Gold Futures Price Prediction Using Transformer Deep Learning Models with Data Scraped via UiPath.

Abstract

Gold is one of the most valuable and widely traded commodities worldwide, particularly in India, and plays a significant role in economic and financial markets. Historically, gold has been a cornerstone of international trade and economic stability, with central banks maintaining reserves to manage inflation and foreign debt. The price of gold serves as a key economic indicator that influences market trends and investment strategies. However, accurately predicting gold prices is challenging due to the complex and nonlinear nature of financial markets which are influenced by various factors including interest rates, economic recessions, oil price fluctuations, and geopolitical events. The study transformer model was used to predict the daily gold prices which were collected from investing.com through web scraping by using UiPath. It is a Robotic Process Automation (RPA) platform to preserve the integrity of the data and enhance model performance, preprocessing operations such as missing data handling and MinMax scaling were performed. The model was tested and trained on key performance metrics and achieved a Mean Squared Error (MSE) of 0.0224, Root Mean Squared Error (RMSE) of 0.1496, and R-squared of 0.93, with a high prediction accuracy. The study results confirm that the transformer model efficiently detects short-term price movements and long-term market trends offering a more accurate and dependable method than traditional forecasting methods. The study provides valuable guidance to investors, financial analysts and policymakers in making informed decisions in the gold bullion market. Future research can be improved by the inclusion of alternative data sources such as sentiment from news headlines and social media which can potentially offer richer insight into market movements.