Analyzing factors influencing global precious metal markets: A feature selection study

Abstract

Precious metals are valuable commodities providing superior protection against risky financial exposure. Identifying factors influencing the market is crucial for anticipating changes. Forecast applications utilize stochastic models capable of learning from historical data to project future values. The dataset is a vital component for prediction tools since all estimations begin with constructing the appropriate information. Detecting the association between input and output is essential to filter data, as including unrelated variables could destabilize the response. Feature selection considers removing uncorrelated attributes before incorporating them as inputs to the predictor. This study employs three regression-based algorithms to examine 58 precious assets from gold, silver, platinum, and palladium markets against several variables cited in the literature. Relationships were detected using regressive feature selection methods, known as least absolute shrinkage and selection operator (LASSO), ridge, and elastic net (EN). Results demonstrate that the proposed algorithms achieved satisfactory performance on 42 assets, justified through a reliable fit and acceptable error. The remaining 16 assets exhibited large deviations with considerably poor regression quality, indicating considerable nonlinearity. Attributes were selected with a detailed emphasis on those exerting the most substantial impact on a particular metal. Based on computational analysis, most investments are susceptible to macroeconomic factors. Some assets may present hedging capabilities towards key features, including stock index, exchange rates, and bond yield. An assessment of common variables among each metal revealed that real GDP growth and interest rates are vital indicators for the precious metal market. Overall, the simulation outcomes show no consistent commonalities amongst attributes within the same asset class in a country. Feature selection from this research offers necessary information regarding time-series dynamics, serving as a basis to project trends. The filtered dataset is expected to enhance the reliability of nonlinear predictive algorithms by removing inaccurate correlations to lower computational load. Furthermore, the outcome provides information regarding correlations affecting global precious metal investments over five-year period. These discussions are necessary for investors considering such commodities as potential portfolio diversifiers.