Ezatollah Abbasian; Mohammad Ali Kamali
Abstract
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 ...
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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.
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.