bong-Soo & Rui, Oliver M., 2002. "The dynamic relationship between stock returns and trading volume: Domestic and cross-country evidence," Journal of Banking & Finance, Elsevier, vol. 26(1), pages 51-78, January.
- Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
- De, Kuhelika & Compton, Ryan A. & Giedeman, Daniel C., 2022. "Oil shocks and the U.S. economy in a data-rich model," Economic Modelling, Elsevier, vol. 108(C).
- Vitor G. Azevedo & Lucila M.S. Campos, 2016. "Combination of forecasts for the price of crude oil on the spot market," International Journal of Production Research, Taylor & Francis Journals, vol. 54(17), pages 5219-5235, September.
- Thomas C. Chiang & Zhuo Qiao & Wing-Keung Wong, 2010. "New evidence on the relation between return volatility and trading volume," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(5), pages 502-515.
- Chen, Wei & Xu, Huilin & Jia, Lifen & Gao, Ying, 2021. "Machine learning model for Bitcoin exchange rate prediction using economic and technology determinants," International Journal of Forecasting, Elsevier, vol. 37(1), pages 28-43.
- Burns, Christopher B. & Kane, Stephen, 2022. "Arbitrage breakdown in WTI crude oil futures: An analysis of the events on April 20, 2020," Resources Policy, Elsevier, vol. 76(C).
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