Journal of Artificial Intelligence and Metaheuristics
Journal DOI
2833-5597ISSN (Online)
Volume 4 , Issue 2 , PP: 36-44, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
S. K. Towfek 1 *
Doi: https://doi.org/10.54216/JAIM.040204
In volatile markets, risk mitigation and analysis play a crucial role in ensuring financial stability and profitability. This paper presents a new framework for risk mitigation and analysis tailored specifically for volatile markets. The framework combines data analysis, statistical modeling, and domain expertise to provide a inclusive and proactive approach to managing risks. The key theories and beliefs underlying the framework are discussed, with a focus on the use of logistic regression as the core risk predictor. The framework's development process, including data collection and preprocessing, feature engineering, and model selection, is outlined. Moreover, the incorporation of the Weight of Evidence (WoE) technique to enhance the interpretability and effectiveness of the logistic regression model is explained. The proposed framework aims to encourage market participants with valuable insights into risk levels and facilitate informed decision-making and effective risk mitigation strategies in volatile market environments.
Risk mitigation , risk analysis , volatile markets , framework , logistic regression , data analysis, statistical modeling , domain expertise , proactive approach , risk predictor , Weight of Evidence (WoE) , decision-making.
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