فهرست مطالب milad shahvaroughi farahani

In any economy, it is essential to monitor the rate of population change closely. Governments employ various strategies and programs to regulate population growth since different population growth rates have distinct economic consequences. This paper reveals a global trend of reduced desire to have children, with variations across countries. The paper aims to predict the population growth rate in England by employing Artificial Neural Networks (ANN) in combination with various meta-heuristic algorithms, including the Sparrow Search Algorithm (SSA). The selection of SSA and other algorithms is based on factors such as accuracy and computational efficiency. A set of 18 economic indicators serves as input variables, and a Genetic Algorithm (GA) is used for feature selection. The data used for analysis spans the most recent ten years and is presented on a monthly basis. The results indicate that SSA exhibits the lowest prediction errors for the population growth rate among the applied algorithms in this paper. The primary contribution of this study lies in the application of hybrid algorithms that combine SSA-ANN with other algorithms, such as LA. The paper also emphasizes the inclusion of influential and impactful indices as input variables to enhance prediction accuracy.

Investigating stock price trends and determining future stock prices have become focal points for researchers within the finance sector. However, predicting stock price trends is a complex task due to the multitude of influencing factors. Consequently, there has been a growing interest in developing more precise and heuristic models and methods for stock price prediction in recent years. This study aims to assess the effectiveness of technical indicators for stock price prediction, including closing price, lowest price, highest price, and the exponential moving average method. To thoroughly analyze the relationship between these technical indicators and stock prices over predefined time intervals, we employ an Artificial Neural Network (ANN). This ANN is optimized using a combination of Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Harmony Search (HS) algorithms as meta-heuristic techniques for enhancing stock price prediction. The GA is employed for selecting the most suitable optimization indicators. In addition to indicator selection, PSO and HS are utilized to fine-tune the Neural Network (NN), minimizing network errors and optimizing weights and the number of hidden layers simultaneously. We employ eight estimation criteria for error assessment to evaluate the proposed model's performance and select the best model based on error criteria. An innovative aspect of this research involves testing market efficiency and identifying the most significant companies in Iran as the statistical population. The experimental results clearly indicate that a hybrid ANN-HS algorithm outperforms other algorithms regarding stock price prediction accuracy. Finally, we conduct run tests, a non-parametric test, to evaluate the Efficient Market Hypothesis (EMH) in its weak form.

The stock market involves risks and returns that, if forecasted correctly, can lead to profitability, and for this forecasting, appropriate methods are needed. It is affected by various parameters and needs a way to identify these parameters well and have a dynamic nature. The main goal of this article is forecasting Tehran Price Index (TEPIX) by using hybrid Artificial Neural Network (ANN) based on Genetic Algorithm (GA), Harmony Search (HS) particle Swarm Optimization algorithm (PSO) Moth Flame Optimization (MFO) and Whale Optimization algorithms. GA is used as feature selection. So, PSO, HS MFO and WOA are used to determine the number of input and hidden layers. We use the daily values of the stock price index of the Tehran Stock Exchange from 2013 to 2018 in order to forecasting price and test it. The accuracy of ANN, hybrid Artificial Neural Network with HS, PSO MFO and WOA is evaluated based on different loss functions such as MSE, MAE and etc. the results show that the predictability of Meta-heuristic algorithms in testing period is higher than normal ANN. Also, the predictability of hybrid WOA is higher than hybrid PSO and HS algorithms and MFO.

One of the main concepts in every economy are startups because they have some characteristics and qualifications such as innovation, job creation, boosting economic productivity and etc. that differentiate them from other companies. So, it is important to better identify them and make familiar with their revenue generations and valuations. In this paper, we have tried to study the main role of startups in economy, their characteristics, main goals and etc. The main goal of article is prediction of startup's return using artificial intelligence methods such as genetic algorithm (GA) and artificial neural network (ANN). There are multiple startup valuation models such as Berkus model, DCF model, venture capital method and etc. Since, there is not any information about startups such as sale, market size, profit and etc. and most of the models works with database, so, we have tried to analyze startups that are in stock markets and passed IPO stage. Some global indices such as S&P500, DJAI, and economic indicators such as 10 years Treasury yield, Wilshire 5000 Total Market Full Cap Index along with some other special indicators in startups like team, idea, timing and etc. are used as input variables. GA is used as feature selection and finding the most important variables. ANN is used as an optimization model and prediction of startup's returns. We used econometric models such as regression analysis. We have estimated Value at risk (VaR) and Conditional Value at risk (C-VAR) for considered portfolios including three startups (public company) such as Dropbox, Inc. (DBX), Scout24 SE (G24.DE) and TIE.AS and optimal portfolio formation. The results show that AI based methods are more powerful in prediction of startup's return. On the other hand, VaR and C-VaR models are very beneficial approach in minimizing risk and maximizing return. We found that artificial intelligence based models having high predictability and qualifications such as speed up calculations, improve by training, no assumption, ease of use and etc. But econometric models have some qualifications and assumptions such as normality, linearity, stationarity and etc. which are the limitation.

One of the most important issues in recent years has been the issue of population aging and its effects on the economy. It is clear that aging leads to increased healthcare costs, decreased productivity, saving, investment, risk taking and etc. finally, the economic growth will slow. On the other hand, it is necessary to address the issues of sustainable development, namely inequality, life expectancy, green life and etc. The main goal of this article is survey the impacts of aging on sustainable development and G20 economies and their plans for reducing and controlling the negative consequences. The main contribution of this article is that we have integrated the issue of sustainable development and aging problem in G20 countries in terms of economic and finance. The results show that the rate of fertility is decreasing and the rate of aging is increasing. So, G20 programs need to be considered and acted upon. We can conclude that by investing and effective measures and identifying potential threats, the effects of reduced economic growth and productivity can be reduced.

One of the most important events of the current century has been the spread of the COVID19 and its devastating effects on the world economy. Because this disease is a pandemic, as a result, it has challenged all economies in some ways and it has had consequences such as unemployment, poverty, negative economic growth, and so on. So, this is a kind of task that we should survey and investigate this destructive impacts and its results on economy and try to overcome. On the other hand, events have occurred simultaneously in the world that can affect the economies of countries such as Brexit. Economic considerations are one of the questions that will weigh on MPs’ minds when they come to scrutinize and vote on the Government’s EU withdrawal agreement. This article summarizes what is known about the long-term economic impact of Brexit and answer questions that may arise in the minds of readers. This article consists of two main parts: the first part is about investigate the effect of COVID19 on UK economy and new COVID. The second part is about investigate the consequences of BREXIT on UK economy. The main contribution of the article is that we have surveyed the effect of COVID19 and Brexit on the UK economy in details in different dimensions such as economic means GDP, jobs, services, industries and etc. and social life and their lifestyle changes means business activities, education, transportation and etc. The main goal of this article is investigation the consequences of COVID19 and Brexit on UK economy simultaneously. The research is done by exploring of the latest economic analysis related to the COVID-19 pandemic in order to identify its effects on UK economy throughout 2020 and Brexit too. The results show that improving economic conditions is a long-term matter and Brexit will definitely have an impact on the future of the UK and some other countries.

One of the most parameters and variables in every economics is the interest rate. Government officials and lawmakers change interest rates for various purposes controlling liquidity, inflation, and prices, Economic growth and development, lending, etc. So, it is important to set the interest rate correctly. If you can predict the interest rate correctly, you can earn and gain profit by investing in various sectors. Moreover, the interest rate can impact other sectors through parallel markets such as the stock market, automobile, housing, etc. Interest rates are related to parallel markets. Thus, if you can forecast the interest rate, you can predict the parallel markets too. The main goal of this article, as it is clear from the title, is the prediction of interest rate using ANN and improving the network using some novel heuristic algorithms such as Moth Flame Optimization algorithm (MFO), Chimp Optimization Algorithm (CHOA), Time-varying Correlation Particle Swarm Optimization algorithm (TVAC-PSO), etc. we used 17 variables such as oil price, gold coin price, house price, etc. as input variables. We used GA and a new algorithm called Grey Wolf Optimization, Particle Swarm Optimization (GWO-PSO) algorithm as a feature selection and choosing the best variables. We have used eight loss functions such as MSE, RMSE, MAE, etc. too. Finally, we have compared different algorithms due to their estimation errors. The main contribution of this paper is that, first, this is for the first time which these novel metaheuristic algorithms have been used for the prediction of interest rate. Second, it has tried to use different graphs and tables for better understanding and totally a comprehensive research paper. The results show that Whale Optimization Algorithm (WOA) performed better than other methods along with less error.