درخت حوزه‌های تخصصی

Asset allocation has always been a challenging issue / for individuals and businesses to survive in our competitive world. One of the famous businesses, which has an enormous impact on people's lives worldwide, is the pension industry. Pension funds- as Defined Benefit, Defined Contribution, or others- accept reserves from contributors and try to invest them in a way to keep up with their obligations in the future or even pay more than that. The equity market has been one of the good choices for investment as pension funds try to reach a particular rate of return to maximize their wealth while considering not crossing red lines in taking risks. This paper will detail the new mathematical model for finding optimal stock portfolios using Generalized Co-Lower Partial Moment as a risk measure to minimize portfolio optimization. On the other hand, it introduces new tailored Expected Utility as a performance metric to maximize in this model. The proposed model's issue against previous studies is considering risk aversion and target rate of investment return as two significant investor characteristics. This is based on price returns' simulation of candidate stocks in TSE while using accurate and nonparametric Probability Density Function in historical data analysis.

Good governance is a system of values, policies, and institutions by which governments manage society. Stability and political stability in countries require the absence of management gaps and the achievement of governance improvement and lack of fragility, which weakened and fragile governments do not have the necessary conditions for such conditions. Usually, developing countries are exposed to the gap in natural resources and brain drain, which provide the basis for important effects on governance and fragility. In this study, to investigate the effect of dependence on natural resources and brain drain on the fragility of states and good governance, the statistical data of 26 developing countries that export fuel from 2007 to 2019 were used and with the approach of SUR Model estimation has been done. The results show that the effect of natural resource rent on the fragility of states is positive and statistically significant. Also, the effect of natural resource rent on good governance is negative and significant. In other words, the effect of natural resource rent on the fragility of states and good governance has an impact coefficient of 0.03 and -0.02, respectively. Therefore, the rent of natural resources can cause governments to weaken and become more fragile in good governance indicators. Also, the effect of brain drain on the fragility of states is positive and significant, and it is negative and significant on good governance. The effect coefficient of the brain drain variable on the fragility of states and good governance is 2.05 and -0.12, respectively.

Risk and uncertainty are inherent characteristics of economic activities. This study aimed to investigate how fluctuations in macroeconomic variables affected the economic growth of Iran, in the period 1989-2021. To this end, fluctuations in macroeconomic variables were measured using EGARCH method, and then Iran’s economic growth equation was estimated based on the Markov Regime-Switching GARCH. The MRS(2)-AR(2) model was estimated. The model estimation results revealed two regimes of prosperity and recession in Iran's economy, the latter was more durable than the former in the studied period. The different effects of OPEC oil price fluctuations in the two regimes, indicating the asymmetric effects of this variable on Iran's economic growth. In other words, the effects of this uncertainty on Iran’s economic growth are smaller during a period of recession than a period of prosperity. The results on real exchange rate fluctuations showed that fluctuations in this variable had similar destructive effects on Iran’s economic growth. The results revealed the positive and negative effects of inflation rate fluctuations on Iran’s economic growth during periods of recession and prosperity, respectively. The IQI coefficient showed the positive effects of this variable on Iran’s economic growth; higher institutional quality increases physical capital and, thereby, economic growth. Based on the model estimation results, it is impossible to propose a single solution to control fluctuations in macroeconomic variables in Iran during different periods. economic policymakers should adopt and implement the best policies considering the periods of recession or prosperity as well as the development priorities.

The ability to predict the stock market and analyze market trends is invaluable to researchers and anyone interested in investing. However, this task is a challenging problem due to a large number of parameters and unpredictable noise that may affect the stock price. To overcome this issue, researchers have employed numerous approaches such as Moving Average (MA), Support Vector Machine (SVM), and Neural Networks. With technological advances, deep learning methods have become popular in processing time-series data. In this paper, we compare two recently introduced deep learning models, namely a Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), in forecasting daily movements of the Standard & Poor (S&P 500) index using the daily closing price of this index from 14/5/1991 to 14/5/2021. Results show that both models are effective and accurate in stock market prediction. In this case study, the mean squared error (MSE) and mean absolute error (MAE) for the GRU model are slightly lower than the LSTM model; hence, GRU outperformed the LSTM model despite its simpler structure. The results of this study are applicable in various instances where it is challenging to identify patterns among large volumes of unstructured data, such as medical data analysis, text mining, and financial time series modeling.

Although theoretical and empirical literature regarding the stylized facts shows evidence of their correlations to herding behavior in financial markets, the causes of such phenomena are still unknown. Using an agent-based model strengthened by the competition co-evolution algorithm (STGP) technique, this study provides laboratory evidence on capital market dynamics and analyses the behavioral foundations of stylized facts such as fat tails, leverage effects, and volatility clustering. The simulated stock markets consist of two groups; the “Best agents”, which are a small portion of artificial agents, and the “Residual agents”, which are the main group of artificial agents. The best performance in terms of breeding fitness returns is the main feature of the “Best agents”. More, the size of the “Best Agents” group is specified as 2.5%, 5%, 10% &20% of the total population size. An agent-based model consists of two portions, a two thousand population of trader agents that each has its decision-making strategy, and a virtual market that creates the trading strategies. Then the model evolved step by step using a feed with real quotes of the financial instruments by Adaptive Modeler. A training period is considered 2500 bars (started in November 2003), and the test period started in December 2013. The observation shows that the herding behavior in the price series created by the “Residual agents” is less than the “Best agents” series. Therefore, the greater diversity of trade strategies as the genetic differences of artificial agents leads to less herding. The observations exhibit that the volatility clustering, leverage effects, and nonlinear dependence are more likely to experience in the price series generated by “Best gents”. Furthermore, observations indicate that if the population is well diversified in terms of trading strategies, the efficiency of the market increases.