درخت حوزه‌های تخصصی

In the present study, the role of quantitative easing on the stability of financial markets in Iran has been analyzed from the quarterly data of 2006-2021 using the econometric model with vector error correction model (VECM). To estimate the model from some observable variables, the effects of quantitative easing on financial market variables, including (liquidity volume, capital adequacy rate, credits granted to the private sector by banks, bank deposit interest rate, stock market index and exchange rate) were estimated. The data was extracted from the official website of the Securities and Exchange Organization and the Central Bank. In VECM models, the estimation was done in both short-term and long-term periods with the aid of Eviews software. The findings of the research, based on the estimation of long-term and short-term relationships, show that quantitative easing has a positive and significant effect on the stability of financial markets. With the MP1 index increasing by one percent, the Financial Market Stability Index increased by 4.157 percent.

Stock market activists are trying to find and apply methods so that they can increase the profit of their capital by predicting the future stock price. Therefore, it seems necessary that appropriate, correct and scientific-base principles methods are used to determine the future price of stocks for investors. Economists use econometric methods for forecasting in most cases. Therefore, the aim of this research was to present a new approach in predicting the types of stock movement trends in water-intensive industries using the decision tree approach. The local domain of this research was the companies listed on the Tehran Stock Exchange active in water-intensive industries and the time do-main was during 2016-2020. In the data section, the research was done by collecting the data of the sample companies by referring to the financial statements, explanatory notes and the stock exchange monthly journal; Based on the systematic elimination method, 72 companies active in water-intensive industries were selected as a statistical sample. In order to describe and summarize the collected data, descriptive and inferential statistics have been used using the decision tree approach; the results showed that by using the decision tree approach, it is possible to predict the profit, industry and stock price trends in water-intensive industries. The results obtained in this research are consistent with the documents mentioned in the theoretical framework of research and financial literature.

In this article, the pricing of option contracts is discussed using the Mikhailov and Nogel model and the artificial neural network method. The purpose of this research is to investigate and compare the performance of various types of activator functions available in artificial neural networks for the pricing of option contracts. The Mikhailov and Nogel model is the same model that is dependent on time. In the design of the artificial neural network required for this research, the parameters of the Mikhailov and Nogel model have been used as network inputs, as well as 700 data from the daily price of stock options available in the Tehran Stock Exchange market (in 2021) as the net-work output. The first 600 data are considered for learning and the remaining data for comparison and conclusion. At first, the pricing is done with 4 commonly used activator functions, and then the results of each are com-pared with the real prices of the Tehran Stock Exchange to determine which item provides a more accurate forecast. The results obtained from this re-search show that among the activator functions available in this research, the ReLU activator function performs better than other activator functions.