Algorithmic Strategies for Precious Metals Price Forecasting

This research is the first attempt to create machine learning (ML) algorithmic systems that would be able to automatically trade precious metals. The algorithm uses three forecast methodologies: linear regression (LR), Darvas boxes (DB), and Bollinger bands (BB). Our data consists of 20 years of daily price data concerning five precious metals futures: gold, silver, copper, platinum, and palladium. We found that all of the examined precious metals' current daily returns are negatively autocorrelated to their former day's returns and identified lagged interdependencies among the examined metals. Silver futures prices were found to be best forecasted by our systems, and platinum the worst. Moreover, our system better forecasts price-up trends than downtrends for all examined techniques and commodities. Linear regression was found to be the best technique to forecast silver and gold prices trends, while the Bollinger band technique best fits palladium forecasting.

Automated summary

This research is the first attempt to create machine learning algorithmic systems for automatic trading of precious metals. The study uses three forecast methodologies and analyzes five precious metals. The research found negative autocorrelation between daily returns and identified lagged interdependencies among the metals. Moreover, the system shows better accuracy in forecasting price-up trends.