Volume 12, Issue 2 (9-2022)
Abstract
The probability of stock prices crash has great importance in portfolio analysis and pricing of capital assets. Therefore, one of the major issues that investors face in the capital markets is predicting the fall of stocks. Given this necessity, the purpose of this study is to provide an approach to estimate the risk of stock price crashes. Recently, methods called "artificial neural networks" have been used to predict monetary and financial variables in parallel with structural models and time series. These models, which are actually derived from the brain learning process, use computer computational speed to learn complex relationships between variables and use them to predict the future. Using the data of 20 companies listed in the Tehran Stock Exchange, the present study presents models to estimate the probability of stock prices crash in the Iranian stock market using artificial neural networks. The results indicate that artificial neural networks have good performance in estimating the probability of stock prices crash in the Iranian stock market.
Zahra Afshari, Hossein Tavakolian, Marziyeh Bayat,
Volume 18, Issue 2 (7-2018)
Abstract
This article attempts to examine the impact of stock market fluctuations on macroeconomic variables by designing a New Keynesian approach in a dynamic stochastic general equilibrium (DSGE) model. For this purpose, first, model parameters are estimated based on Bayesian approach and using of quarterly data from 1994 to 2014. Second, the impulse response functions of variables to innovations in stock price index, monetary shock, technology shock, consumer spending and public investment are investigated. Then, the optimal weights related to inflation gap, output gap and the stock price index gap within the monetary policy function are extracted. According to the results, a shock to stock price index has a negligible effect on inflation and output variables. This may be due to the small size of the stock market in Iran. Finally, the optimal coefficients are determined for inflation and output gaps, stock price index gap, and the central bank deadweight loss under various scenarios. Based on findings, first, the central bank should attribute more weight to inflation in itself reaction functions. Second, a scenario in which the weight of stock price index is zero has less deadweight loss, thus the response of the central bank to stock price index gap leads to a reduction in social welfare. Therefore, when the stock market is booming, the central bank is recommended not to be intervened to reduce liquidity.
