مطالب مرتبط با کلیدواژه

Genetic Algorithm


۱.

Using Genetic Algorithm in Solving Stochastic Programming for Multi-Objective Portfolio Selection in Tehran Stock Exchange(مقاله علمی وزارت علوم)

نویسنده:
منبع: Advances in Mathematical Finance and Application, Volume ۲, Issue ۴, Autumn ۲۰۱۷ 107 - 120

کلیدواژه‌ها: Portfolio optimization Multi criteria decision making Stochastic Programming Chance constrained compromise Genetic Algorithm

حوزه های تخصصی:
تعداد بازدید : ۵۹۴ تعداد دانلود : ۳۴۰
Investor decision making has always been affected by two factors: risk and returns. Considering risk, the investor expects an acceptable return on the investment decision horizon. Accordingly, defining goals and constraints for each investor can have unique prioritization. This paper develops several approaches to multi criteria portfolio optimization. The maximization of stock returns, the power of liquidity of selected stocks and the acceptance of risk to market risk are set as objectives of the problem. In order to solve the problem of information in the Tehran Stock Exchange in 2017, 45 sample stocks have been identified and, with the assumption of normalization of goals, a genetic algorithm has been used. The results show that the selected model provides a good performance for selecting the optimal portfolio for investors with specific goals and constraints. 
۲.

Portfolio Optimization by Means of Meta Heuristic Algorithms(مقاله علمی وزارت علوم)

نویسنده:
منبع: Advances in Mathematical Finance and Application, Volume ۴, Issue ۴, Autumn ۲۰۱۹ 83 - 97

کلیدواژه‌ها: Artificial Bee Colony Portfolio optimization Genetic Algorithm Ant Colony Algorithm

حوزه های تخصصی:
تعداد بازدید : ۵۹۳ تعداد دانلود : ۵۳۷
Investment decision making is one of the key issues in financial management. Selecting the appropriate tools and techniques that can make optimal portfolio is one of the main objectives of the investment world. This study tries to optimize the decision making in stock selection or the optimization of the portfolio by means of the artificial colony of honey bee algorithm. To determine the effectiveness of the algorithm, its sharp criteria was calculated and compared with the portfolio made up of genes and ant colony algorithms. The sample consisted of active firms listed on the Tehran Stock Exchange from 2005 to 2015. The sample selected by the systematic removal method. The findings show that artificial bee colony algorithm functions better than the genetic and ant colony algorithms in terms of portfolio formation
۳.

The Predictability Power of Neural Network and Genetic Algorithm from Fiems’ Financial crisis(مقاله علمی وزارت علوم)

نویسنده:
منبع: Advances in Mathematical Finance and Application, Volume ۵, Issue ۲, Spring ۲۰۲۰ 1 - 17

کلیدواژه‌ها: Artificial Neural Network Genetic Algorithm financial crisis

حوزه های تخصصی:
تعداد بازدید : ۶۷۲ تعداد دانلود : ۳۲۵
Organizations expose to financial risk that can lead to bankruptcy and loss of business is increased nowadays. This may leads to discontinuity in operations, increased legal fees, administrative costs and other indirect costs. Accordingly, the purpose of this study was to predict the financial crisis of Tehran Stock Exchange using neural network and genetic algorithm. This research is descriptive and practical and in order to collect data Stock Exchange database software has been used. For data analysis, we used artificial neural network in base form and artificial neural network mix with genetic algorithm. In addition for methods comparison, determination coefficient, Mean squared error and Root-mean square error have been used. The result of study shows that the best artificial neural network is a network with a hidden layer and eight neurons in the layer. This network could predict 97.7 percent of healthy and bankrupt companies correctly for test data. Furthermore the best mixed neural network with genetic algorithm is a network with 400 replications and population size 50, one layer and eight neurons which could correctly predict 100% of healthy and bankrupt companies. Finally, comparison of results of two methods shows that the best method for predicting financial crisis is mixed neural network with genetic algorithm.
۴.

Investigating the Effects of New Corporate Liquidity and Market Operational Performance Indicators on the Markowitz Model Portfolio Returns Using Genetic Algorithm: A Case Study on Refineries and Petrochemical Companies Listed on Tehran Stock Exchange(مقاله علمی وزارت علوم)

نویسنده:
منبع: Petroleum Business Review (PBR), Volume ۳, Issue ۱ - Serial Number ۶, Winter ۲۰۱۹ 1 - 15

کلیدواژه‌ها: Liquidity Indicators Operational Efficiency Genetic Algorithm Markowitz Model Optimum Portfolio

حوزه های تخصصی:
تعداد بازدید : ۲۶۶ تعداد دانلود : ۱۸۶
The research on the Markowitz model and optimization of its portfolio using a variety of evaluation indicators and meta-beta-algorithms has always been the focus of attention of accounting and finance researchers. The results of studies carried out by various types of optimization methods are different in the Markowitz modified models. The purpose of this study is to measure the optimal portfolio and its corresponding return, with respect to the portfolio in the traditional Markowitz model, as well as to compare the position of the refining and petrochemical companies versus stock market outperformers, through integrating the operational criteria and the new indicators of liquidity using the genetic algorithm in the Markowitz model. Therefore, financial data related to the research variables for 35 cases of TSE-listed refinery and petrochemical companies from 2012 to 2016 fiscal years were extracted from Rahavard Novin database software and simulated by the genetic algorithm. The results show that returns on the stock portfolio optimized using the genetic algorithm and without considering the liquidity limitations and filters have a significant and positive difference with the return on the stock portfolios optimized with regard to the liquidity limitations and filters. Furthermore, the application of liquidity limitations and filters in the formation of optimal stock portfolios leads to a conservative increase in the choice of stocks (portfolio formation), which leads to a reduction in the risk and return of investment in such portfolios.
۵.

An Algorithmic Trading system Based on Machine Learning in Tehran Stock Exchange(مقاله علمی وزارت علوم)

نویسنده:
منبع: Advances in Mathematical Finance and Application, Volume ۶, Issue ۳, Summer ۲۰۲۱ 653 - 669

کلیدواژه‌ها: Stock Trading System Machine Learning Genetic Algorithm Neural Network Fuzzy Logic

حوزه های تخصصی:
تعداد بازدید : ۴۳۱ تعداد دانلود : ۲۶۶
Successful trades in financial markets have to be conducted close to the key recurrent points. Researchers have recently developed diverse systems to help the identification of these points. Technical analysis is one of the most valid and all-purpose kinds of these systems. With its numerous rules, the technical analysis endeavors to create well-timed and correct signals so that these points are identified. However, one of the drawbacks of this system is its overdependence on human analysis and knowledge in selecting and applying these rules. Employing the three tools of genetic algorithm, fuzzy logic, and neural network, this study attempts to develop an intelligent trading system based on the recognized rules of the technical analysis. Indeed, the genetic algorithm will assist with the optimization of technical rules owing to computing complexities. The fuzzy inference will also help the recognition of the total current condition in the market. It is because a set of rules will be selected based on the market kind (trending or non-trending). Finally, the signal developed by every rule will be translated into a single result (buy, sell, or hold). The obtained results reveal that there is a statistically meaningful difference between a stock's buy and hold and the trading system proposed by this research. In other words, our proposed system displays an extremely higher profitability potential.
۶.

Hybrid Algorithm for Efficient node and Path in Opportunistic IoT Network(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Information Technology Management , Volume ۱۳, Special Issue: Big Data Analytics and Management in Internet of Things, ۲۰۲۱ 68 - 91

کلیدواژه‌ها: Boundary Fitness function Fuzzy Logic Genetic Algorithm IoT Multi-copy routing

حوزه های تخصصی:
تعداد بازدید : ۳۱۷ تعداد دانلود : ۱۶۵
Opportunistic networks in the Internet of Things (IoT) scenario, also known as OppIoT, espouse IoT devices interactions opportunistically in order to improve connectivity, the lifetime of the network, and network reliability. An increase in opportunistic utilization is fostered by IoT applications to find communication opportunities whenever possible to route and deliver data efficiently. In this opportunistic scenario, devising an efficient path for data delivery is a challenging work due to uncertainty in the connection between the nodes and the selection of intermediate forwarder nodes for data delivery towards the destination. Considering the scenario of uncertainty in device location and exploiting IoT devices opportunistically, this paper propounds a routing algorithm for OppIoT called Hybrid Multi-Copy Routing Algorithm (HMCRA). The proposed algorithm finds potential forwarder nodes by using fuzzy logic wherein residual energy, distance, and speed of the nodes are considered as input values while preparing fuzzy rules. Genetic Algorithm (GA) is considered along with fuzzy logic to select an efficient path for data delivery. In GA, the delay is taken as the fitness function to select a reliable path for data delivery. Simulation results of the proposed algorithm perform well in contrast with relative existing routing algorithms with respect to latency, overhead ratio, delivery probability, and hop count. The work uniqueness lies in the selection of potential nodes and finding path having less hop count in an opportunistic IoT network scenario.
۷.

A Hybrid Artificial Intelligence Approach to Portfolio Management(مقاله علمی وزارت علوم)

نویسنده:
منبع: Iranian Journal of Finance, Volume ۶, Issue ۱, Winter ۲۰۲۲ 1 - 27

کلیدواژه‌ها: Portfolio optimization Artificial Intelligence Algorithmic Trading trading systems Genetic Algorithm Technical Analysis Neural Network

حوزه های تخصصی:
تعداد بازدید : ۳۶۵ تعداد دانلود : ۱۸۶
The tremendous advances in artificial intelligence over the past decade have led to their increasing use in financial markets. In recent years a large number of investment companies and hedge funds have been implementing algorithmic and automated trading on their trading. The speed of decision-making and execution is the most important factor in the success of institutional and individual investors in capital markets. Algorithmic trading using machine learning methods has been able to improve the performance of investors by finding investment opportunities as well as time entry and exit of trading. The purpose of this study is to achieve a better portfolio performance by designing an intelligent and fully automated trading system that investors with the support of this system, in addition to finding the best opportunities in the market, can allocate resources optimally. The present study consists of four separate steps. Respectively, tuning the parameters of technical indicators, detecting the current market regime (trending or non-trending), issuing a definite signal (buy, sell or hold) from the indicators’ signals and finally portfolio rebalancing. These 4 steps respectively are performed using genetic algorithm, fuzzy logic, artificial neural network and conventional portfolio optimization model. The results show the complete superiority of the proposed model in achieving higher returns and less risk compared to the performance of the TEDPIX and other mutual funds in the same period.
۸.

Forecasting the Tehran Stock market by Machine Learning Methods using a New Loss Function(مقاله علمی وزارت علوم)

نویسنده:
منبع: Advances in Mathematical Finance and Application, Volume ۶, Issue ۲, Spring ۲۰۲۱ 194-205

کلیدواژه‌ها: Support vector machine Artificial Neural Network Genetic Algorithm

حوزه های تخصصی:
تعداد بازدید : ۴۳۳ تعداد دانلود : ۱۲۷
Stock market forecasting has attracted so many researchers and investors that many studies have been done in this field. These studies have led to the development of many predictive methods, the most widely used of which are machine learning-based methods. In machine learning-based methods, loss function has a key role in determining the model weights. In this study a new loss function is introduced, that has some special features, making the investing in the stock market more accurate and profitable than other popular techniques. To assess its accuracy, a two-stage experiment has been designed using data of Tehran Stock market. In the first part of the experiment, we select the most accurate algorithm among some of the well-known machine learning algorithms based on artificial neural network, ANN, support vector machine, SVM. In the second stage of the experiment, the various popular loss functions are compared with the proposed one. As a result, we introduce a new neural network using a new loss function, which is trained based on genetic algorithm. This network has been shown to be more accurate than other well-known and common networks such as long short-term memory (LSTM) for both train and test data.
۹.

Presenting a Model for Financial Reporting Fraud Detection using Genetic Algorithm(مقاله علمی وزارت علوم)

نویسنده:
منبع: Advances in Mathematical Finance and Application, Volume ۶, Issue ۲, Spring ۲۰۲۱ 377-392

کلیدواژه‌ها: financial reporting fraud fraud detection Genetic Algorithm Data mining

حوزه های تخصصی:
تعداد بازدید : ۴۰۶ تعداد دانلود : ۱۳۷
both academic and auditing firms have been searching for ways to detect corporate fraud. The main objective of this study was to present a model to detect financial reporting fraud by companies listed on Tehran Stock Exchange (TSE) using genetic algorithm. For this purpose, consistent with theoretical foundations, 21 variables were selected to predict fraud in financial reporting that finally, using statistical tests, 9 variables including SALE/EMP, RECT/SALE, LT/CEQ, INVT/SALE, SALE/TA, NI/CEQ, NI/SALE, LT/XINT, and AT/LT were selected as the potential financial reporting fraud indexes. Then, using genetic algorithm, the final model of fraud detection in financial reporting was presented. The statistical population of this study included 66 companies including 33 fraudulent and 33 non-fraudulent companies from 2011 to 2016. The results showed that the presented model with the accuracy of 91.5% can detect fraudulent companies. These findings extend financial statement fraud research and can be used by practitioners and regulators to improve fraud risk models.
۱۰.

A Genetic algorithm for Objective formulation effect on the shortfall of retirees in developing countries: a case study in Iran(مقاله علمی وزارت علوم)

نویسنده:
منبع: Iranian Journal of Finance, Volume ۷, Issue ۱, Winter ۲۰۲۳ 85 - 104

کلیدواژه‌ها: Optimization Retirement Simulation Bootstrapping Simulation Method Genetic Algorithm

حوزه های تخصصی:
تعداد بازدید : ۵۴۶ تعداد دانلود : ۴۰۸
The certainty about retirement income is dependent on the longevity and the selected investment policy by individuals during their working years. Attention to longevity and investment risks is of high significance in making enough income for retirees. In this research, the impact of the objective formulation selection on investment decisions has been investigated. Two functions including terminal wealth objective formulation and retirement income objective formulation are applied to investigate these decisions. Based on the investment alternatives, 5 asset classes including Equity, Certificate of deposit (cash), real estate investment trust (REIT), gold coin, and foreign exchange have been selected for investment. According to the complexity of modeling in the aforementioned functions, the Metaheuristic Genetic algorithm has been used. The results are indicative of the importance of objective formulation selection. The retirement income objective function has the nature of more wealth accumulation and more control over economic and market turbulences through higher cooperation in investment and as a result, it has been recommended as the optimal function.
۱۱.

Improved NARX-ANFIS Network structure with Genetic Algorithm to optimizing Cash Flow of ATM Model(مقاله علمی وزارت علوم)

نویسنده:
منبع: Advances in Mathematical Finance and Application, Volume ۸, Issue ۱, Winter ۲۰۲۳ 241 - 254

کلیدواژه‌ها: Prediction model autoregressive with exogenous input network Genetic Algorithm ATM device

حوزه های تخصصی:
تعداد بازدید : ۱۸۵ تعداد دانلود : ۱۵۱
Nowadays, the rapid growth of data in organizations has caused managers to look for a way to analyze them. Extracting useful knowledge from aggregation data can lead to appropriate strategic decision-making for the organization. This paper suggests an application of hybrid network based on amount month demand in every ATM device based on transaction mean of 9 months for 1377 devices to obtain customer behavior patterns, to do so, first designed a basic model based on an auto-regressive with exogenous input network (NARX) then, the optimization of the weight and bias of the designed network is made by the genetic algorithm (GA). As a result, finding the weights of the network represents a nonlinear optimization problem that is solved by the genetic algorithm. Paper results show that the NARX-ANFIS Hybrid network using GA for the learning of rules and to optimize the network weights and weights of the network and the fixed threshold can improve the accuracy of the prediction model. Also, classic models are more efficient and increased benefits and lower financing costs and more rational inventory cash control. As well, the designed model can lead to increase benefits and decrease costs in the bank so that, exact forecast and optimal cash upload in ATMs will lead to increase funds on the bank and rise customers and popularity the brand of the bank.
۱۲.

Modelling Optimal Predicting Future Cash Flows Using New Data Mining Methods (A Combination of Artificial Intelligence Algorithms)(مقاله علمی وزارت علوم)

نویسنده:
منبع: Advances in Mathematical Finance and Application, Volume ۸, Issue ۳, Summer ۲۰۲۳ 1007 - 1022

کلیدواژه‌ها: Future Cash Flows Neural Network Model Genetic Algorithm Particle swarm Algorithm

حوزه های تخصصی:
تعداد بازدید : ۱۵۲ تعداد دانلود : ۱۲۷
The purpose of this study was to present an optimal model Predicting Future Cash Flows optimized neural network with genetic (ANN+GA) and particle swarm algorithms (ANN+PSO). In this study, due to the nonlinear relationship among accounting information, we have tried to predict future cash flows by combining artificial intelligence algorithms. Variables of accruals components and operating cash flows were employed to investigate this prediction; therefore, the data of 137 companies listed in Tehran Stock Exchange during (2009-2017) were analysed. The results of this study showed that both neural network models optimized by genetic and particle swarm algorithms with all variables presented in this study (with 15 predictor variables) are able to provide an optimal model Predicting Future Cash Flows. The results of fitting models also showed that neural network optimized with particle swarm algorithm (ANN+PSO) has lower error coefficient (better efficiency and higher prediction accuracy) than neural network optimized with ge-netic algorithms (ANN+GA).
۱۳.

Comparison of Genetic and Hill Climbing Algorithms to Improve an Artificial Neural Networks Model for Water Consumption Prediction(مقاله علمی وزارت علوم)

نویسنده:
منبع: مدیریت شهری دوره ۱۴ بهار ۱۳۹۵ ضمیمه لاتین شماره ۴۲ ۱۳۰-۱۱۹

کلیدواژه‌ها: Water Consumption Prediction Genetic Algorithm Hill Climbing Algorithm Artificial Neural Network Multi-Layer Perceptron Correlation Coefficient

حوزه های تخصصی:
تعداد بازدید : ۴۱ تعداد دانلود : ۳۹
No unique method has been so far specified for determining the number of neurons in hidden layers of Multi-Layer Perceptron (MLP) neural networks used for prediction. The present research is intended to optimize the number of neurons using two meta-heuristic procedures namely genetic and hill climbing algorithms. The data used in the present research for prediction are consumption data of water subscribers in Fasa City of Fars Province (Iran) between the years 2010 to 2013. Ultimately, using the respective data set, the data of the subsequent year 2014 can be predicted. In the present research it was observed that the mean square errors of per data (MSEPD) for the abovementioned algorithms are less than 0.2, indicating a high performance in the neural networks’ prediction. Correlation coefficients using genetic and hill climbing algorithms were respectively equal to 0.891 and 0.759. Thus, GA was able to leave a better effect on optimization of neural network.
۱۴.

The Impact of Content Produced on Instagram Social Network on Successful Economic Services of Isfahan in Corona Crisis Using a Combination of Genetic Algorithm and Forbidden Search Algorithm(مقاله علمی وزارت علوم)

نویسنده:
منبع: International Journal of Digital Content Management, Vol. ۵, No. ۹, Summer & Fall ۲۰۲۴ 236 - 258

کلیدواژه‌ها: Brand and Advertising Dimensions Media Dimensions Instagram Prohibited Search Algorithm Genetic Algorithm

حوزه های تخصصی:
تعداد بازدید : ۳۹ تعداد دانلود : ۳۵
Purpose: The purpose of this research was to provide a model for choosing the best content for the activity of service guilds.Method: In inferential statistics, the K-S test is used for the normality of research hypotheses. For this purpose, Pearson's correlation coefficient and linear regression tests have been used through SPSS 21 software, and the best content generated using genetic algorithms and forbidden search were introduced.Findings: Analysis of research and implementation results with two collective intelligence algorithms shows that Instagram has a positive and significant effect on all four dimensions and thus leads to the success of the service classes that have used Instagram.Conclusion: In this article, a combination model of genetic algorithm and forbidden search algorithm was chosen for users so that the best content, which of course does not contain malicious ads and cookies, etc., is introduced for the continuation of the service industry.