TY - GEN
T1 - Machine Learning for Predictive Risk Analytics in Industry 4.0
T2 - 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024
AU - Asante, Oliver J.
AU - Rusliem, Enrico W.
AU - Pan, Yue
AU - Basarah, Michelle A.
AU - Alin, Cashmere B.R.
AU - Zhang, Guangfeng
AU - Chen, Yi
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - In financial forecasting, where navigating uncertainties is crucial for informed decision-making, risk management plays an essential role. Large datasets and market fluctuations can lead to inaccurate forecasts and significant losses. Fortunately, artificial intelligence and machine learning algorithms offer promising solutions. The Random Forest Model, a commonly used machine learning algorithm, has the potential to alleviate these issues. Unlike conventional models that rely on a single algorithm, Random Forest aggregates multiple decision trees, each analysing a subset of data and identifying key variables. Averaging the predictions using this method appeared to reduce bias, improve accuracy, and make it adept at handling messy financial data. This paper aims to provide an in-depth analysis of Random Forest’s potential in financial forecasting and risk management, especially evaluating its effectiveness and practicality in the field.
AB - In financial forecasting, where navigating uncertainties is crucial for informed decision-making, risk management plays an essential role. Large datasets and market fluctuations can lead to inaccurate forecasts and significant losses. Fortunately, artificial intelligence and machine learning algorithms offer promising solutions. The Random Forest Model, a commonly used machine learning algorithm, has the potential to alleviate these issues. Unlike conventional models that rely on a single algorithm, Random Forest aggregates multiple decision trees, each analysing a subset of data and identifying key variables. Averaging the predictions using this method appeared to reduce bias, improve accuracy, and make it adept at handling messy financial data. This paper aims to provide an in-depth analysis of Random Forest’s potential in financial forecasting and risk management, especially evaluating its effectiveness and practicality in the field.
KW - Financial Forecasting
KW - Machine Learning
KW - Random Forest Model
KW - Risk Management
KW - Stock Market Prediction
KW - Walk-Forward Validation
UR - https://www.scopus.com/pages/publications/105002729246
U2 - 10.1007/978-981-96-3949-6_7
DO - 10.1007/978-981-96-3949-6_7
M3 - Conference Proceeding
AN - SCOPUS:105002729246
SN - 9789819639489
T3 - Lecture Notes in Networks and Systems
SP - 92
EP - 111
BT - Selected Proceedings from the 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 - Advances in Intelligent Manufacturing and Robotics
A2 - Chen, Wei
A2 - Ping Tan, Andrew Huey
A2 - Luo, Yang
A2 - Huang, Long
A2 - Zhu, Yuyi
A2 - PP Abdul Majeed, Anwar
A2 - Zhang, Fan
A2 - Yan, Yuyao
A2 - Liu, Chenguang
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 22 August 2024 through 23 August 2024
ER -