Optimized Gradient Boosting for Financial Forecasting: A Data-Driven Approach to Gold Stock Prediction
الموضوعات : 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
الکلمات المفتاحية: Gold Stock Prediction, Gradient Boosting Regressor, Machine-Learning, Financial Analysis, Financial Strategy, Artificial Intelligence,
ملخص المقالة :
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.
[1] Wasserbacher, H., Spindler, M. Machine learning for financial forecasting, planning and analysis: recent developments and pitfalls. Digit Finance 4, 63–88 (2022). https://doi.org/10.1007/s42521-021-00046-2 .
[2] Ferreira, Fernando GDC, Amir H. Gandomi, and Rodrigo TN Cardoso. "Artificial intelligence applied to stock market trading: a review." IEEE Access 9 (2021): 30898-30917.
[3] Bussmann, Niklas. "Explainable machine learning in credit risk management." Computational Economics 57 (2021): 203-216.
[4] Pustokhina, Irina V. "Artificial intelligence assisted Internet of Things based financial crisis prediction in FinTech environment." Annals of operations research (2021): 1-21.
[5] Cao, Longbing, Qiang Yang, and Philip S. Yu. "Data science and AI in FinTech: An overview." International Journal of Data Science and Analytics 12 (2021): 81-99.
[6] Ciampi, Francesco. "Rethinking SME default prediction: a systematic literature review and future perspectives." Scientometrics 126 (2021): 2141-2188.
[7] Von Zahn, Moritz, Stefan Feuerriegel, and Niklas Kuehl. "The cost of fairness in AI: Evidence from e-commerce." Business & information systems engineering (2021): 1-14.
[8] Drydakis, Nick. "Artificial Intelligence and reduced SMEs’ business risks. A dynamic capabilities analysis during the COVID-19 pandemic." Information Systems Frontiers 24.4 (2022): 1223-1247.
[9] Piehlmaier, Dominik M. "Overconfidence and the adoption of robo-advice: why overconfident investors drive the expansion of automated financial advice." Financial Innovation 8.1 (2022): 1-24.
[10] Altrock, Sophie, Anne-Laure Mention, and Tor Helge Aas. "Being human in the digitally enabled workplace: Insights from the robo-advice literature." IEEE Transactions on Engineering Management 71 (2023): 7876-7891.
[11] Breuer, Wolfgang, and Andreas Knetsch. "Recent trends in the digitalization of finance and accounting." Journal of Business Economics 93.9 (2023): 1451-1461.
[12] Von Walter, Benjamin, Dietmar Kremmel, and Bruno Jäger. "The impact of lay beliefs about AI on adoption of algorithmic advice." Marketing Letters 33.1 (2022): 143-155.
[13] Guo, Haochen, and Petr Polak. "Artificial intelligence and financial technology FinTech: How AI is being used under the pandemic in 2020." The Fourth Industrial Revolution: Implementation of Artificial Intelligence for Growing Business Success (2021): 169-186.
[14] Kumari, Bharti, Jaspreet Kaur, and Sanjeev Swami. "System dynamics approach for adoption of artificial intelligence in finance." Advances in Systems Engineering: Select Proceedings of NSC 2019. Springer Singapore, 2021.
[15] Hildebrand, Christian, and Anouk Bergner. "Conversational robo advisors as surrogates of trust: onboarding experience, firm perception, and consumer financial decision making." Journal of the Academy of Marketing Science 49.4 (2021): 659-676.
[16] Ajmani, Prerna, et al. "Impact of AI in Financial Technology-A Comprehensive Study and Analysis." 2023 6th International Conference on Contemporary Computing and Informatics (IC3I). Vol. 6. IEEE, 2023.
[17] Raj, Arman, et al. "Enhancing security feature in financial transactions using multichain based blockchain technology." 2023 4th International Conference on Intelligent Engineering and Management (ICIEM). IEEE, 2023.
[18] Arghydeep, Dataset Gold and Silver Price Prediction with Rolling Regression and LSTM: https://github.com/Arghyadeep/Gold-and-Silver-Price-Prediction-with-Rolling-Regression-and-LSTM/blob/master/Gold%20Futures%20Historical%20Data.csv, Accessed on 25 August 2024.
