The purpose of this study is to investigate the impact of financial development on efficiency of monetary policy in Iran during 1979-2020. The ratio of banks' domestic credit to GDP was considered as an indicator of financial development based on banking sector and rati More
The purpose of this study is to investigate the impact of financial development on efficiency of monetary policy in Iran during 1979-2020. The ratio of banks' domestic credit to GDP was considered as an indicator of financial development based on banking sector and ratio of the value of stock market transactions to GDP was considered as an indicator of financial development based on the capital market. In this regard, 4 models were introduced to achieve research objectives and were estimated using the Kalman-Filter approach. The results of estimating the first two models of the research showed that with improvement of financial development indicators, the efficiency of monetary policy in influencing economic growth will decrease. The results of estimating the third and fourth models of the study also showed that effect of financial development indicators on efficiency of monetary policy in impact on inflation has been negative and statistically significant, meaning that with improvement of financial development indicators in country, monetary policies will lead to lower inflation.
Manuscript profile
AbstractSince the creation of the stock market in the nineteenth century, many researchers have focused on research into stock price forecasting models and market returns. Statistical prediction models such as Arma, Arima, Arch, have been widely used but none of them ha More
AbstractSince the creation of the stock market in the nineteenth century, many researchers have focused on research into stock price forecasting models and market returns. Statistical prediction models such as Arma, Arima, Arch, have been widely used but none of them have had the desired result. Therefore, many researchers have recently considered the stock market as a nonlinear dynamic system. The application of nonlinear models as well as advanced techniques, although not many years have begun, but in a short time has been able to open its place in various sciences. The purpose of this study is to predict the stock index using the dynamic model averaging DMA and also the method of the dynamic model selective DMS and the use of quarterly data for the years 1380-1399. The main advantage of the model used in the present study is the introduction of a large number of independent variables for its dynamics without the usual problem of overfitting appearing in the model. In this paper, the effect of some macroeconomic variables on the process of modeling and forecasting stock returns on the stock exchange was investigated. The results of the article showed that the probability of entering the variables of money supply growth, quasi-money growth, inflation, land price index growth in large cities is more than other input variables.
Manuscript profile
Abstract
With the increasing popularity and widespread use of cryptocurrencies, the creation and development of methods for predicting price movements in this field has attracted a lot of attention. In between, recent developments in deep learning (DL) models with stru More
Abstract
With the increasing popularity and widespread use of cryptocurrencies, the creation and development of methods for predicting price movements in this field has attracted a lot of attention. In between, recent developments in deep learning (DL) models with structures such as long-short-term memory (LSTM) and convolutional neural network (CNN) have made improvements in the analysis of this type of data. Another approach that can be effective in the analysis of cryptocurrencies time series is the decomposition through algorithms such as complete integrated empirical mode decomposition (CEEMD). Considering the importance of forecasting in the cryptocurrencies field, in this research, by combining deep learning models and complete integrated empirical mode decomposition (CEEMD), The hybrid CEEMD-DL(LSTM) model has been used to forecast the bitcoin return (as the most popular currency). In this regard, the daily data of the total index of the Tehran Stock Exchange was used in the period of 2013/01/01 – 2022/05/28 and the results obtained were compared with the results of competing models based on efficiency measurement criteria. Based on the obtained results, the use of the introduced model (CEEMD-DL(LSTM)) has increased the efficiency and accuracy of bitcoin return forecasting. Accordingly, the use of this model in this field is suggested.
Manuscript profile
Sanad
Sanad is a platform for managing Azad University publications