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.
Fereydon Rahnamay Roodposhti; Gholamreza Zomorodian; Hosaine Hasangholipoure
Abstract
Iran allocated 6% of the world's natural disaster fatality, while the country only has one percent of the world's population. More than 40 natural disasters have been recognized globally. According to the evaluations carried out in Iran, at least 31 natural disasters have occurred locally. Domestic insurance ...
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Iran allocated 6% of the world's natural disaster fatality, while the country only has one percent of the world's population. More than 40 natural disasters have been recognized globally. According to the evaluations carried out in Iran, at least 31 natural disasters have occurred locally. Domestic insurance capacity does not cover this volume of risk and the need for external reinsurance capacity is always felt. Catastrophe bonds structure as a financial innovative solution allows an issuing institution to transfer catastrophic exposures and risk to capital market’s investors by creating capital relief and additional risk capacity for Iran’s insurance industry. We adjusted formal cat bonds structure with the Islamic jurisprudence, as well as domestic regulations consideration. According to the research and interviews conducted and using thematic analysis research method, we finally suggested that the catastrophe bond instrument in Iran could be issued in the form of "insurance Sukuk" contract template, as a solution for transferring catastrophic risks to the Iranian capital market. Since this proposed structure have many aspects, the comprehensive implementation of it depends on cooperation between money market, capital market and insurance industry.