Evaluating the "framing effect" on the exchange rate in Iran using machine learning methods

Document Type : Research Paper

Authors

1 Professor, Department of Economics, Semnan University, Semnan, Iran.

2 PhD candidate in the Department of Economics, Semnan University, Semnan, Iran.

3 Assistant Professors, Department of Economics, Semnan University, Semnan, Iran.

Abstract

Purpose: In recent years, most of the currency fluctuations in Iran have occurred after the spread of influential domestic and foreign political-economic news. Despite this significant effect, the impact of the news content is not considered, and its results are used in the form of numerical data in modeling. However, numerical indirect results are available after the impact of the news on the market and are practically useless in forecasting. Therefore, there needs to be a model that includes the news directly in forecasting the exchange rate. Another important phenomenon about the exchange rates and news is the framing effect. This effect is a linguistic-cognitive phenomenon resulting in people with two different attitudes, positive and negative, making different choices regarding the same item or reality. While this action is done explicitly or implicitly in policymakers' announcements, criticisms, and statements, there needs to be a systematic way to analyze and predict its effect on the exchange rate. This paper presents a comprehensive model based on machine learning to predict exchange rate moves by receiving all kinds of textual, numerical, and categorical data. Also, using this model, it is possible to analyze the effect of framing on exchange rate changes systematically.
Methodology: A processing method has been designed to predict the exchange rate from receiving data to producing the final model. In this method, various types of input data, including time series data, batch data, and political-economic news, are received and preprocessed from essential and relevant sources. In numerical data preprocessing, noises and outliers are removed, and values are normalized. Text data are also the texts normalized and converted into tokens. In the next step, custom embedding is provided based on Pars-BERT embedding and the collected news data. Using this embedding, the textual data are converted into numerical vectors, and all the data are given to the model. Here are three supervised models. The first and third models use textual and numerical data to predict the exchange rate, and the second model only uses numerical data. The core of all the models is a Bi-GRU, a deep neural network. Dropout and batch normalization and regularization techniques have been used in these models to avoid overfit and bias.
Findings and discussion: The tests performed to evaluate the models' efficiency are divided into two parts. The first part evaluates all the three developed models and their results. The second part is dedicated to assessing and analyzing the issue of news framing and the model's performance in this field. Based on the accuracy metric, the first to third models perform properly at the rate of 96.5, 87.2 and 97.07, respectively. The difference between the results of the second model and the first and third ones shows the effect of news on increasing the model's accuracy. In the second part, a dataset of the news affecting the exchange rate with different framings is prepared to evaluate the effect of framing. This set includes 74 main pieces of news and 435 secondary ones. In approximately 32% of the cases, where the news was announced to reduce the rate, the behavior of the market matched the purpose of the report. Meanwhile, in 68% of the other cases, the news that was expected to have a downward effect on the exchange rate did not match the market behavior. This can indicate their incorrect framing. On the other hand, the news that is expected to have a negative (increasing) effect on the exchange rate has worked about 78% of the time and has led to an increase in the rate. This observation also confirms the imbalance effect of positive and negative news on the currency market. Interestingly, the first and third models correctly identified 98.8% and 99.05% of the samples, including negative and positive news of internal and external origins, respectively. This indicates that the models resist different framings of a news story and have made correct predictions.
Conclusions and policy implications: In this article, using machine learning models, an operational method was presented to investigate the effect of framing on the exchange rate in Iran. The proposed architecture includes the five stages of preprocessing textual, numerical and categorical data, data vector representation, feature engineering, model training, and final evaluation and analysis. In the stage of textual data representation, customized embedding is used for economic data. In this regard, there are three models presented, including a model based on numerical data and two models based on numerical and textual data with a data fusion structure. All the three models are recurrent neural networks trained with deep learning techniques. The results of the experiments show that the models with combined data perform very well in forecasting the exchange rate. In addition to predicting the exchange rate, the produced models have been used to analyze the effect of the framing of the published news on the currency market. The research results show that negative news still has a far more significant impact on the currency market, and no positive framing can reduce its impact. Also, the mainly domestic report published to reduce the exchange rate has been successful in about 32% of the cases. Another significant achievement is the excellent performance of the presented models in framed samples. In other words, these models have correctly predicted market behavior by considering different aspects of a news story, news history, market history, and information, regardless of the type of the news framing.

Keywords

Main Subjects

References

Abunouri, A., Khanalipour, A., & Abbasi, J. (2009). The Effect of News on Exchange Rate Fluctuations in Iran: An Application of the ARCH Family. Business Journal, 50, 101-120. (In Persian)
Bayat, N. (2018). Forecasting the Exchange Rate Using Self-Organizing Ten-Recurrence Maps. Modern Economy and Business, 13, 55-84. (In Persian)
Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017). Enriching Word Vectors with Subword Information. Transactions of the Association for Computational Linguistics, 5, 135-146.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., & Askell, A. (2020). Language Models Are Few-Shot Learners. Advances in Neural Information Processing Systems, 33, 1877-1901.
Chan, S. W., & Chong, M. W. (2017). Sentiment Analysis in Financial Texts. Decision Support Systems, 94, 53-64.
Damiri, M., Saeedi, P., Didehkhani, H., & Abbasi, A. (2020). Modeling Exchange Rate Fluctuations with System Dynamics Approach. Financial Engineering and Securities Management, 11(43), 220-244. (In Persian)
Danielsson, S., & Gramer, A. (2023). Predicting Forex Rates Using Sentiment Analysis on Financial Articles. Journal of Behavioral and Experimental Economics, 103, 101-109.
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint, arXiv:1810.04805.
Dharshing, S., Hille, S. L., & Wüstenhagen, R. (2017). The Influence of Political Orientation on the Strength and Temporal Persistence of Policy Framing Effects. Ecological Economics, 142, 295-305.
Farahani, M., Gharachorloo, M., Farahani, M., & Manthouri, M. (2021). Parsbert: Transformer-based Model for Persian Language Understanding. Neural Processing Letters, 53, 3831-3847.
Feuerriegel, S., & Prendinger, H. (2016). News-based Trading Strategies. Decision Support Systems, 90, 65-74.
Galperti, S., & Cerigioni, F. (2023). Listing Specs: The Effect of Framing Attributes on Choice. Journal of the European Economic Association, 7, 32-38.
Hashmi-Dizaj, A., Hazeri-Niri, H., & Pour-Vahdani, R. (2020). Comparing the Performance of Artificial Neural Network Models for Predicting the Exchange Rate in Iran. Biquarterly Scientific Journal of Economic Studies and Policies, 7(2), 53-80. (In Persian)
Heaton, J. (2016). An Empirical Analysis of Feature Engineering for Predictive Modeling. SoutheastCon 2016.
Jiménez-Jiménez, F., & Rodero-Cosano, J. (2023). Conditioning Competitive Behavior in Experimental Bertrand Markets Through Contextual Frames. Journal of Behavioral and Experimental Economics, 103, 101-109.
Josse, J., & Husson, F. (2012). Handling Missing Values in Exploratory Multivariate Data Analysis Methods. Journal de la société française de statistique, 153(2), 79-99.
Kahneman, D., & Tversky, A. (1979). Prospect theory. Econometrica, 47, 39-48.
Kapoor, A., Gulli, A., Pal, S., & Chollet, F. (2022). Deep Learning with TensorFlow and Keras: Build and deploy supervised, unsupervised, deep, and reinforcement learning models. Packt Publishing Ltd.
Khadaveisi, H., & Mollabahrami, A. (2012). Modeling and Forecasting Exchange Rates Based on Stochastic Differential Equations. Economic Research, 100(47), 129-144. (In Persian)
Khan, W., Daud, A., Khan, K., Muhammad, S., & Haq, R. (2023). Exploring the Frontiers of Deep Learning and Natural Language Processing: A Comprehensive Overview of Key Challenges and Emerging Trends. Natural Language Processing Journal, 100-126.
Khashee, M., Bijari, M., & Mokhatab-Rafiei F., (2012). Forecasting Exchange Rates Using Hybrid Models of Multilayer Perceptrons (MLPs) and Probabilistic Neural Classifiers (PNNs). Scientific Research Journal of Numerical Methods in Engineering, 32(1), 1-14. (In Persian)
Kühberger, A. (1998). The Influence of Framing on Risky Decisions: A meta-analysis. Organizational Behavior and Human Decision Processes, 75(1), 23-55.
Li, Y., & Yang, T. (2018). Word Embedding For Understanding Natural Language: A Survey. Guide to Big Data Applications, 83-104.
Liebmann, M., Orlov, A. G., & Neumann, D. (2016). The Tone of Financial News and the Perceptions of Stock and CDS Traders. International Review of Financial Analysis, 46, 159-175.
Liu, J., Li, T., Xie, P., Du, S., Teng, F., & Yang, X. (2020). Urban Big Data Fusion Based on Deep Learning: An Overview. Information Fusion, 53, 123-133.
Lopez-Lira, A., & Tang, Y. (2023). Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models. arXiv preprint, arXiv:2304.07619.
Lunsford, K. G. (2020). Policy Language and Information Effects in the Early Days of Federal Reserve Forward Guidance. American Economic Review, 110(9), 2899-2934.
Mansourigargari, H., & Khodavisi, H. (2019). Exchange Rate Forecasting: Comparing Logistics Growth Patterns with Competing Patterns. Economics and modeling, 10, 141-179. (In Persian)
Marzban, D. H., Javaheri, B., Behnam, & Akbarian. (2005). A Comparison Between Structural, Time Series and Neural Network Econometric Models for Forecasting Exchange Rates. Journal of Economic Research, 40(2). (In Persian)
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. arXiv preprint, arXiv:1301.3781.
Morales, E. F., & Escalante, H. J. (2022). A Brief Introduction to Supervised, Unsupervised, and Reinforcement Learning. In Biosignal Processing and Classification Using Computational Learning and Intelligence (pp. 111-129). Elsevier.
Nargesian, F., Samulowitz, H., Khurana, U., Khalil, E. B., & Turaga, D. S. (2017). Learning Feature Engineering for Classification. IJCAI, 196-235.
Nassirtoussi, A. K., Aghabozorgi, S., Wah, T. Y., & Ngo, D. C. L. (2015). Text mining of News-headlines for FOREX Market Prediction: A Multi-layer Dimension Reduction Algorithm with Semantics and Sentiment. Expert Systems with Applications, 42(1), 306-324.
Nayak, A. S., Kanive, A. P., Chandavekar, N., & Balasubramani, R. (2016). Survey on Pre-processing Techniques for Text Mining. International Journal of Engineering and Computer Science, 5(6), 16875-16879.
Pennington, J., Socher, R., & Manning, C. D. (2014). Glove: Global Vectors for Word Representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP).
Potdar, K., Pardawala, T. S., & Pai, C. D. (2017). A Comparative Study of Categorical Variable Encoding Techniques for Neural Network Classifiers. International Journal of Computer Applications, 175(4), 7-9.
Sarzynska-Wawer, J., Wawer, A., Pawlak, A., Szymanowska, J., Stefaniak, I., Jarkiewicz, M., & Okruszek, L. (2021). Detecting Formal Thought Disorder by Deep Contextualized Word Representations. Psychiatry Research, 304, 114-135.
Seabe, P. L., Moutsinga, C. R. B., & Pindza, E. (2023). Forecasting Cryptocurrency Prices Using LSTM, GRU, and Bi-directional LSTM: a Deep Learning Approach. Fractal and Fractional, 7(2), 203.
Semiromi, H. N., Lessmann, S., & Peters, W. (2020). News Will Tell: Forecasting Foreign Exchange Rates Based on News Story Events in the Economy Calendar. The North American Journal of Economics and Finance, 52, 101-111.
Shilpa, B., & Shambhavi, B. (2021). Combined Deep Learning Classifiers for Stock Market Prediction: Integrating Stock Price and News Sentiments. Kybernetes, 52(3), 748-773.
Shirazi, Homayun, & Nasrallahi. (2014). Monetary Models and Exchange Rate Forecasting in Iran: From Theory to Empirical Evidence. Financial and Economic Policy Quarterly, 1(4), 5-24. (In Persian)
Shynkevich, Y., McGinnity, T. M., Coleman, S. A., & Belatreche, A. (2016). Forecasting Movements of Health-care Stock Prices Based on Different Categories of News Articles Using Multiple Kernel Learning. Decision Support Systems, 85, 74-83.
Singh, D., & Singh, B. (2020). Investigating the Impact of Data Normalization on Classification Performance. Applied Soft Computing, 97, 105-124.
Singh, J., & Gupta, V. (2016). Text Stemming: Approaches, Applications, and Challenges. ACM Computing Surveys (CSUR), 49(3), 1-46.
Sousa, M. G., Sakiyama, K., de Souza Rodrigues, L., Moraes, P. H., Fernandes, E. R., & Matsubara, E. T. (2019). BERT for Stock Market Sentiment Analysis. IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI).
Sun, A., Lachanski, M., & Fabozzi, F. J. (2016). Trade the Tweet: Social Media Text Mining and Sparse Matrix Factorization For Stock Market Prediction. International Review of Financial Analysis, 48, 272-281.
Tagvi, M., & Khodam, M. (2011). A Comparative Study of the Effectiveness of Currency Theories in Predicting Exchange Rate Changes in the International Currency Exchange Market. Financial Knowledge of Securities Analysis (Financial Studies), 9, 147-192. (In Persian)
Tversky, A., & Kahneman, D. (1981). The Framing of Decisions and the Psychology of Choice. Science, 211(4481), 453-458.
Villamil, L., Bausback, R., Salman, S., Liu, T. L., Horn, C., & Liu, X. (2023). Improved Stock Price Movement Classification Using News Articles Based on Embeddings and Label Smoothing. arXiv preprint, arXiv:2301.10458.
Yarmohammadi, M., & Mahmovand, R. (2015). Forecasting the Exchange Rate Using the Method of Analyzing the Set of Singular Values. Quarterly Journal of Applied Economics Studies in Iran, 18, 146-137. (In Persian)
Zaranjad, M., Feqh Majidi, A., & Rezaei, R. A. (2008). Forecasting Exchange Rate Using Artificial Neural Networks and ARIMA Model. Quantitative Economics (Economic Reviews), 19, 107-130. (In Persian).
The Journal of Economic Policy
Volume 17, Issue 33
September 2025
Pages 212-247

Files

Share

How to cite

Statistics