Abstract
In recent years, the challenging global economy has induced a surge in the investment risk. Precious metal index has gained popularity among investors because of its ability to diversify investments and hedge risk. To facilitate optimal decision-making in the face of market volatility, financial institutions, investors and related enterprises need an efficient and reliable method to predict the price fluctuations of precious metals. Based on these, the primary objective of this study is to develop an ensemble model which could predict precious metal index better. In this study, K-means method is employed for label segmentation, categorizing the price percentage changes of precious metal index into five distinct labels, and four machine learning models are selected for predictive performance comparison: XGBoost, LightGBM, CatBoost, and Random Forests. Then three models with better predictive performance are selected for ensemble. The ensemble model shows improved prediction ability compared to these individual ones and the empirical evidence is provided to demonstrate its robustness. In addition, this study provides an interpretable analysis of the characteristics with the SHAP method and proposes some investment recommendations. These results could provide ideas to the participants in the precious metal market.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data Availability
Data sources and availability are indicated in the manuscript.
References
Adekoya, O. B., & Oliyide, J. A. (2021). How COVID-19 drives connectedness among commodity and financial markets: Evidence from TVP-VAR and causality-in-quantiles techniques. Resources Policy, 70, 101898. https://doi.org/10.1016/j.resourpol.2020.101898
Arreola Hernandez, J., & Al Janabi, M. A. M. (2020). Forecasting of dependence, market, and investment risks of a global index portfolio. Journal of Forecasting, 39(3), 512–532. https://doi.org/10.1002/for.2641
Baur, D. G., & Lucey, B. M. (2010). Is gold a hedge or a safe haven? An analysis of stocks, bonds and gold. The Financial Review, 45(2), 217–229. https://doi.org/10.1111/j.1540-6288.2010.00244.x
Ben Ameur, H., Boubaker, S., Ftiti, Z., Louhichi, W., & Tissaoui, K. (2023). Forecasting commodity prices: Empirical evidence using deep learning tools. Annals of Operations Research. https://doi.org/10.1007/s10479-022-05076-6
BenSaïda, A. (2023). Safe haven property of gold and cryptocurrencies during COVID-19 and Russia-Ukraine conflict. Annals of Operations Research. https://doi.org/10.1007/s10479-023-05517-w
Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32. https://doi.org/10.1023/A:1010933404324
Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). https://doi.org/10.1145/2939672.2939785
Chu, Y., Knell, G., Brayton, R. P., Burkhart, S. O., Jiang, X., & Shams, S. (2022). Machine learning to predict sports-related concussion recovery using clinical data. Annals of Physical and Rehabilitation Medicine, 65(4), 101626. https://doi.org/10.1016/j.rehab.2021.101626
Das, D., Bhatia, V., Kumar, S. B., & Basu, S. (2022). Do precious metals hedge crude oil volatility jumps? International Review of Financial Analysis, 83, 102257. https://doi.org/10.1016/j.irfa.2022.102257
Dhifaoui, Z., Khalfaoui, R., Abedin, M. Z., & Shi, B. (2022). Quantifying information transfer among clean energy, carbon, oil, and precious metals: A novel transfer entropy-based approach. Finance Research Letters, 49, 103138. https://doi.org/10.1016/j.frl.2022.103138
Dorogush, A. V., Ershov, V., & Gulin, A. (2018). CatBoost: gradient boosting with categorical features support. Undefined. Retrieved 26 October, 2022 from https://www.semanticscholar.org/reader/f5fbcd9ff72c5820a21b9d6871d2a3d475c9bb7f
Gu, Q., Chang, Y., Xiong, N., & Chen, L. (2021). Forecasting Nickel futures price based on the empirical wavelet transform and gradient boosting decision trees. Applied Soft Computing, 109, 107472. https://doi.org/10.1016/j.asoc.2021.107472
He, K., Chen, Y., & Tso, G. K. F. (2017). Price forecasting in the precious metal market: A multivariate EMD denoising approach. Resources Policy, 54, 9–24. https://doi.org/10.1016/j.resourpol.2017.08.006
HongXing, Y., Naveed, H. M., Memon, B. A., Ali, S., Haris, M., Akhtar, M., & Mohsin, M. (2023). Connectedness between currency risk hedging and firm value: A deep neural network-based evaluation. Computational Economics. https://doi.org/10.1007/s10614-022-10353-4
Idilbi-Bayaa, Y., & Qadan, M. (2021). Forecasting commodity prices using the term structure. Journal of Risk and Financial Management, 14(12), 585. https://doi.org/10.3390/jrfm14120585
Idilbi-Bayaa, Y., & Qadan, M. (2022). What the current yield curve says, and what the future prices of energy do. Resources Policy, 75, 102494. https://doi.org/10.1016/j.resourpol.2021.102494
Irwin, S. H., & Sanders, D. R. (2012). Financialization and structural change in commodity futures markets. Journal of Agricultural and Applied Economics, 44(3), 371–396. https://doi.org/10.1017/S1074070800000481
Jabeur, S. B., Mefteh-Wali, S., & Viviani, J.-L. (2021). Forecasting gold price with the XGBoost algorithm and SHAP interaction values. Annals of Operations Research. https://doi.org/10.1007/s10479-021-04187-w
Kao, L.-J., Chiu, C.-C., Lu, C.-J., & Chang, C.-H. (2013). A hybrid approach by integrating wavelet-based feature extraction with MARS and SVR for stock index forecasting. Decision Support Systems, 17, 1288–1344.
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., et al. (2017). LightGBM: A highly efficient gradient boosting decision tree. In 31st Conference on Neural Information Processing Systems, 9.
Khan, H. A., Ghorbani, S., Shabani, E., & Band, S. S. (2023). Enhancement of neural networks model’s predictions of currencies exchange rates by phase space reconstruction and Harris Hawks’ optimization. Computational Economics. https://doi.org/10.1007/s10614-023-10361-y
Lahiani, A., Mefteh-Wali, S., & Vasbieva, D. G. (2021). The safe-haven property of precious metal commodities in the COVID-19 era. Resources Policy, 74, 102340. https://doi.org/10.1016/j.resourpol.2021.102340
Li, A., Liu, M., & Sheather, S. (2023a). Predicting stock splits using ensemble machine learning and SMOTE oversampling. Pacific-Basin Finance Journal, 78, 101948. https://doi.org/10.1016/j.pacfin.2023.101948
Li, J., Song, L., Wu, D., Shui, J., & Wang, T. (2023b). Lagging problem in financial time series forecasting. Neural Computing and Applications. https://doi.org/10.1007/s00521-023-08879-1
Li, X., Ergu, D., Zhang, D., Qiu, D., Cai, Y., & Ma, B. (2022). Prediction of loan default based on multi-model fusion. Procedia Computer Science, 199, 757–764. https://doi.org/10.1016/j.procs.2022.01.094
Lin, Y. (2021). Forecasting stock index price using the CEEMDAN-LSTM model. North American Journal of Economics and Finance, 14, 101421.
Liu, H., Zhao, T., Wang, S., & Li, X. (2023). A stock rank prediction method combining industry attributes and price data of stocks. Information Processing & Management, 60(4), 103358. https://doi.org/10.1016/j.ipm.2023.103358
Malagrino, L. S., Roman, N. T., & Monteiro, A. M. (2018). Forecasting stock market index daily direction: A Bayesian Network approach. Expert Systems with Applications, 105, 11–22. https://doi.org/10.1016/j.eswa.2018.03.039
Mensi, W., Ali, S. R. M., Vo, X. V., & Kang, S. H. (2022). Multiscale dependence, spillovers, and connectedness between precious metals and currency markets: A hedge and safe-haven analysis. Resources Policy, 77, 102752. https://doi.org/10.1016/j.resourpol.2022.102752
Mishra, N. K., & Singh, P. K. (2021). Feature construction and smote-based imbalance handling for multi-label learning. Information Sciences, 563, 342–357. https://doi.org/10.1016/j.ins.2021.03.001
Na, S. H., & Sohn, S. Y. (2011). Forecasting changes in Korea Composite Stock Price Index (KOSPI) using association rules. Expert Systems with Applications, 38(7), 9046–9049. https://doi.org/10.1016/j.eswa.2011.01.025
Naeem, M. A., Agyemang, A., Hasan Chowdhury, M. I., Hasan, M., & Shahzad, S. J. H. (2022). Precious metals as hedge and safe haven for African stock markets. Resources Policy, 78, 102781. https://doi.org/10.1016/j.resourpol.2022.102781
Nevasalmi, L. (2020). Forecasting multinomial stock returns using machine learning methods. The Journal of Finance and Data Science, 6, 86–106. https://doi.org/10.1016/j.jfds.2020.09.001
Patel, J. (2015). Predicting stock market index using fusion of machine learning techniques. Expert Systems with Applications, 42, 2162–2172.
Peng, X. (2020). Do precious metals act as hedges or safe havens for China’s financial markets? Finance Research Letters, 8, 101353.
Qadan, M., Aharon, D. Y., & Eichel, R. (2019). Seasonal patterns and calendar anomalies in the commodity market for natural resources. Resources Policy, 63, 101435. https://doi.org/10.1016/j.resourpol.2019.101435
Rehman, M. U., Shahzad, S. J. H., Uddin, G. S., & Hedström, A. (2018). Precious metal returns and oil shocks: A time varying connectedness approach. Resources Policy, 58, 77–89. https://doi.org/10.1016/j.resourpol.2018.03.014
Rubio, A., Bermúdez, J. D., & Vercher, E. (2017). Improving stock index forecasts by using a new weighted fuzzy-trend time series method. Expert Systems with Applications, 76, 12–20. https://doi.org/10.1016/j.eswa.2017.01.049
Shi, Y., Li, B., Long, W., & Dai, W. (2022). Method for improving the performance of technical analysis indicators by neural network models. Computational Economics, 59(3), 1027–1068. https://doi.org/10.1007/s10614-021-10116-7
Su, C.-W., Wang, K.-H., Lobonţ, O.-R., & Qin, M. (2023a). Continuous wavelet transform of time-frequency analysis technique to capture the dynamic hedging ability of precious metals. Mathematics, 11(5), 1186. https://doi.org/10.3390/math11051186
Su, I., Lin, P. L., Chung, Y., & Lee, C. (2023b). Forecasting of Taiwan’s weighted stock Price index based on machine learning. Expert Systems. https://doi.org/10.1111/exsy.13408
Sun, X., Liu, M., & Sima, Z. (2020). A novel cryptocurrency price trend forecasting model based on LightGBM. Finance Research Letters, 32, 101084. https://doi.org/10.1016/j.frl.2018.12.032
de Tavares, T. H. B. C., Ferreira, B. P., & Mendes, E. M. A. M. (2022). Fuzzy time series model based on red–black trees for stock index forecasting. Applied Soft Computing, 127, 109323. https://doi.org/10.1016/j.asoc.2022.109323
Wang, J., Cui, Q., Sun, X., & He, M. (2022a). Asian stock markets closing index forecast based on secondary decomposition, multi-factor analysis and attention-based LSTM model. Engineering Applications of Artificial Intelligence, 113, 104908. https://doi.org/10.1016/j.engappai.2022.104908
Wang, J.-J., Wang, J.-Z., Zhang, Z.-G., & Guo, S.-P. (2012). Stock index forecasting based on a hybrid model. Omega, 40(6), 758–766. https://doi.org/10.1016/j.omega.2011.07.008
Wang, K., Li, M., Cheng, J., Zhou, X., & Li, G. (2022b). Research on personal credit risk evaluation based on XGBoost. Procedia Computer Science, 199, 1128–1135. https://doi.org/10.1016/j.procs.2022.01.143
Wang, S., Liu, S., Zhang, J., Che, X., Yuan, Y., Wang, Z., & Kong, D. (2020). A new method of diesel fuel brands identification: SMOTE oversampling combined with XGBoost ensemble learning. Fuel, 282, 118848. https://doi.org/10.1016/j.fuel.2020.118848
Wang, Y., Wang, L., Yang, F., Di, W., & Chang, Q. (2021). Advantages of direct input-to-output connections in neural networks: The Elman network for stock index forecasting. Information Sciences, 547, 1066–1079. https://doi.org/10.1016/j.ins.2020.09.031
Wang, Y.-J., Wu, L.-H., & Wu, L.-C. (2023). An integrative extraction approach for index-tracking portfolio construction and forecasting under a deep learning framework. The Journal of Supercomputing. https://doi.org/10.1007/s11227-023-05538-z
Wen, X., Xie, Y., Wu, L., & Jiang, L. (2021). Quantifying and comparing the effects of key risk factors on various types of roadway segment crashes with LightGBM and SHAP. Accident Analysis & Prevention, 159, 106261. https://doi.org/10.1016/j.aap.2021.106261
Zhang, D., & Tang, P. (2023). Forecasting European Union allowances futures: The role of technical indicators. Energy, 270, 126916. https://doi.org/10.1016/j.energy.2023.126916
Zhao, Y., Zhang, M., Pei, Z., & Nan, J. (2023). The effects of quantitative easing on Bitcoin prices. Finance Research Letters, 57, 104232. https://doi.org/10.1016/j.frl.2023.104232
Zhou, J., Li, W., Wang, J., Ding, S., & Xia, C. (2019). Default prediction in P2P lending from high-dimensional data based on machine learning. Physica a: Statistical Mechanics and Its Applications, 534, 122370. https://doi.org/10.1016/j.physa.2019.122370
Acknowledgements
The authors would like to thank the editors and the anonymous reviewers for their invaluable comments and suggestions.
Funding
This work was partially supported by Beijing Municipal Social Science Foundation (No. 19GLB040).
Author information
Authors and Affiliations
Contributions
All authors contributed to the study conception and design. Methodology, experimentation, data organization and writing were performed by YZ and ML. Funding acquisition, supervision and correspondence were performed by HO.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Zhang, Y., Liang, M. & Ou, H. Prediction of Precious Metal Index Based on Ensemble Learning and SHAP Interpretable Method. Comput Econ (2024). https://doi.org/10.1007/s10614-024-10557-w
Accepted:
Published:
DOI: https://doi.org/10.1007/s10614-024-10557-w