Design Science Research: A Practical Methodology for Enhancing Qualitative Liquidity Risk Management

Authors

  • Hamed Mirashk Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
  • Amir Albadvi Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
  • Mehrdad Kargari Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
  • Mohammad Ali Rastegar Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran
  • Mohammad Talebi Imam Sadiq University, Iran

DOI:

https://doi.org/10.34190/ejbrm.23.1.3544

Keywords:

Design science research (DSR), Proactive liquidity risk management, Liquidity risk scenarios, News sentiment, Predictive model

Abstract

In the banking sector, managing liquidity risk is paramount to ensure financial stability and resilience. This study is motivated by a quest to determine the appropriate research methodology that satisfies both theoretical and practical aspects of designing and developing a system that integrates qualitative factors, specifically news sentiment, into liquidity risk forecasting for risk managers to rely on and use the predicted results. Previous works reveal a significant theoretical gap in liquidity risk prediction, highlighting the necessity for a methodology that bridges theoretical advancements and practical applications. The primary questions focus on evaluating how well Design Science Research (DSR) handles short-term liquidity risk prediction and the influence of qualitative factors on these predictions. The DSR approach in this study involved iterative phases of problem identification, artifact creation, and rigorous evaluation. A predictive model was developed, intertwining news sentiment analysis with quantitative liquidity ratios derived from Basel III principles. The results demonstrate that the model achieves an 86% accuracy rate in theoretical evaluations and an impressive 95.5% in real-world scenarios, outperforming traditional methods. This integration of qualitative factors into the predictive model enhances accuracy, providing a more comprehensive understanding of liquidity risk dynamics. By meeting its objectives, this study answers the posed questions that DSR can be used as a research methodology that validates not only the theoretical aspect of the problem but also the practical application of the framework. The study contributes to advancing risk management practices and suggests future work directions, reinforcing the importance of DSR methodology and similar methods considering qualitative dimensions in banking liquidity risk assessment. This advancement paves the way for more proactive and informed decision-making processes in banking institutions.

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Published

24 Jan 2025

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