Liquidity Risk Prediction Using News Sentiment Analysis
Subject Areas : ICThamed mirashk 1 , albadvi albadvi 2 , mehrdad kargari 3 , Mohammad Ali Rastegar 4 , Mohammad Talebi 5
1 - ُStudent
2 - Tarbiat Modares University
3 -
4 - Assistant Professor
5 - Imam Sadiq University
Keywords: Liquidity risk prediction, machine learning, sentiment analysis, scenario analysis, design science rese,
Abstract :
One of the main problems of Iranian banks is the lack of risk management process with a forward-looking approach, and one of the most important risks in banks is liquidity risk. Therefore, predicting liquidity risk has become an important issue for banks. Conventional methods of measuring liquidity risk are complex, time-consuming and expensive, which makes its prediction far from possible. Predicting liquidity risk at the right time can prevent serious problems or crises in the bank. In this study, it has been tried to provide an innovative solution for predicting bank liquidity risk and leading scenarios by using the approach of news sentiment analysis. The news sentiment analysis approach about one of the Iranian banks has been used in order to identify dynamic and effective qualitative factors in liquidity risk to provide a simpler and more efficient method for predicting the liquidity risk trend. The proposed method provides practical scenarios for real-world banking risk decision makers. The obtained liquidity risk scenarios are evaluated in comparison with the scenarios occurring in the bank according to the guidelines of the Basel Committee and the opinion of banking experts to ensure the correctness of the predictions and its alignment. The result of periodically evaluating the studied scenarios indicates a relatively high accuracy. The accuracy of prediction in possible scenarios derived from the Basel Committee is 95.5% and in scenarios derived from experts' opinions, 75%.
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