Measuring the Media-based Economic Uncertainty index by Machine Learning Algorithms in Iran and its Effect on the Exchange Rate

Economic uncertainty is one of the important and influential factors on economic policies and their results, and in such a situation, rational decisions are replaced by other methods. Various studies has shown the effect of economic uncertainty on inflation, investment, economic growth, consumption and demand for money.Uncertainty is difficult to measure due to its invisibility, and as the uncertainty measurement methods improve, the measurement of its effect on various economic variables and markets and the prediction of their behavior in response to the actions of economic agents will be more accurate.The main aim of this article is to measure the economic uncertainty index by using news published in social networks. This method of measurement has become very important with the widespread use of social networks.

 THEORETICAL FRAMEWORK:

Uncertainty is one of the most controversial concepts in the philosophy and methodology of economics. The history of the concept of economic uncertainty goes back to David Hume. There are three categories of theories about economic uncertainty. The first group believes that the future reality is unchangeable and predetermined and economic decision makers have perfect information. In this view, there is no such thing as uncertainty and the world is in complete certainty. 18th century the economists of were the first group to present this theory. The second group believes that the reality of the future is unchangeable and predetermined and the decision makers are able to know the future. These economists use objective conditional probability functions to solve the future uncertainty problem. The third class considers the future reality to be changeable and unknown. The starting point of these theories started from the study of the Chicago school economist Frank Knight titled "Risk, Uncertainty and Profit". He clearly distinguished between the two concepts of risk and uncertainty. Keynes also reached the same results as Knight. In general, in a situation where the economy has a high level of uncertainty, the theories of the first and second category have a good explanation. But in confronting with exogenous shocks such as the corona virus epidemic, war and financial crisis, the concept of uncertainty will be more appropriate in the theories of the third category. This study will measure this index based on fundamental uncertainty (the third category).

In this article, the economic uncertainty index in Iran was measured from January 2017 to December 2020 by monitoring and analyzing 3,117,960 news from 28 popular and influential Iranian Telegram channels. To analyze these news, we used "supervised machine learning" methods. In the first step, 13,404 news items were labeled by human evaluators according to their impact on uncertainty. The labels had two modes "affecting uncertainty" and "neutral". Then by using four algorithms ("C4.5" from decision tree methods, "Multilayer Perceptron" from artificial neural network methods, "Logistics" from function-oriented methods and "Simple Bayes" from Bayesian methods) labeling of the whole news was done. The economic uncertainty index was calculated numerically and based on the number of news items that affect economic uncertainty, the measurement and value of this index was standardized, and then the quality of the index was evaluated with historical evidence, relabeling and comparison with the index based on Google data.

Among the 4 media-based uncertainty indicators, 3 indicators can better explain the historical events of this period. Among them, the best performance is determined by C4.5 algorithm from the decision tree methods. After this algorithm, multilayer perceptron, logistic has the best performance and the weakest performance belongs to the simple Bayes method. Media-based economic uncertainty index trend with C4.5 method is consistent with the important events of the study period, in such a way that the highest level of uncertainty occurred during the period when Trump announced his withdrawal from the JCPOA until the official withdrawal of the United States from the JCPOA. In general, it can be said that the fluctuations of the economic uncertainty index have been limited and have several jumps, which are due to the withdrawal of the United States from the JCPOA, the oil embargo and the assassination of Sardar Soleimani.In the logistic algorithm, the highest level of uncertainty dates back to the end of 2020. The period that coincides with Trump's presidential election. The level of economic uncertainty increases after Trump's official withdrawal from the JCPOA and reaches its peak with oil sanctions.The output of the multilayer perceptron algorithm indicates that the average level of uncertainty has not changed significantly.  In the simple Bayes algorithm, the highest level was also reached during the period of the withdrawal of the United States from the JCPOA and the increase in enrichment.  The results of the regression showed that economic uncertainty has a positive and significant effect on the average logarithm of the exchange rate with multilayer perceptron, logistic and simple methods. This effect is larger in the multilayer perceptron model, which had better performance based on machine learning indicators.

CONCLUSIONS & SUGGESTIONS:

The calculated economic uncertainty index is consistent with the important events of the study period, such as the US withdrawal from the JCPOA, Iran’soil sanctions, and the escalation of the US confrontation with Iran in the assassination of Sardar Soleimani. It is suggested that daily calculation of this index be used to reduce uncertainty in the managing future events. We employed GARCH model to test effect of Media-based Economic Uncertainty index on Iranian exchange rate. The results showed that Economic Uncertainty index has poisitve effect on exchange rate.