Optimized Gradient Boosting for Financial Forecasting: A Data-Driven Approach to Gold Stock Prediction
محورهای موضوعی : Machine learningShreya Garag 1 , Jossy George 2 , Akhil M. Nair 3 , Bosco Paul Alapatt 4 , Riya Baby 5
1 - Computer Science Department, Christ University, Bengaluru, India
2 - Computer Science Department, Christ University, Bengaluru, India
3 - Luxsh Technologies Pvt Ltd, United Kingdom
4 - Computer Science Department, Christ University, Bengaluru, India
5 - Computer Science Department, Christ University, Bengaluru, India
کلید واژه: Gold Stock Prediction, Gradient Boosting Regressor, Machine-Learning, Financial Analysis, Financial Strategy, Artificial Intelligence,
چکیده مقاله :
The application of machine learning algorithms in finance forecasting and stock investment domain has revolutionized the way the financial data is analyzed, interpreted and employed for various investment options. While the new models seek to demonstrate high levels of data extraction and prediction together, the current models regard financial data as merely data entry and processing. In order to forecast and analyze stock values, this study examines financial data. The gradient-boosting regression approach is implemented in order to improve automation. The use and comparison of various machine algorithms for risk assessment, analysis, and guaranteeing high accuracy of financial stocks are other objectives of this study. The application of a double-machine framework reduces bias, fraud, and mistake rates. Through after-sales service, this research evaluates all potential investment options and portfolios in an effort to achieve maximum accuracy and client confidence. Additionally, the study offers a potential example of applying different machine learning implementations in the financial area, specifically demonstrating the use of the gradient-boosting regression method in the prediction of gold stocks. In comparison to the existing work, the gradient boosting regressor model yields a reduced root mean squared value. The dataset was imputed using median and features with more than 30% missing values were removed for further processing The proposed work demonstrates high predictive accuracy and reduced root mean squared value support our proposed work for more dependable forecasting when it comes to stock price prediction.
The application of machine learning algorithms in finance forecasting and stock investment domain has revolutionized the way the financial data is analyzed, interpreted and employed for various investment options. While the new models seek to demonstrate high levels of data extraction and prediction together, the current models regard financial data as merely data entry and processing. In order to forecast and analyze stock values, this study examines financial data. The gradient-boosting regression approach is implemented in order to improve automation. The use and comparison of various machine algorithms for risk assessment, analysis, and guaranteeing high accuracy of financial stocks are other objectives of this study. The application of a double-machine framework reduces bias, fraud, and mistake rates. Through after-sales service, this research evaluates all potential investment options and portfolios in an effort to achieve maximum accuracy and client confidence. Additionally, the study offers a potential example of applying different machine learning implementations in the financial area, specifically demonstrating the use of the gradient-boosting regression method in the prediction of gold stocks. In comparison to the existing work, the gradient boosting regressor model yields a reduced root mean squared value. The dataset was imputed using median and features with more than 30% missing values were removed for further processing The proposed work demonstrates high predictive accuracy and reduced root mean squared value support our proposed work for more dependable forecasting when it comes to stock price prediction.
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