Advancing Gold Market Predictions: Integrating Machine Learning and Economic Indicators in the Gold Nexus Predictor (GNP)
- Title
- Advancing Gold Market Predictions: Integrating Machine Learning and Economic Indicators in the Gold Nexus Predictor (GNP)
- Creator
- Tejasvi B.; Paswan A.S.; Mahajan J.; Singh V.P.; Saxena A.
- Description
- This study employs advanced machine learning algorithms to predict gold prices, using a comprehensive dataset from Bloomberg. The Gold Nexus Predictor (GNP), a key innovation, integrates historical data and economic indicators through advanced feature engineering. Methodologies include exploratory data analysis, model training with various algorithms like Linear regression, Random Forest, Ada Boost, SVM, and ARIMA, and evaluation using metrics like MSC, MAPE, and RMSE. The study's philosophical foundation emphasizes rationalism in economic forecasting and ethical model use. This research offers significant insights for investors and policymakers, enhancing understanding and decision-making in the gold market. 2024 IEEE.
- Source
- TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Bloomberg Dataset; Economic Indicators; Financial Forecasting; Gold Nexus Predictor (GNP); Gold Prices; Investment Strategy; Machine Learning Algorithms
- Coverage
- Tejasvi B., Christ University, Bengaluru, India; Paswan A.S., Christ University, Bengaluru, India; Mahajan J., Christ University, Bengaluru, India; Singh V.P., Ewec, United Arab Emirates; Saxena A., Christ University, Bengaluru, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835038427-7
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Tejasvi B.; Paswan A.S.; Mahajan J.; Singh V.P.; Saxena A., “Advancing Gold Market Predictions: Integrating Machine Learning and Economic Indicators in the Gold Nexus Predictor (GNP),” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19165.