Volume 19, Issue 2 (6-2024)
J. Mon. Ec. 2024, 19(2): 249-274 |
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Mortazavian Farsani S J, Toloie Eshlaghy A, Radfar R. Prediction the Short-term Exchange Rate of USD/IRR Using Deep Learning and the Impact of Sentiment Analysis Features on it. J. Mon. Ec. 2024; 19 (2) : 6
URL: http://jme.mbri.ac.ir/article-1-688-en.html
URL: http://jme.mbri.ac.ir/article-1-688-en.html
1- Department of Information Technology Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University
2- Department of Industrial Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University
2- Department of Industrial Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University
Abstract: (2025 Views)
This study investigates the role of sentiment analysis in improving exchange rate prediction models, providing empirical evidence for narrative economics; the idea that economic outcomes are shaped by prevailing beliefs and popular narratives. By integrating sentiment-based features into predictive frameworks, we demonstrate that exchange rate movements are influenced by subjective factors beyond traditional economic variables. Our findings suggest that market sentiment systematically impacts currency fluctuations. To assess the effectiveness of sentiment-enhanced models, we compare various forecasting approaches. Notably, a generalized linear model (GLM) outperforms more complex deep learning architectures, including long short-term memory (LSTM) networks and hybrid CNN-LSTM models. Additionally, even an optimized multilayer perceptron (MLP) fails to surpass GLM performance, highlighting the potential linearity of the relationship between predictors and exchange rates. These results underscore the importance of aligning model complexity with the statistical properties of the target variable. Beyond exchange rate forecasting, our study underscores the broader significance of incorporating sentiment and narratives into economic models. By acknowledging the role of subjective beliefs, researchers and policymakers can enhance predictive accuracy and improve decision-making processes in financial markets.
Article number: 6
Keywords: Prediction, Narrative Economics, Foreign Exchange Rate, Deep Learning Models, Sentiment Analysis, Social Network
Type of Study: Original Research - Empirical |
Subject:
Economics
Received: 9 Apr 2025 | Accepted: 1 Jun 2025 | Published: 29 Jun 2025
Received: 9 Apr 2025 | Accepted: 1 Jun 2025 | Published: 29 Jun 2025
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