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The Impact of International Sanctions on the Stock Market of Iran, Using Automated Content Analysis Methods

Mahmoudi Meymand, Mohammad | 2019

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 52525 (44)
  4. University: Sharif University of Technology
  5. Department: Management and Economics
  6. Advisor(s): Barakchian, Mahdi; Ebrahimnejad, Ali
  7. Abstract:
  8. In this study, we provide a detailed analysis of the Iranian stock market, in particular, the impacts of the recent sanctions imposed on this country. We develop a novel economics concept, sanction-index, to measure the effect of sanctions on the Iranian stock market. We utilize a set of principled approach on news analysis and automated content analysis methods and construct an extensive dataset using over a decade of World Economics newspaper, from 2009 to2019. This newsletter is a highly celebrated and most influential newspaper in the Iranian stock market society. Employing this dataset, we search over sanctions-related keywords to identify sanctions-related articles from the set of all articles. We then deploy the data preprocessing methods on these articles. After removing the redundant terms, named stop-words, from the dataset, we count the number of words with the negative tone in each article and obtain the sanction-index using the ratio of the number of negative words to the total number of remained words. We show that this index is directly correlated with the history of sanctions in Iran. Next, we identify the long-term relationship between the sanction-index and other control variables within the stock market index. For this purpose, we deploy the ARDL error correction model and confirm the existence of a long-run relationship between these variables and the sanction-index using the boundary test method. After removing the effect of these control variables, we determine that there exists a significant negative correlation between the sanction-index and the stock market index in the long-run
  9. Keywords:
  10. Sanction ; Stock Market ; Autoregressive Distributed Lag Model ; Political Events ; Automated Content Analysis Method ; Empirical Error Correction Model (ECM)

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