مطالب مرتبط با کلیدواژه

particle swarm optimization algorithm


۱.

Stock price prediction using the Chaid rule-based algorithm and particle swarm optimization (pso)(مقاله علمی وزارت علوم)

نویسنده:
منبع: Advances in Mathematical Finance and Application, Volume ۵, Issue ۲, Spring ۲۰۲۰ 1 - 19

کلیدواژه‌ها: Stock Price particle swarm optimization algorithm Chaid rule-based algoritm

حوزه های تخصصی:
تعداد بازدید : ۳۸۹ تعداد دانلود : ۳۰۴
Stock prices in each industry are one of the major issues in the stock market. Given the increasing number of shareholders in the stock market and their attention to the price of different stocks in transactions, the prediction of the stock price trend has become significant. Many people use the share price movement process when com-paring different stocks while investing, and also want to predict this trend to see if the trend continues to increase or decrease over time. In this research, stock price prediction for 1170 years -company during 2011-2016 (a six-year period) of listed companies in stock exchange has been studied using the machine learning method (Chaid rule-based algorithm and Particle Swarm Optimization Algorithm). The results of the research show that there is a significant relationship between earnings per share, e / p ratio, company size, inventory turnover ratio, and stock returns with stock prices. Also, particle swarm optimization (pso) algorithm has a good ability to predict stock prices.
۲.

Stock Portfolio Optimization Using a Combined Approach of Relative Robust Risk Parity(مقاله علمی وزارت علوم)

نویسنده:
منبع: Iranian Journal of Finance, Volume ۵, Issue ۴, Autumn ۲۰۲۱ 87 - 106

کلیدواژه‌ها: Risk parity portfolio Relative robustness Sharpe ratio particle swarm optimization algorithm

حوزه های تخصصی:
تعداد بازدید : ۲۵۲ تعداد دانلود : ۱۷۷
Risk parity is perceived as one of the stock portfolio selection models that have received a lot of attention since the US financial crisis in 2008. The philosophy of this model is to allocate the same amount of portfolio risk between the constituent assets. In the present study, the combined portfolio selection model of relative robust risk parity is introduced, which uses the worst-case scenario approach on the covariance matrix parameter appearing in the robust risk model in portfolio robustness. According to historical data, several scenarios are considered for the covariance matrix. The objective function value of the hybrid model for each portfolio (feasible point) is the worst result (with most volatility) among the set of scenarios.  Finally, the model selects a portfolio for which the worst possible result has the least relative volatility. The research portfolio consists of 8 industries from Tehran Stock Exchange in the period 2011 to 2020. This portfolio has a higher Sharpe ratio than conventional models of mean-variance and weight parity, and is more resilient to market declines than the two models and produces less loss. Therefore, risk-averse investors are advised to use this stock portfolio selection model as a cover to face severe market declines.
۳.

Spatial Analysis of Rescue and Relief Bases in Alborz Province in order to Reduce Hazards(مقاله پژوهشی وزارت بهداشت)

نویسنده:
منبع: Journal of Rescue and Relief, Volume ۱۵, Issue ۳, Serial ۵۸ (۲۰۲۳) 191-203
تعداد بازدید : ۱۱۳ تعداد دانلود : ۱۰۷
INTRODUCTION: The occurrence of a huge number of road accidents in Iran makes it necessary to pay more attention than before to the rescue and relief sector, the correct locating of road rescue and relief bases and its development and equipment, especially in Alborz province and topological conditions, geographical diversity and its tourism characteristics. Therefore, in this research, in order to reduce the hazards, the spatial analysis of rescue and relief bases in this province was conducted. METHODS: In this research, in order to optimize the allocation and locating the rescue and relief centers, the intended indicators were extracted from the Red Crescent Society instructions and after preparing the required data, the weight of each index was extracted and optimized in PSO algorithm in MATLAB environment using AHP hierarchical analysis and OWA weighted average. The obtained weights were applied in the corresponding layers and the optimal points were suggested for the development of the rescue and relief network of Alborz province. FINDINGS: Finally, prioritizing the development of rescue and relief centers in the province was suggested after evaluating the accident-prone state of the province in relation to the existing and proposed centers as well as the development plans of the province CONCLUSION: The results showed that the use of PSO algorithm can have acceptable results in the field of optimal locating of rescue and relief centers.