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Gold prices have risen sharply in recent months, prompting renewed debate over whether the market has reached its peak. In this post, we examine quantitative models used to forecast gold prices and evaluate their effectiveness in capturing volatility and market dynamics. However, gold is not only a speculative vehicle, it also functions as an effective hedging instrument. We explore both aspects to provide a comprehensive view of gold’s role in modern portfolio management.
 

Comparative Analysis of Gold Forecasting Models: Statistical vs. Machine Learning Approaches

Gold is an important asset class, serving as both a store of value and a hedge against inflation and market uncertainty. Therefore, performing predictive analysis of gold prices is essential. Reference [1] evaluated several predictive methods for gold prices. It examined not only classical, statistical approaches but also newer machine learning techniques. The study used data from 2021 to 2025, with 80% as in-sample data and 20% as validation data.
 

Findings

-The study analyzes gold’s forecasting dynamics, comparing traditional statistical models (ARIMA, ETS, Linear Regression) with machine learning methods (KNN and SVM).

-Daily gold price data from 2021 to 2025 were used for model training, followed by forecasts for 2026.

-Descriptive analysis showed moderate volatility (σ = 501.12) and strong cumulative growth of 85%, confirming gold’s ongoing role as a strategic safe-haven asset.

-Empirical results indicate that Linear Regression (R² = 0.986, RMSE = 35.7) and ETS models achieved superior forecasting accuracy compared to ARIMA, KNN, and SVM.

-Machine learning models (KNN and SVM) underperformed, often misrepresenting volatility and producing higher forecast errors.

-The results challenge the assumption that complex algorithms necessarily outperform traditional methods in financial forecasting.

-Forecasts for 2026 project an average gold price of $4,659, corresponding to a 58.6% potential return.

-The study cautions that these forecasts remain sensitive to macroeconomic shocks and market uncertainties.

-The findings emphasize that simpler, transparent, and interpretable models can outperform more complex machine learning approaches in volatile market conditions.

In short, the paper shows that,

-Linear Regression and ETS outperformed ARIMA, KNN, and SVM, delivering the lowest error and highest explanatory power,

-Machine learning models (KNN, SVM) did not outperform traditional statistical methods, emphasizing the value of interpretability and stability in volatile markets.

Another notable aspect of the study is its autocorrelation analysis, which reveals that, unlike equities, gold does not exhibit clear autocorrelation patterns—its price behavior appears almost random. The paper also suggested improving the forecasting model by incorporating macroeconomic variables.

Reference

[1] Muhammad Ahmad, Shehzad Khan, Rana Waseem Ahmad, Ahmed Abdul Rehman, Roidar Khan, Comparative analysis of statistical and machine learning models for gold price prediction, Journal of Media Horizons, Volume 6, Issue 4, 2025
 

Using Gold Futures to Hedge Equity Portfolios

Hedging is a risk management strategy used to offset potential losses in one investment by taking an opposing position in a related asset. By using financial instruments such as options, futures, or derivatives, investors can protect their portfolios from adverse price movements. The primary goal of hedging is not to maximize profits but to minimize potential losses and provide stability.

Reference [2] explores hedging basic materials portfolios using gold futures.
 

Findings

-The study examines commodities as alternative investments, hedging instruments, and diversification tools.

-Metals, in particular, tend to be less sensitive to inflation and exhibit low correlation with traditional financial assets.

-Investors can gain exposure to metals through shares of companies in the basic materials sector, which focus on exploration, development, and processing of raw materials.

-Since not all companies in this sector are directly linked to precious metals, the study suggests including gold futures to enhance portfolio diversification.

-The research compares a portfolio composed of basic materials sector stocks with a similar portfolio hedged using gold futures.

-Findings show that hedging with gold reduces both profits and losses, providing a stabilizing effect suitable for risk-averse investors.

-The analysis used historical data from March 1, 2018, to March 1, 2022, and tested several portfolio construction methods, including equal-weight, Monte Carlo, and mean-variance approaches.

-Between March 2022 and November 2023, most portfolios without gold futures experienced losses, while portfolios with short gold futures positions showed reduced drawdowns and more stable performance.

-The basis trading strategy using gold futures did not change the direction of returns but significantly mitigated volatility and portfolio swings.

In short, the study concludes that hedging base metal equity portfolios with gold futures can effectively reduce PnL volatility and enhance portfolio stability, offering a practical approach for conservative investors and professional asset managers.

Reference

[2] Stasytytė, V., Maknickienė, N., & Martinkutė-Kaulienė, R. (2024), Hedging basic materials equity portfolios using gold futures, Journal of International Studies, 17(2), 132-145.
 

Closing Thoughts

In summary, gold can serve as an investment, a speculative vehicle, and a hedging instrument. In the first article, simpler models such as Linear Regression and ETS outperformed complex algorithms in forecasting gold prices, emphasizing the importance of interpretability in volatile markets. In the second, incorporating gold futures into base metal portfolios reduced profit and loss volatility, offering stability for risk-averse investors. Together, the studies highlight gold’s dual function as both a return-generating asset and a tool for risk management.

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