A Model to Predict Daily Gold Price Using Machine Learning-Based Predictive Analysis Approach

Abstract

Abstract This thesis presents a comprehensive analysis of daily gold price prediction in the Sri Lankan market, utilizing a range of exogenous variables to enhance forecasting accuracy. The study explores the impact of key economic indicators, including Brent crude oil prices, USD to LKR exchange rates, CNY to LKR exchange rates, silver prices, S&P 20 index, Colombo Consumer Price Index (CCPI), and gold reserves on the volatility and trends of gold prices in Sri Lanka. Two powerful machine learning algorithms, XGBoost and Random Forest, were employed to predict daily gold prices. The study investigates the predictive performance of these algorithms and compares their effectiveness in capturing the intricate dynamics of the Sri Lankan gold market. The models were trained and tested on a dataset spanning from January 2014 to September 2022 to ensure robustness and reliability in the results. The findings reveal that XGBoost outperformed Random Forest in terms of predictive accuracy and model performance. The superiority of XGBoost suggests its efficacy in handling complex relationships and nonlinear patterns within the dataset, thereby providing more accurate and reliable predictions of daily gold prices in the Sri Lankan market. Furthermore, the inclusion of diverse exogenous variables allows for a more holistic understanding of the factors influencing gold prices. The study contributes valuable insights to the financial community, policymakers, and investors. Keywords: Gold Price, XGBoost, Radom Forest, Machine Learning