Hamidreza Haddadian; Morteza Baky Haskuee; Gholamreza Zomorodian
Abstract
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 ...
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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.
Farzaneh Abdollahian; Mohammad Ebrahim Mohammad Pourzarandi; Mehrzad Minouei; Seyed Mohammad Hasheminejad
Abstract
The stock exchange is considered to be an important establishment to finance long term projects, on one hand, and to collect savings and finance of private section. The stock exchange can be a safe and secure place to invest surplus funds to purchase corporate stocks. As recession and prosperity in this ...
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The stock exchange is considered to be an important establishment to finance long term projects, on one hand, and to collect savings and finance of private section. The stock exchange can be a safe and secure place to invest surplus funds to purchase corporate stocks. As recession and prosperity in this market can have a great role in stockholders` decision-making, it becomes vital to predict these cycles. In this paper, using model MSMH(4)AR(2), we extract the financial cycles of the market. Then, using the ant colony algorithm, we determine the most significant predictors and predict the market financial cycles using neural networks. The results show that the PNN model performs better in predicting the future market with respect to the criteria of mean squared error, the root mean squared error, the model accuracy and kappa coefficient.