Innovation Owner
Miss NATCHA THONGCHANA
Student
Details
This project aims to develop and compare gold price prediction models by integrating quantitative variables with news text data. It utilizes nine key predictors and NLP techniques to enhance forecasting accuracy.
This special project aims to develop and compare the performance of gold price prediction models using quantitative variables and news text data. The study incorporates nine key predictors, including:
- Brent crude oil prices
- WTI crude oil prices
- Silver prices
- Platinum prices
- The U.S. Federal Reserve's policy interest rate
- The Nikkei 225 index
- The Dow Jones Industrial Average
- The S&P 500 index
- Daily news articles from Bangkok Business News
Relevant news data will be processed using Natural Language Processing (NLP) techniques and integrated with three predictive models: Gradient Boosting, Machine Learning Models, and Regression Analysis. The model performance will be evaluated using three key metrics: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the Coefficient of Determination (R^2).
Objective
To develop a gold price prediction model using commodity prices, economic indicators, financial variables, and news text, and to compare the performance of the prediction models.
To develop a gold price prediction model using commodity prices, economic indicators, financial variables, and news text, and to compare the performance of the prediction models.