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Machine Learning Applications in Predicting Bond Market Trends: Explore How Machine Learning Algorithms Can Be Used to Predict Movements in the Fixed Bond Market
Version 1
: Received: 25 April 2024 / Approved: 25 April 2024 / Online: 25 April 2024 (11:20:21 CEST)
How to cite:
Kapil, D.; Yamini, K. Machine Learning Applications in Predicting Bond Market Trends: Explore How Machine Learning Algorithms Can Be Used to Predict Movements in the Fixed Bond Market. Preprints2024, 2024041667. https://doi.org/10.20944/preprints202404.1667.v1
Kapil, D.; Yamini, K. Machine Learning Applications in Predicting Bond Market Trends: Explore How Machine Learning Algorithms Can Be Used to Predict Movements in the Fixed Bond Market. Preprints 2024, 2024041667. https://doi.org/10.20944/preprints202404.1667.v1
Kapil, D.; Yamini, K. Machine Learning Applications in Predicting Bond Market Trends: Explore How Machine Learning Algorithms Can Be Used to Predict Movements in the Fixed Bond Market. Preprints2024, 2024041667. https://doi.org/10.20944/preprints202404.1667.v1
APA Style
Kapil, D., & Yamini, K. (2024). Machine Learning Applications in Predicting Bond Market Trends: Explore How Machine Learning Algorithms Can Be Used to Predict Movements in the Fixed Bond Market. Preprints. https://doi.org/10.20944/preprints202404.1667.v1
Chicago/Turabian Style
Kapil, D. and Kannan Yamini. 2024 "Machine Learning Applications in Predicting Bond Market Trends: Explore How Machine Learning Algorithms Can Be Used to Predict Movements in the Fixed Bond Market" Preprints. https://doi.org/10.20944/preprints202404.1667.v1
Abstract
This paper explores the application of machine learning (ML) algorithms in predicting trends in the fixed bond market, where traditional analytical methods have proven inadequate. Focusing on various ML techniques such as supervised and unsupervised learning, neural networks, and deep learning, the study evaluates their effectiveness in forecasting market movements. It details a series of experiments in which different ML models are rigorously trained and tested against historical bond market data. The findings reveal that models employing time series analysis and advanced deep learning show marked potential in accurately predicting bond market trends. Additionally, the paper delves into the challenges and limitations inherent in these ML approaches, including data requirements and the risk of model overfitting. Finally, it proposes directions for future research, emphasizing the integration of ML into broader financial market analysis.
Keywords
Machine Learning (ML); Bond Market Analysis; FinTech; Supervised Learning; Unsupervised Learning; Neural Networks; Deep Learning; Time Series Analysis; Bond Market Forecasting; Risk Management in Finance; Algorithmic Trading; Data Preprocessing in Finance; Model Overfitting
Subject
Business, Economics and Management, Finance
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
