GPT Models or Econometric Models: A Comparative Analysis of Gold Price Determinants
DOI:
https://doi.org/10.18778/2391-6478.3.47.08Keywords:
gold price, econometric model, gold price determinants, GPTAbstract
The purpose of the article. The main objective of this article is to compare the results of data analysis regarding gold prices and their determinants using two approaches: a classical econometric model and Microsoft Copilot, which integrates advanced artificial intelligence technologies, including the GPT-4 language model (Generative Pre-trained Transformer 4). The secondary objective is to identify, based on the existing literature, the main factors influencing fluctuations in gold prices. These include: the price of crude oil, the USD/EUR exchange rate, the S&P 500 index, and the Consumer Price Index (CPI) in the United States.
Methodology. The empirical study involves determining the descriptive statistics of the analyzed variables, the correlation matrix, and estimating the structural parameters of the model explaining the gold price.
Results of the research. The best results were obtained for the logarithmic returns of the analyzed variables. In line with the stated hypotheses, there is a negative relationship between the gold price and changes in the S&P 500 index, a negative relationship between the gold price and changes in the US$/EUR exchange rate, and a positive relationship between the gold price and the CPI. The study shows that, during the analyzed period (02.2004–11.2023), changes in crude oil prices did not have a statistically significant impact on gold price changes. To obtain data analysis results using Microsoft Copilot, a "chat" session was conducted. The responses provided the following information: proposed determinants of gold prices, a list of scientific articles, and R code to perform the auto.arima procedure. A comparison was made between the model incorporating economic theory-based factors and the model from the auto.arima procedure suggested by Microsoft Copilot. Based on the conducted study, it can be concluded that the model incorporating both autoregressive factors and other gold price determinants better explains the analyzed variable.
Downloads
References
Amini, A., Kalantari R. (2024). Gold price prediction by a CNN-Bi-LSTM model along with automatic parameter tuning. PLoS ONE 19(3). https://doi.org/10.1371/journal.pone.0298426
Google Scholar
DOI: https://doi.org/10.1371/journal.pone.0298426
Bankier.pl – https://www.bankier.pl/
Google Scholar
Borkowski, B., Dudek H., Szczęsny W. (2007). Ekonometria. wybrane zagadnienia. Wydawnictwo Naukowe PWN.
Google Scholar
Bukowski, S.I. (2016). The main determinants of gold price in the international market. International Business and Global Economy, Tom 35/1, 402–413. https://doi.org/10.4467/23539496IB.16.029.5610
Google Scholar
Choong, P., Kwoo, P., Piong, C., Wong, W. (2012). Determinants of Gold Price: Using Simple and Multiple Linear Regression. Universiti Tunku Abdul Rahman, 1–99.
Google Scholar
Chudy-Hyski, D. (2006). Ocena wybranych uwarunkowań rozwoju funkcji turystycznej obszaru. Infrastruktura i Ekologia Terenów Wiejskich, 2/1.
Google Scholar
Greene W.H. (2002). Econometric analysis, 5th edition, Pearson.
Google Scholar
Gretl software – https://gretl.sourceforge.net/
Google Scholar
Guha, B., Bandyopadhyay, G. (2016). Gold price forecastin using ARIMA model. Journal of Advanced Management Science, vol. 4, no. 2., 117–121. http://dx.doi.org/10.12720/joams.4.2.117-121
Google Scholar
DOI: https://doi.org/10.12720/joams.4.2.117-121
Ismail, Z., Yahya, A., Shabri, A. (2009). Forecasting gold prices using multiple linear regression method. American Journal of Applied Sciences, vol. 6 (8), 1509–1514. https://doi.org/10.3844/ajassp.2009.1509.1514
Google Scholar
DOI: https://doi.org/10.3844/ajassp.2009.1509.1514
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, vol. 334, 679–699. https://link.springer.com/article/10.1007/s10479-021-04187-w
Google Scholar
DOI: https://doi.org/10.1007/s10479-021-04187-w
Levin, E.J., Wright, R.E. (2006). Short-run and long-run determinants of the price of gold. World Gold Council, Research Study no. 32.
Google Scholar
Livieris, I.E., Pintelas, E., Pintelas, P. (2020). A CNN–LSTM model for gold price time-series forecasting. Neural Comput & Applic, vol. 32, 17351–17360. https://link.springer.com/article/10.1007/s00521-020-04867-x
Google Scholar
DOI: https://doi.org/10.1007/s00521-020-04867-x
Maddala, G.S. (2001). Introduction to econometrics, 3rd edition. Wiley India, ISBN: 9788126510955.
Google Scholar
Makalala, D., Li, Z. (2021). Prediction of gold price with ARIMA and SVM. Journal of Physics: Conference Series, vol. 1767. http://dx.doi.org/10.1088/1742-6596/1767/1/012022
Google Scholar
DOI: https://doi.org/10.1088/1742-6596/1767/1/012022
Polyus https://sustainability.polyus.com/en/esg_data_and_reports/
Google Scholar
Puci, J., Demi, A., Pjeshka, A. (2022). The effect of the S&P500 on gold prices. Journal of Financial and Monetary Economics, vol. 10, 308–313.
Google Scholar
Setyowibowo, S., As’ad, M., Sujito, S., Farida, E. (2021). Forecasting of daily gold price using ARIMA-GARCH hybrid model. Jurnal Ekonomi Pembangunan, vol. 19, 257–270. https://www.researchgate.net/publication/358593313_Forecasting_of_Daily_Gold_Price_using_ARIMA-GARCH_Hybrid_Model
Google Scholar
DOI: https://doi.org/10.29259/jep.v19i2.13903
Verbeek, M. (2012). A guide to modern econometrics, 4th Edition, John Wiley & Sons, Ltd.
Google Scholar
Yang, X. (2019). The prediction of gold price using ARIMA model. 2nd International Conference on Social Science, Public Health and Education. http://dx.doi.org/10.2991/ssphe-18.2019.66
Google Scholar
DOI: https://doi.org/10.2991/ssphe-18.2019.66
Zhang, P., Ci, B. (2020). Deep belief network for gold price forecasting. Resources Policy, vol. 69. http://dx.doi.org/10.1016/j.resourpol.2020.101806
Google Scholar
DOI: https://doi.org/10.1016/j.resourpol.2020.101806
Downloads
Published
How to Cite
Issue
Section
License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.




