ORIGINAL PAPER
Prediction of mineral product price based on mean reversion model
1
BGRIMM Technology Group
2
Beijing Key Laboratory of Nonferrous Intelligent Mining Technology
3
BGRIMM Intelligent Technology Co. Ltd
Submission date: 2022-05-03
Final revision date: 2022-05-13
Acceptance date: 2022-05-28
Publication date: 2022-06-28
Gospodarka Surowcami Mineralnymi – Mineral Resources Management 2022;38(2):61-75
KEYWORDS
TOPICS
ABSTRACT
The mean-reversion model is introduced into the study of mineral product price prediction. The gold price data from January 2018 to December 2021 are selected, and a mean-reverting stochastic process simulation of the gold price was carried out using Monte Carlo simulation (MCS) method. By comparing the statistical results and trend curves of the mean-reversion (MR) model, geometric Brownian motion (GBM) model, time series model and actual price, it is proved that the mean-reversion process is valid in describing the price fluctuation of mineral product. At the same time, by comparing with the traditional prediction methods, the mean-reversion model can quantitatively assess the uncertainty of the predicted price through a set of equal probability stochastic simulation results, so as to provide data support and decision-making basis for the risk analysis of future economy.
ACKNOWLEDGEMENTS
This work was jointly supported by the Major Science and Technology Innovation Project of Shandong Province No. 2019SDZY05 and the Scientific Research Fund of the BGRIMM Technology Group No. 02-2035.
METADATA IN OTHER LANGUAGES:
Polish
Prognozowanie ceny produktu mineralnego w oparciu o model średniej rewersji
cena produktu mineralnego, model średniej rewersji, symulacja Monte Carlo, analiza niepewności
W badaniach predykcji cen produktów mineralnych wprowadzono model średniej rewersji. Wybrano dane dotyczące cen złota od stycznia 2018 do grudnia 2021 r., a symulację ceny złota w procesie odwracania średniej przeprowadzono metodą symulacji Monte Carlo (MCS). Porównując wyniki statystyczne i krzywe trendu modelu średniej rewersji (MR), modelu geometrycznego ruchu Browna (GBM), modelu szeregów czasowych i rzeczywistej ceny, udowodniono, że proces średniej rewersji jest prawidłowy w opisie fluktuacji cen na produkt mineralny. Jednocześnie, porównując z tradycyjnymi metodami predykcji, model średniej rewersji może ilościowo oszacować niepewność przewidywanej ceny za pomocą zestawu wyników symulacji stochastycznej równego prawdopodobieństwa, w celu zapewnienia wsparcia danych i podstawy decyzyjnej do analizy ryzyka przyszłej gospodarki.
REFERENCES (22)
1.
Chen et al. 2019 – Chen, J., Zhu, X. and Zhong, M. 2019. Nonlinear effects of financial factors on fluctuations in nonferrous metals prices: A Markov-switching VAR analysis. Resources Policy 61, pp. 489–500, DOI: 10.1016/j.resourpol.2018.04.015.
3.
Fama, E.F. and French, K.R. 1988. Permanent and temporary components of stock prices. Journal of Political Economy 96, pp. 246–273, DOI: 10.1086/261535.
4.
Garcia et al. 2018 – Garcia, F., Guijarro, F., Oliver, J. and Tamošiūnienė, R. 2018. Hybrid fuzzy neural network to predict price direction in the German DAX-30 index. Technological and Economic Development of Economy 24(6), pp. 2161–2178, DOI: 10.3846/tede.2018.6394.
5.
Gleich et al. 2013 – Gleich, B., Achzet, B., Mayer, H. and Rathgeber, A. 2013. An empirical approach to determine specific weights of driving factors for the price of commodities – A contribution to the measurement of the economic scarcity of minerals and metals. Resources Policy 38(3), pp. 350–362, DOI: 10.1016/j.resourpol.2013.03.011.
6.
Labys et al. 1999 – Labys, W.C., Achouch, A. and Terraza, M. 1999. Metal prices and the business cycle. Resources Policy 25(4), pp. 229–238, DOI: 10.1016/S0301-4207(99)00030-6.
7.
Liu, H. and Long, Z. 2020. An improved deep learning model for predicting stock market price time series. Digital Signal Processing 102, DOI: 10.1016/j.dsp.2020.102741.
8.
Liu et al. 2022 – Liu, K., Cheng, J. and Yi, J. 2022. Copper price forecasted by hybrid neural network with Bayesian Optimization and wavelet transform. Resources Policy 75, DOI: 10.1016/j.resourpol.2021.102520.
9.
Livieris et al. 2020 – Livieris, I.E., Pintelas, E. and Pintelas, P. 2020. A CNN-LSTM model for gold price time-series forecasting. Neural computing and applications 32, pp. 17351–17360, DOI: 10.1007/s00521-020-04867-x.
11.
Mohamed, J. 2020. Time series modeling and forecasting of Somaliland consumer price index: a comparison of ARIMA and regression with ARIMA errors. American Journal of Theoretical and Applied Statistics 9(4), pp. 143–53, DOI: 10.11648/j.ajtas.20200904.18.
12.
Obthong et al. 2020 – Obthong, M., Tantisantiwong, N., Jeamwatthanachai, W. and Wills, G.B. 2020. A Survey on Machine Learning for Stock Price Prediction: Algorithms and Techniques. FEMIB 2020, pp. 63–71, DOI: 10.5220/0009340700630071.
13.
Postali, F. and Picchetti, P. 2006. Geometric Brownian Motion and structural breaks in oil prices: A quantitative analysis. Energy Economics 28(4), pp. 506–522, DOI: 10.1016/j.eneco.2006.02.011.
14.
Poterba, J.M. and Summers, L.H. 1987. Mean Reversion in Stock Prices: Evidence and Implications. Journal of Financial Economics 22(1), pp. 27–59.
15.
Ramos et al. 2019 – Ramos, A.L., Mazzinghy, D.B., Barbosa, V.D.S.B., Oliveira, M.M. and Silva, G.R.D. 2019. Evaluation of an iron ore price forecast using a geometric Brownian motion model. REM-International Engineering Journal, 72, pp. 9–15, DOI: 10.1590/0370-44672018720140.
16.
Rathnayaka, R. and Seneviratna, D. 2019. Taylor series approximation and unbiased GM(1,1) based hybrid statistical approach for forecasting daily gold price demands. Grey systems: theory and application 9, pp. 5–18, DOI: 10.1108/GS-08-2018-0032.
17.
Rubaszek et al. 2020 – Rubaszek, M., Karolak, Z. and Kwas, M. 2020. Mean-reversion, non-linearities and the dynamics of industrial metal prices. A forecasting perspective. Resources Policy 65, DOI: 10.1016/j.resourpol.2019.101538.
18.
Savolainen et al. 2022 – Savolainen, J., Rakhsha, R. and Durham, R. 2022. Simulation-based decision-making system for optimal mine production plan selection. Mineral Economics, pp. 1–15, DOI: 10.1007/s13563-021-00297-w.
19.
Schwartz, E.S. 1997. The stochastic behavior of commodity prices: Implications for valuation and hedging. The Journal of finance 52, pp. 923–973, DOI: 10.1111/j.1540-6261.1997.tb02721.x.
20.
Shrestha, M.B. and Bhatta, G.R. 2018. Selecting appropriate methodological framework for time series data analysis. Journal of Finance and Data Science 4(2), pp. 71–89, DOI: 10.1016/j.jfds.2017.11.001.
21.
Wadi et al. 2018 – Wadi, S., Almasarweh, M., Alsaraireh, A.A. and Aqaba, J. 2018. Predicting closed price time series data using ARIMA Model. Modern Applied Science 12(11), pp. 181–185, DOI: 10.5539/mas.v12n11p181.
22.
Yao, T. and Wang, Z. 2021. Crude oil price prediction based on LSTM network and GM (1,1) model. Grey Systems: Theory and Application 11, pp. 80–94, DOI: 10.1108/gs-03-2020-0031.
| eISSN: | 2299-2324 |
| ISSN: | 0860-0953 |
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