genetic algorithms

One of the important features of industrialized and developing countries is the presence of money, dynamic market and capital. In other words, if the saving of individuals will be directed by appropriate mechanism to the manufacturing sector it brings efficiency not only to the owners of capital but also it can be considered as the most important funding for launching economic projects of society.
In present study, three stock selection and optimization algorithms including genetic algorithm, particle swarm algorithm, and cultural algorithm has been studied. So, 106 listed companies in Tehran Stock Exchange, since 2007 to 2014 were tested in order to investigate this.
In this study, for plotting the efficient frontier and comprising of the optimal portfolio half of the variance is considered as the main factor of risk. This research investigates the significant difference between the averages of investment output in selected baskets based on three methods. The statistical analysis of the results shows that there is no difference between the three algorithms. However, in order to compare the two algorithms and analysis of superiority of algorithms, these two methods of optimization have been compared from two aspects of objective function, output ratio and risk.
Since the objective function of particle swarm algorithms was less, in other word, it has the least error and gain the best result so in comparing to other algorithms it has been performed better which shows the relative superiority of this algorithms in the selection of the optimal portfolio.

Selection of appropriate time and price in trading stocks has an important role in investment decisions on profit and loss of investors in capital markets. Nonlinear intelligent systems, such as artificial neural networks, fuzzy- neural networks and genetic algorithms, would be used to forecast stock prices motions. In this article,a model of stock prices motions has been designed using Adaptive Neuro- Fuzzy Inference System (ANFIS)integrated with genetic algorithm, in which two different groups of fundamental and technical variables have been employed as model inputs. According to Model outputs,the rate of forecasting errors in both groups of inputs is not significant and these systems are able to forecast daily stock prices. The Mann-Whitney test has been used to measure the accuracy of models and it was found that there is no significant difference between results of prices forecasted in both methods. Both methods are able to forecast next day price with an insignificant error provided that at least one of the inputs in both methods has a linear dependence with price, . Also, results show that these systems do not work properly to forecast prices of high volatility stocks.

Considering stock market history, major concerns in the first phase to enter the capital market is that what the right price for the initial public offering and could they convince investors to buy shares. Besides that, there are also investors concerns about the accuracy of the pricing stocks.
This study uses nonlinear method has resolved this issue.
Study provides a model pricing initial public offering of shares on the Tehran Stock Exchange. The research period between 1382 to 1393.
Research population 145 enterprises entering the Tehran Stock Exchange in this period of time and the sample of study is according to the condition of the Company and continuous investment of funds and access to company data, were reduced to 103 companies. The proposed network is a neural network optimized the genetic algorithm to determine the price of shares of new companies entering the stock exchange.With a choice of 12 variables affecting the price of initial public offerings and one dependent variable (Initial Public Offering price) suitable model to _ pricing than other linear models presented. The results of the fourth measure, RMSE, MAE, R-SQUARE, U-THEIL reflect the correct pricing proposed model, in most cases.

One of the important features of industrialized and developing countries is the presence of money, dynamic market and capital. In other Words, if the saving of individuals will be directed by appropriate mechanism to the manufacturing sector it brings efficiency not only to the owners of capital but also it can be considered as the most important funding for launching economic projects of society.
In present study, three stock selection and optimization algorithms including genetic algorithm, particle swarm algorithm, and cultural algorithm has been studied. So, 106 listed companies in Tehran Stock Exchange, since 2007 to 2014 were tested in order to investigate this.
In this study, for plotting the efficient frontier and comprising of the optimal portfolio half of the variance is considered as the main factor of risk. This research investigates the significant difference between the averages of investment output in selected baskets based on three methods. The statistical analysis of the results shows that there is no difference between the three algorithms. However, in order to compare the two algorithms and analysis of superiority of algorithms, these two methods of optimization have been compared from two aspects of objective function, output ratio and risk.
Since the objective function of genetic algorithms was less, in other word, it has the least error and gain the best result so in comparing to other algorithms it has been performed better which shows the relative superiority of these algorithms in the selection of the optimal portfolio.

Risk-return tradeoff and its analysis in alternative investments as a classic goal of finance have been the main subject of many researches in financial management. The use of technical indicators is a portfolio management tools. This research aims to use these indicators in mining stocks trading rules. The period of investigation is from beginning of 1388 until the end of 1393 and the sample of study is including 216 companies listed in TSE. In the period from 1388 to 1390 by using technical indicators and genetic algorithm with aim for maximize return and minimize risk, we obtain a model for portfolio optimization and in the period from 1391 to 1393 this model was used in portfolio management. In order to evaluate this model, the results were compared with the market index and found that by using technical indicators can outperform the market.

In recent years, earnings management in university research has attracted much attention. The aim of this study is to predict earnings management through discretionary accruals based on adjusted Jones model. In this study, two models of artificial neural networks and genetic algorithms - neural network hybrid model as a successful model to predict earnings management based on adjusted Jones model were used in the Tehran Stock Exchange. The sample used in this study is consisted of 570 firm-year between 2008 to 2013. The results showed that neural networks have a high ability to predict earnings management rather than the adjusted Jones linear model. The findings also suggest that, the genetic algorithm through optimizing artificial neural network weights is able to increase power of artificial neural network to predict earnings management.

Design and implementation of credit rating model in the banking system plays an important role in enhancing the efficiency of resource allocation is to target customers. In this research aims to develop a model for evaluating the credit risk of the bank's corporate clients have been used Support Vector Machine (SVM) and Genetic Algorithms (GA). Therefore, a study has been on the financial variables of 282 companies during the years 2007 to 2010, have received loans from TEJARAT bank. In this research, to optimize the input of support vector machine is used of genetic algorithms. The power of the genetic algorithm to select the optimum points, always provides confidence that the optimal-made for the proposed going to be higher optimum points. In the hybrid model GA-SVM, genetic algorithm optimizes SVM model inputs the data.
Research findings show GA-SVM hybrid model performed better than the SVM model in the identifying good customer accounts and bad customer accounts and credit risk prediction.

Continuous growth and development of the economics is considered as the main objectives which the firms are seeking to achieve. In doing so, the banks play key roles in the economic growth and development. Due to the increasing numbers of the public and private banks in Iran, predicting their efficiency has attracted significant attentions. This study aims at modeling and predicting the efficiency of the public and private banks by using artificial neural networks, Fuzzy neural networks and genetic algorithms. Using data envelopment analysis (DEA) and considering the total assets and total number of branches as the inputs of the model, the banks’ efficiency has been examined during a period from 2007 to 2011. The outputs of the model include the net profit or loss, the balance of granted credits and receivables. As the next step, the multivariate regression approach, artificial neural network, fuzzy neural network and genetic algorithms have been employed to predict the efficiency of the banks. The findings revealed that the fuzzy neural network is the most precise model in comparison with the other models of predicting efficiency. Based on the sensitivity analysis of the inputs by the neural networks, the net profit or loss has been known as the input with the highest impact on the banks’ efficiency.