Predictive Analytics for Financial Risk Management in Dynamic Markets

Serkan Yilmaz Kandir^1,*, Murat Ismet Haseki²

¹Faculty of Economics and Administrative Sciences, Adana, Turkey

²Faculty of economics and administrative sciences, Cukurova University, Adana, Turkey

 Emails: skandir@cu.edu.tr; mhaseki@cu.edu.tr

Abstract

effective risk management is an indispensable requirement for improving the flow of transactions in dynamic financial markets. To this end, this study presents an applied predictive analytics methodology, that integrate gradient boosting algorithm to model the risk behavior in dynamic markets. This study, based on predictive analytics in monetary and financial systems, faces an urgent need for robust models that can overcome the uncertainties inherent in dynamic markets. Holistic experimentations on public case study of U.S retail data demonstrate the predictive power of the proposed approach of the state-of-the-art techniques across different performance metrics. This in turn highlights the nuanced interaction between variables and delivering intuitions into crucial risk determining factor.

Keywords: Business Intelligence; Machine Learning; Risk Managements; Market Analysis; Predictive Modeling