Monetary economics
Ali Cheshomi; Fariba Osmani
Abstract
Despite the recession in global financial markets, the Tehran stock Exchange experienced significant growth during the COVID-19 outbreak. Therefore, this article tries to solve this puzzle by analyzes the effect of three Coronavirus waves on the total index of Tehran stock Exchange, its sub-indices, ...
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Despite the recession in global financial markets, the Tehran stock Exchange experienced significant growth during the COVID-19 outbreak. Therefore, this article tries to solve this puzzle by analyzes the effect of three Coronavirus waves on the total index of Tehran stock Exchange, its sub-indices, and the different industries with daily data of Iran and the regression method with multiple breakpoints. The results show that each wave of COVID-19 have different effects on the stock market. COVID-19 in the first wave had a negative effect on the index of industries such as refined petroleum, chemical, Metals and transportation but had a positive effect on industries such as medicine and food. But in subsequent waves, response of different industries to the new pandemic is complicated for two reasons. First, the nominal exchange rate has positive and significant effect on main industries such as motor mehicles, banks, refined petroleum, metals and chemical (which have a considerable weight in the Tehran Stock Exchange), can show the positive trend of the index, especially in the first and second waves of the COVID-19. Second, the government's manipulatian in raising the stock prices of these main industries to finance its budget deficit caused the Tehran Stock Exchange index to move in the opposite direction in some periods in response to the Corona virus.
Econometrics
Amin Aminimehr; Ali Raoofi; Akbar Aminimehr; Amirhossein Aminimehr
Abstract
In this research, the impact of different preprocessing methods on the Long-Short term memory in predicting the financial time series was examined. At first, the model was implemented on the Tehran stock exchange index by utilizing the Principal Component Analysis (PCA) model on 78 technical indicators. ...
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In this research, the impact of different preprocessing methods on the Long-Short term memory in predicting the financial time series was examined. At first, the model was implemented on the Tehran stock exchange index by utilizing the Principal Component Analysis (PCA) model on 78 technical indicators. Then, the same model was implemented by the advantage of the random forest to select features rather than the PCA to extract them. In the next step, other technical strategy dummy variables were added to the model to examine the changes in its performance. Finally, two deep learning methods with the advantage of only target lags were deployed to compare the accuracy to the other models. The first deep model was plain but the second one was with the advantage of the Wavelet denoising process. The results of the MSE, MAE, MAPE, and R2 score on unseen test sequences showed that applying the Long Short-Term Memory with its own deep feature extraction procedure and the wavelet’s denoising process leads to the best accuracy in prediction of the Tehran stock exchange index. Finally, the Diebold Mariano test exposed a significant difference between the accuracy of the best model and the rest. This result implied that although the application of deep learning gains accurate results, it can be alleviated by feeding the model with creatively extracted and denoised features.
Ezatollah Abbasian; Ali Yehea Nemer
Abstract
Objectives: The Tehran Stock Exchange fell in recession in December 2013, as it roughly persisted until the end of 2015. However, there are significant differences in the various industries both in terms of the beginning of recession and in terms of the end of recession. By the evaluation of the Bulls ...
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Objectives: The Tehran Stock Exchange fell in recession in December 2013, as it roughly persisted until the end of 2015. However, there are significant differences in the various industries both in terms of the beginning of recession and in terms of the end of recession. By the evaluation of the Bulls and Bears Markets in the major industries in the Tehran Stock Exchange, the recession contexts can be identified and indicated. Method: In the present study, the Lunde and Timmermann approach is applied to identify the situation of the different industries during the period of November 2008 to February 2016 and, Cox regression is used to examine the impact of the industry type and the main macroeconomic variables effect on the continuation of the Bears Market.Result and Conclusions: The results indicated that since the beginning towards the end of the recession between the under-study industries, there are many differences, which can be used as a criterion to identify the key industries in the recession of the capital market. In addition, the results of Cox regression indicate that the type of industry and the inflation rate have a significant effect on the continuation of the Bears Market.
