A Review of Gold Price Prediction Models Based on the Least Square Method
DOI:
https://doi.org/10.53469/ijomsr.2025.08(05).04Keywords:
Least Square method, Gold price prediction, Linear regression, Nonlinear model, Model optimizationAbstract
With the increasing complexity of the global financial market, gold, as a traditional safe-haven asset, has received widespread attention for its price fluctuations. Accurately predicting the gold price is not only crucial for investors to formulate strategies, but also an important part of the risk management of financial institutions. The least square method, as a classic regression analysis technique, has been widely applied in financial time series prediction due to its simplicity and effectiveness. This paper reviews the research progress of gold price prediction models based on the least square method, covering the evolution from traditional linear regression to advanced nonlinear models. It analyzes the characteristics, application scenarios and prediction performance of different models, and discusses the model optimization strategies and future research directions.
References
Bakas, D., & Triantafyllou, A. (2018). The impact of uncertainty shocks on the volatility of commodity prices. Journal of International Money and Finance, 87, 96-111.
Piffer, M., & Podstawski, M. (2018). Identifying uncertainty shocks using the price of gold. The Economic Journal, 128(616), 3266-3284.
Baur, D. G., & McDermott, T. K. (2016). Why is gold a safe haven?. Journal of Behavioral and Experimental Finance, 10, 63-71.
Erb, C.B., & Harvey, C.R. (2013). The golden dilemma. FAJ, 69(4), 10-42.
Saunders, A., Cornett, M. M., & Erhemjamts, O. (2021). Financial institutions management: A risk management approach. McGraw-Hill.
Brooks, C. (2014). Introductory econometrics for finance. Cambridge university press.
Ruppert, D., & Matteson, D. S. (2011). Statistics and data analysis for financial engineering (Vol. 13). New York: Springer.
Golub, G. H., & Van Loan, C. F. (2013). Matrix computations. JHU press.
John Lu, Z. Q. (2010). The elements of statistical learning: data mining, inference, and prediction.