Author(s):
Sivaprakash J., Manu K. S.
Email(s):
manu.ks@christuniversity.in
DOI:
10.52711/2321-5763.2021.00049 
Address:
Sivaprakash J.1, Dr. Manu K. S.2*
1Student, MBA (Finance Management), Christ (Deemed to be University), Bengaluru - 560029, Karnataka, India.
2Assistant Professor, Christ (Deemed to be University), Bengaluru - 560029, Karnataka, India.
*Corresponding Author
Published In:
Volume - 12,
Issue - 3,
Year - 2021
ABSTRACT:
In the advanced global economy, crude oil is a commodity that plays a major role in every economy. As Crude oil is highly traded commodity it is essential for the investors, analysts, economists to forecast the future spot price of the crude oil appropriately. In the last year the crude oil faced a historic fall during the pandemic and reached all time low, but will this situation last? There was analysis such as fundamental analysis, technical analysis and time series analyses which were carried out for predicting the movement of the oil prices but the accuracy in such prediction is still a question. Thus, it is necessary to identify better methods to forecast the crude oil prices. This study is an empirical study to forecast crude oil prices using the neural networks. This study consists of 13 input variables with one target variable. The data are divided in the ratio 70:30. The 70% data is used for training the network and 30% is used for testing. The feed forward and back propagation algorithm are used to predict the crude oil price. The neural network proved to be efficient in forecasting in the modern era. A simple neural network performs better than the time series models. The study found that back propagation algorithm performs better while predicting the crude oil price. Hence, ANN can be used by the investors, forecasters and for future researchers.
Cite this article:
Sivaprakash J., Manu K. S. Forecasting Crude Oil price using Artificial Neural Network model. Asian Journal of Management. 2021; 12(3):321-6. doi: 10.52711/2321-5763.2021.00049
Cite(Electronic):
Sivaprakash J., Manu K. S. Forecasting Crude Oil price using Artificial Neural Network model. Asian Journal of Management. 2021; 12(3):321-6. doi: 10.52711/2321-5763.2021.00049 Available on: https://ajmjournal.com/AbstractView.aspx?PID=2021-12-3-14
REFERENCES:
1. El-chaarani, H. (2019). The Impact of Oil Prices on Stocks Markets: New Evidence During and After the Impact of Oil Prices on Stocks Markets: New Evidence During and After the Arab Spring in Gulf Cooperation Council Economies. November. https:// doi.org/10.32479/ijeep.7978
2. Fazelabdolabadi, B. (2019). A hybrid Bayesian-network proposition for forecasting the crude oil price. Financial Innovation, 5(1). https://doi.org/10.1186/s40854-019-0144-2
3. Gupta, N., and Nigam, S. (2020). Crude Oil Price Prediction using Artificial Neural Network. Procedia Computer Science, 170: 642–647. https://doi.org/10.1016/j.procs.2020.03.136
4. Hamdi, M., Finance, I., Manar, E., Aloui, C., Saud, K., and Arabia, S. (2015). Volume 35, Issue 2 Forecasting Crude Oil Price Using Artificial Neural Networks: A Literature. 35(2): 1339–1359.
5. Hashemi, M., and Virginia, W. (2017). The Relationship between Oil Prices and US Dollar Exchange Rates in Short and Long Scales Mostafa Hashemi Thesis submitted Division of Resource Economics and Management.
6. Hu, J. W., Hu, Y., and Lin, R. R. (2012). Applying Neural Networks to Prices Prediction of Crude Oil Futures. 2012(August 2006). https://doi.org/10.1155/2012/959040
7. Mahdiani, M. R., and Khamehchi, E. (2016). A modified neural network model for predicting the crude oil price. Intellectual Economics, 10(2): 71-77. https://doi.org/10.1016/ j.intele.2017.02.001
8. Malhotra, A., and Krishna, S. (2015). The effect of Crude Oil prices on Inflation and Interest Rates in India: Evidence from DCC-GARCH model. 1-8.
9. Mikhaylov, A. Jaehyung A. N., Nikita Moiseev. (2020). Oil Price Predictors: Machine Learning Approach Oil Price Predictors: Machine Learning Approach. September 2019. https://doi.org/ 10.32479/ijeep.7597
10. Qiang, W., Lin, A., Zhao, C., Liu, Z., Liu, M., and Wang, X. (2019). The impact of international crude oil price fluctuation on the exchange rate of petroleum ‑ importing countries: a summary of recent studies. Natural Hazards, 95(1): 227-239. https://doi.org/ 10.1007/s11069-018-3501-y
11. Ramyar, S., and Kianfar, F. (2017). Forecasting Crude Oil Prices: A Comparison between Artificial Neural Networks and Vector Autoregressive Models. Computational Economics, 53(2): 743-761. https://doi.org/10.1007/s10614-017-9764-7
12. Rahmanifard, H., Plaksina, T. Application of artificial intelligence techniques in the petroleum industry: a review. Artif Intell Rev 52, 2295-2318 (2019). https://doi.org/10.1007/s10462-018-9612-8
13. Sahu, T. N., Tukaran, K., Bandopadhyay, K., and Mondal, D. (2015). Crude Oil Price, Exchange Rate and Emerging Stock Market: Evidence from India. 42: 75-87.
14. Sakashita Y, Yoshizaki Y. The Effects of Oil Price Shocks on IIP and CPI in Emerging Countries. Economies. 2016; 4(4):20. https:// doi.org/10.3390/economies4040020
15. Tahmoorespour, R., Rezvani, M., Safari, M., and Randjbaran, E. (2018). The impact of oil price fluctuations on industry stock returns: Evidence from international markets. 5(1): 1-12.
16. Waheed, R., Wei, C., Sarwar, S., and Lv, Y. (2018). Impact of oil prices on firm stock return: industry-wise analysis. Empirical Economics, 55(2): 765–780. https://doi.org/10.1007/s00181-017-1296-4