Neural Network Predictions Can Be Misleading Evidence From Predicting Crude Oil Futures Prices

Weige Huang; Hua Wang; Qian Chen

doi:10.1051/e3sconf/202125302015

All issues

Volume 253 (2021)

E3S Web Conf., 253 (2021) 02015

References

Open Access

Issue		E3S Web Conf. Volume 253, 2021 2021 International Conference on Environmental and Engineering Management (EEM 2021)


Article Number		02015
Number of page(s)		4
Section		Big Data Environment Management Application and Industry Research
DOI		https://doi.org/10.1051/e3sconf/202125302015
Published online		06 May 2021

Garbade, Kenneth D., and William L. Silber. “Price movements and price discovery in futures and cash markets.” The Review of Economics and Statistics (1983): 289-297. [Google Scholar]
Oellermann, Charles M., Brorsen B. Wade, and Paul L. Farris. “Price discovery for feeder cattle.” The Journal of Futures Markets (1986-1998) 9. 2 (1989): 113. [Google Scholar]
Quan, Jing. “Two-step testing procedure for price discovery role of futures prices.” The Journal of Futures Markets (1986-1998) 43.1 (1992): 139. [Google Scholar]
Sun, Ting, and Miklos A. Vasarhelyi. “Predicting credit card delinquencies: An application of deep neural networks.” Intelligent Systems in Accounting, Finance and Management 25.4 (2018): 174-189. [Google Scholar]
Yim, Juliana. “A comparison of neural networks with time series models for forecasting returns on a stock market index.” International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. Springer, Berlin, Heidelberg, 2002. [Google Scholar]
Bengio, Yoshua, Aaron Courville, and Pascal Vincent. “Representation learning: A review and new perspectives.” IEEE transactions on pattern analysis and machine intelligence 35.8 (2013): 1798-1828. [PubMed] [Google Scholar]
LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. “Deep learning.” nature 521.7553 (2015): 436-444. [PubMed] [Google Scholar]
Schmidhuber, Jurgen. “Deep learning in neural networks: An overview.” Neural networks 61 (2015): 85-117. [Google Scholar]
Lin, Aimei. “Prediction of international crude oil futures price based on GM (1, 1).” 2009 IEEE International Conference on Grey Systems and Intelligent Services (GSIS 2009). IEEE, 2009. [Google Scholar]
Jiang, Zhi-Qiang, Wen-Jie Xie, and Wei-Xing Zhou. “Testing the weak-form efficiency of the WTI crude oil futures market.” Physica A: Statistical Mechanics and its Applications 405 (2014): 235-244. [Google Scholar]
Karali, Berna, Shiyu Ye, and Octavio A. Ramirez. “Event Study of the Crude Oil Futures Market: A Mixed Event Response Model.” American Journal of Agricultural Economics 101.3 (2019): 960-985. [Google Scholar]
Rucker, Randal R., Walter N. Thurman, and Jonathan K. Yoder. “Estimating the structure of market reaction to news: Information events and lumber futures prices.” American Journal of Agricultural Economics 87.2 (2005): 482-500. [Google Scholar]
Yu, Lean, Shouyang Wang, and Kin Keung Lai. “Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm.” Energy Economics 30.5 (2008): 2623-2635. [Google Scholar]
Jammazi, Rania, and Chaker Aloui. “Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling.” Energy Economics 34.3 (2012): 828-841. [Google Scholar]
Yonaba, H., F. Anctil, and V. Fortin. “Comparing sigmoid transfer functions for neural network multistep ahead streamflow forecasting.” Journal of Hydrologic Engineering 15. 4 (2010): 275-283. [Google Scholar]
Chiroma, Haruna, Sameem Abdulkareem, and Tutut Herawan. “Evolutionary Neural Network model for West Texas Intermediate crude oil price prediction.” Applied Energy 142 (2015): 266-273. [Google Scholar]
Omole, O., O. A. Falode, and A. Deng Deng. “Prediction of Nigerian crude oil viscosity using artificial neural network.” Petroleum and Coal 51.3 (2009): 181-188. [Google Scholar]
Mingming, Tang, and Zhang Jinliang. “A multiple adaptive wavelet recurrent neural network model to analyze crude oil prices.” Journal of Economics and Business 64.4 (2012): 275-286. [Google Scholar]

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[1] Garbade, Kenneth D., and William L. Silber. “Price movements and price discovery in futures and cash markets.” The Review of Economics and Statistics (1983): 289-297. [Google Scholar]

[2] Oellermann, Charles M., Brorsen B. Wade, and Paul L. Farris. “Price discovery for feeder cattle.” The Journal of Futures Markets (1986-1998) 9. 2 (1989): 113. [Google Scholar]

[3] Quan, Jing. “Two-step testing procedure for price discovery role of futures prices.” The Journal of Futures Markets (1986-1998) 43.1 (1992): 139. [Google Scholar]

[4] Sun, Ting, and Miklos A. Vasarhelyi. “Predicting credit card delinquencies: An application of deep neural networks.” Intelligent Systems in Accounting, Finance and Management 25.4 (2018): 174-189. [Google Scholar]

[5] Yim, Juliana. “A comparison of neural networks with time series models for forecasting returns on a stock market index.” International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. Springer, Berlin, Heidelberg, 2002. [Google Scholar]

[6] Bengio, Yoshua, Aaron Courville, and Pascal Vincent. “Representation learning: A review and new perspectives.” IEEE transactions on pattern analysis and machine intelligence 35.8 (2013): 1798-1828. [PubMed] [Google Scholar]

[7] LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. “Deep learning.” nature 521.7553 (2015): 436-444. [PubMed] [Google Scholar]

[8] Schmidhuber, Jurgen. “Deep learning in neural networks: An overview.” Neural networks 61 (2015): 85-117. [Google Scholar]

[9] Lin, Aimei. “Prediction of international crude oil futures price based on GM (1, 1).” 2009 IEEE International Conference on Grey Systems and Intelligent Services (GSIS 2009). IEEE, 2009. [Google Scholar]

[10] Jiang, Zhi-Qiang, Wen-Jie Xie, and Wei-Xing Zhou. “Testing the weak-form efficiency of the WTI crude oil futures market.” Physica A: Statistical Mechanics and its Applications 405 (2014): 235-244. [Google Scholar]

[11] Karali, Berna, Shiyu Ye, and Octavio A. Ramirez. “Event Study of the Crude Oil Futures Market: A Mixed Event Response Model.” American Journal of Agricultural Economics 101.3 (2019): 960-985. [Google Scholar]

[12] Rucker, Randal R., Walter N. Thurman, and Jonathan K. Yoder. “Estimating the structure of market reaction to news: Information events and lumber futures prices.” American Journal of Agricultural Economics 87.2 (2005): 482-500. [Google Scholar]

[13] Yu, Lean, Shouyang Wang, and Kin Keung Lai. “Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm.” Energy Economics 30.5 (2008): 2623-2635. [Google Scholar]

[14] Jammazi, Rania, and Chaker Aloui. “Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling.” Energy Economics 34.3 (2012): 828-841. [Google Scholar]

[15] Yonaba, H., F. Anctil, and V. Fortin. “Comparing sigmoid transfer functions for neural network multistep ahead streamflow forecasting.” Journal of Hydrologic Engineering 15. 4 (2010): 275-283. [Google Scholar]

[16] Chiroma, Haruna, Sameem Abdulkareem, and Tutut Herawan. “Evolutionary Neural Network model for West Texas Intermediate crude oil price prediction.” Applied Energy 142 (2015): 266-273. [Google Scholar]

[17] Omole, O., O. A. Falode, and A. Deng Deng. “Prediction of Nigerian crude oil viscosity using artificial neural network.” Petroleum and Coal 51.3 (2009): 181-188. [Google Scholar]

[18] Mingming, Tang, and Zhang Jinliang. “A multiple adaptive wavelet recurrent neural network model to analyze crude oil prices.” Journal of Economics and Business 64.4 (2012): 275-286. [Google Scholar]