meta-heuristic algorithms

The most important goal of every investor in the stock market is to increase returns and reduce investment risk. Therefore, the purpose of this research is to analyze the effectiveness of meta-heuristic algorithms in stock portfolio optimization. Considering that in this research, the past performance of Tehran Stock Exchange companies is examined in past studies from 1390-1399, therefore, in terms of the research design, this research was post-event using Delphi and meta-analysis techniques. The statistical community of this research Academic researchers in the field of finance and active in the Tehran Stock Exchange, and the sampling method in this research was targeted with a volume of 30 people. The data collection tool was a researcher-made questionnaire. The method of collecting information was structured interview of researchers and review of the results of various studies in the field of determining the optimal stock portfolio in Tehran Stock Exchange. In order to analyze the data, Spss software version 23 and Laserl version 5.7 were used. The results showed that among meta-heuristic algorithms of genetic algorithm, ant colony and bee colony are the most suitable tools with the aim of not stopping at local optimal points and not premature convergence. Finally, after evaluating the appropriate algorithms, a comparison of the average risk and returns of the stock portfolio in genetic algorithms, ant colony and bee colony was done in the study unit, they showed that in terms of the criteria of reducing the risk of genetic and bee algorithms and in terms of increasing the return of the optimal portfolio Stock bee algorithm has worked more efficiently.

The main goal of investors in the stock market is to get the highest return at the desired time, therefore introducing the most suitable method for conducting transactions is of special importance for investors. Successful trading in financial markets should be done close to key reversal points. In recent years, various systems have been developed to identify these return points. Technical analysis tries to identify the time to enter and exit trades.In this article, we are trying to select the one with a higher success rate by using the technical rules according to the previous researches, and by using soft calculations, the decision parameters in the technical rules are improved using the firefly algorithm.The results of this model are compared with the results of using the standard parameters of the indicators and the results of the purchase and maintenance strategy. In order to validate the introduced trading system, we compared it with the results of the optimized intelligent system based on optics and genetic algorithm. The results of the research show that by optimizing the parameters of technical analysis indicators, the investment efficiency can be increased compared to the usual methods in the stock market and previous researches.

Accurately predicting of earning management to detect and identify manipulation of financial statements has always been one of the most fundamental Challenges ahead of financial reports users. Applying the Beneish model can be one of the most appropriate models for modeling in the field of earning management prediction. Beneish model 1999 has been developed for this purpose by emphasis on the variables of corporate governance system including audit committee structure, legal inspector and independent auditor, board of director's structure and corporate ownership structure requirements. The data of 81 companies listed on TSE during 2012-2018 has been analyzed by the hybrid method of multi-layer perceptron MLP neural network and metahuristic algorithms of biogeography based optimization BBO, Imperialist Competitive Algorithm ICA and water cycle algorithm WCA. The accuracy of the model by hybirid mothodes of MLP-BBO, MLP-ICA and MLP-WCA has been increased from 59.08 ، 59.96 and 59.79 percentages to 92.06 ، 89.24 and 79.72 percentage respectively. The results indicate the accuracy improvment of the proposed model in detecting earning-manipulator companies and also the higher efficiency of the hybrid method of MLP-WCA compared to the other methods in optimization of multilayer perceptron neural network.

The classical efficient market hypothesis states that it is not possible to beat the market by developing a strategy based on historical price series. In this paper we propose a profitable automatic trading system based on the divergence definition in relative strength index and using other technical analysis tools which presents empirical evidence confronting the classical efficient market hypothesis. In order to validate the developed solution an extensive evaluation was performed, comparing the designed strategy against the market itself and several other investment methodologies. An intraday database comprised of 59 symbols from NYSE in The time span 2010 to 2016 was employed. The whole sample is categorized over two sub-periods, training and widening its validity. By enjoying Meta-heuristic algorithms the rules in the first sub-period was improved. Then, in the second division the improved model was evaluated. The results indicates that this model improved predictability power and its performance is better than buy and hold and random strategies