مطالب

Today, social networks are fast and dynamic communication intermediaries that are a vital business tool. This study aims at examining the views of those involved with Facebook stocks so that we can summarize their views to predict the general behavior of this stock and collectively consider possible Facebook stock price movements, and create a more accurate pattern compared to previous patterns. In this study, we have analyzed two statistical samples, the first being a large dataset containing a variety of tweets with an emotional tag. That is, it needed a set that had already been extracted from each individual tweet by a trusted human or machine. Consequently, we have collected posts on Facebook in an eighty-day period. In this study, we used a tagged dataset using Python's programming language and vector-to-word algorithm. The research results show that, we need stock change information, machine learning and sentiment analysis, and on paper we conclude that positive news about a company excites people to have positive opinions about it which in turn results in people encouraging each other to buy and hold stocks. Meanwhile, the opposite trend is also true, but everything will not always be easy and clear, and it is in areas of high complexity and mental uncertainty that the art of using the three elements mentioned above is evident.

IIn recent years, policymakers have generally relied on regulatory policies to address financial stability concerns. However, our understanding of these policies and their efficacy in curbing housing prices is limited. In this paper, we examine the impact of three regulatory tools i.e. LTV (loan to value) ratio, reserve requirement rate (RR), and capital adequacy ratio (CAR) on housing price inflation in Iran for the1993: Q2 to 2017:Q1 period. We investigate whether tightening the policy tools are effective in curbing the housing price inflation by using a vector autoregressive model. The results indicate that all three regulatory policy tools exhibit countercyclical impact on housing inflation, but with varying degrees of influence. While the impact of CAR tightening in curbing housing prices is quite trivial, the impact of RR and LTV tightening are roughly significant.

In the Iranian economy, part of the government's fiscal policies and liabilities is always financed by banks. As government debt to banks increases, the private sector's access to loans and facilities is limited. It can cause undesirable macroeconomic outcomes. This study investigates the macroeconomic effects of government debt on banks in Iran over 1972–2016 by using an SVAR model. Results show that government debt to banks does not significantly affect the aggregate demand ratio to aggregate supply and GDP per labor. Still, it significantly increases the real exchange rate and decreases the non-tradable goods' ratio to tradable goods prices. In the long-run, the real exchange rate, the ratio of non-tradable goods to tradable goods price, and the general price level changed by 34.46, 20.95, and 46.4 percent, respectively, which can be explained by the government debt to banks. Results indicate that the government policy manages the Iranian economy.

The Sierpinski triangle is a fractal that is commonly used due to some of its characteristics and features. The Forex financial market is among the places wherein this triangle's characteristics are effective in forecasting the prices and their direction changes for the selection of the proper trading strategy and risk reduction. This study presents a novel approach to the Sierpinski triangle and introduces an innovative model based on it to forecast the direction changes in currency pairs, particularly EUR/USD. The model proposed in this study is dependent on the number of data selected for forecasting. The number of data is, in fact, the area of the initial triangle and the forecasted value of the self-similar triangles formed in each stage. For the performance assessment of the proposed method within one year (03/01/2019 to 28/02/2020), daily EUR/USD closed price data was classified into three categories, namely the training (70%), testing (20%), and validation (10%). Three approaches were proposed that led to forecasting the mean direction accuracy and the best result of over 60 percent in the third approach and over 50 percent in the first and second approaches. Results reflect the satisfactory improvements in the third approach compared to the econometrics, time-series, and machine learning methods. Moreover, the optimal number of data for the model is selected such that the difference between the accuracy of the direction forecasting in the training category and testing category is above 0.6 and below 0.05.

We seek to determine the optimal amount of the insurer’s investment in all types of assets for a small and closed economy. The goal is to detect the implications and contributions the risk seeker and risk aversion insurer commonly make and the effectiveness in the investment decision. Also, finding the optimum portfolio for each is the main goal of the present study. To this end, we adopted the optimal asset-liability management (ALM) method to control the firm's risk of financial stability and growth by balancing the assets and liabilities of the firm. In the process, stochastic interest rates and inflation risks were taken into account according to the expected utility maximization framework. All assets were established and calculated by the Kalman Filter with the stochastic interest rate following the Hull-White model; an additional stochastic process models the inflation risk. To consider the stochastic process, we employed the geometric Brownian motion in the liability process to ensure a definite liability value. We chose Iran’s Social Security Organization as our sample insurer company since it has a portfolio of five types of assets and four types of liabilities, and operates in a small and closed economy. By Applying the ALM method with the stochastic control theory approach, we acquire the optimal investment strategies for insurers to minimize their risk. Our findings demonstrate the effects of model parameters, such as the degree of risk-taking on the insurer decision.