پديد آورندگان :
ﺧﺎدم ﭘﻮر آراﻧﯽ، ﻋﺒﺎس داﻧﺸﮕﺎه آزاد اﺳﻼﻣﯽ واﺣﺪ ﺗﻬﺮان ﻣﺮﮐﺰي - ﮔﺮوه ﻣﺪﯾﺮﯾﺖ ﻣﺎﻟﯽ، ﺗﻬﺮان، اﯾﺮان , ﮐﯿﻘﺒﺎدي، اﻣﯿﺮرﺿﺎ داﻧﺸﮕﺎه آزاد اﺳﻼﻣﯽ واﺣﺪ ﺗﻬﺮان ﻣﺮﮐﺰي - ﮔﺮوه ﺣﺴﺎﺑﺪاري، ﺗﻬﺮان، اﯾﺮان , ﻣﻌﺪﻧﭽﯽ زاج، ﻣﻬﺪي داﻧﺸﮕﺎه آزاد اﺳﻼﻣﯽ واﺣﺪ اﻟﮑﺘﺮوﻧﯿﮑﯽ - گروه ﻣﺪﯾﺮﯾﺖ ﻣﺎﻟﯽ، ﺗﻬﺮان، اﯾﺮان , زﻣﺮدﯾﺎن، ﻏﻼﻣﺮﺿﺎ داﻧﺸﮕﺎه آزاد اﺳﻼﻣﯽ واﺣﺪ ﺗﻬﺮان ﻣﺮﮐﺰي - ﮔﺮوه ﻣﺪﯾﺮﯾﺖ ﻣﺎﻟﯽ، ﺗﻬﺮان، اﯾﺮان
كليدواژه :
اﻗﺘﺼﺎدﺳﻨﺠﯽ , ﭘﺮﺗﻔﻮي ﺑﻬﯿﻨﻪ , ﮔﺎرچ ﭼﻨﺪ ﻣﺘﻐﯿﺮه , رﯾﺴﮏ ﻧﻘﺪﺷﻮﻧﺪﮔﯽ , اﻟﮕﻮرﯾﺘﻢ ژﻧﺘﯿﮏ
چكيده فارسي :
ﺑﺮاي رﺷﺪ و ﺗﻮﺳﻌﻪ ﮐﺸﻮرﻫﺎ، ﺑﻨﮕﺎه ﻫﺎ و ﺣﺘﯽ اﻓﺮاد ﺟﺎﻣﻌﻪ، ﺳﺮﻣﺎﯾﻪﮔﺬاري از ﺟﺎﻧﺐ آﻧﻬﺎ اﻣﺮي ﺿﺮوري و ﺣﯿﺎﺗﯽ اﺳﺖ و ﺑﺮاي ﺑﻬﺮهﮔﯿﺮي و اﺛﺮﺑﺨﺸﯽ ﺑﯿﺸﺘﺮ، اﯾﻦ ﺳﺮﻣﺎﯾﻪﮔﺬاريﻫﺎ ﻣﯿﺒﺎﯾﺴﺖ ﺑﻬﯿﻨﻪ ﺑﺎﺷﺪ. از زﻣﺎن ﻣﻌﺮﻓﯽ ﺗﺌﻮري ﻣﺎرﮐﻮﯾﺘﺰ و ﺣﺘﯽ ﻗﺒﻞ از آن، ﻣﻔﻬﻮم ﺳﺮﻣﺎﯾﻪﮔﺬاري ﺑﻬﯿﻨﻪ ﺑﻪ ﻋﻨﻮان ﻣﺼﺎﻟﺤﻪاي ﺑﯿﻦ رﯾﺴﮏ و ﺑﺎزده، ﻣﻮرد ﺗﻮﺟﻪ ﻗﺮار ﮔﺮﻓﺘﻪ ﺑﻮد. در ﻃﻮل ﭼﻨﺪﯾﻦ دﻫﻪ ﺑﻌﺪ از آن، ﺗﻌﺎﺑﯿﺮ و اﺑﻌﺎد ﺟﺪﯾﺪي از ﻣﻌﯿﺎرﻫﺎي ﺑﻬﯿﻨﻪ و ﺧﺼﻮﺻﺎً رﯾﺴﮏ ﻣﻄﺮح ﺷﺪه اﺳﺖ. در اﯾﻦ ﻣﻘﺎﻟﻪ ﺳﻌﯽ ﺷﺪه اﺳﺖ ﺑﺎ اراﺋﻪ ﻣﺪﻟﯽ از رﯾﺴﮏ ﻧﻘﺪﺷﻮﻧﺪﮔﯽ ﺑﺎ ﺑﻬﺮهﮔﯿﺮي از ﻣﻔﻬﻮم ﻣﺘﻨﻮعﺳﺎزي در ﻗﺎﻟﺐ آﻧﺘﺮوﭘﯽ ﺷﺎﻧﻮن و روﯾﮑﺮد اﻗﺘﺼﺎدﺳﻨﺠﯽ، ﺳﺒﺪ ﺑﻬﯿﻨﻪاي از ﺳﺮﻣﺎﯾﻪ ﮔﺬاري ﺑﺎ ﮐﻤﺘﺮﯾﻦ رﯾﺴﮏ و ﺑﯿﺸﺘﺮﯾﻦ ﺑﺎزده، در ﻗﺎﻟﺐ ﯾﮏ ﭘﺮﺗﻔﻮي از 4 ﮔﺮوه ﺻﻨﻌﺘﯽ ﺑﻮرس ﺗﻬﺮان ﺷﺎﻣﻞ ﮔﺮوﻫﻬﺎي ﻓﻠﺰات اﺳﺎﺳﯽ، ﺑﺎﻧﮑﻬﺎ، ﻓﺮآوردهﻫﺎي ﻧﻔﺘﯽ و ﮐﺎﻧﻪﻫﺎي ﻓﻠﺰي ﮐﻪ ﺑﯿﺸﺘﺮﯾﻦ ارزش ﺑﺎزار ﺑﻮرس اﯾﺮان را در اﺧﺘﯿﺎر دارﻧﺪ، اراﺋﻪ ﺷﻮد. دادهﻫﺎي آﻣﺎري اﯾﻦ ﭘﮋوﻫﺶ ﺑﺮاي ﺻﻨﺎﯾﻊ ﻣﻨﺘﺨﺐ، ﺷﺎﻣﻞ ﺑﺎزده ﺷﺎﺧﺺ ﻗﯿﻤﺘﯽ روزاﻧﻪ و ﺑﺎزده ﺷﮑﺎف ﻗﯿﻤﺘﯽ روزاﻧﻪ در ﻓﺎﺻﻠﻪ ﺳﺎلﻫﺎي 1395 ﺗﺎ ﭘﺎﯾﺎن ﺳﺎل 1399 اﺳﺖ. ﺑﺮاي ﻣﺤﺎﺳﺒﻪ رﯾﺴﮏ ﻧﻘﺪﺷﻮﻧﺪﮔﯽ، ﺑﺎ اﺳﺘﻔﺎده از روﺷﻬﺎي ﮔﺎرچ ﭼﻨﺪ ﻣﺘﻐﯿﺮه ، ﻣﺎﺗﺮﯾﺲ وارﯾﺎﻧﺲ-ﮐﻮوارﯾﺎﻧﺲ ﺑﺎزده ﺷﺎﺧﺺ ﻗﯿﻤﺘﯽ و ﺷﮑﺎف ﻗﯿﻤﺘﯽ، ﻣﺤﺎﺳﺒﻪ و در ﻣﺪل اراﺋﻪ ﺷﺪه، اﺳﺘﻔﺎده ﺷﺪه و ﻧﻬﺎﯾﺘﺎً وزن ﺑﻬﯿﻨﻪ ﺑﺎ اﺳﺘﻔﺎده از ﮐﺪﻧﻮﯾﺴﯽ در ﻧﺮم اﻓﺰار ﻣﺘﻠﺐ و اﺳﺘﻔﺎده از روش ﺑﻬﯿﻨﻪﺳﺎزي اﻟﮕﻮرﯾﺘﻢ ژﻧﺘﯿﮏ رﺗﺒﻪﺑﻨﺪي ﻧﺎﻣﻐﻠﻮب ﻧﺴﺨﻪ دوم ، ﺑﺮاي ﺻﻨﺎﯾﻊ ﻣﻨﺘﺨﺐ ﻣﺤﺎﺳﺒﻪ ﺷﺪه اﺳﺖ. ﻧﺘﺎﯾﺞ ﺧﺮوﺟﯽ ﻣﺪل ﻧﺸﺎن ﻣﯽدﻫﻨﺪ وزن ﺑﻬﯿﻨﻪ ﮔﺮوهﻫﺎﯾﯽ ﮐﻪ وارﯾﺎﻧﺲ ﮐﻤﺘﺮي دارﻧﺪ در ﺳﺒﺪ ﺑﻬﯿﻨﻪ ﺑﯿﺸﺘﺮ اﺳﺖ. ﺿﻤﻦ اﯾﻨﮑﻪ ﺗﺎﺛﯿﺮ ﺣﺬف ﻣﻔﻬﻮم ﻧﻘﺪﺷﻮﻧﺪﮔﯽ از ﻣﺪل ﻣﻨﺠﺮ ﺑﻪ اﻓﺰاﯾﺶ وزن ﺻﻨﺎﯾﻌﯽ ﻣﯽ ﺷﻮد ﮐﻪ ﻧﻘﺪﺷﻮﻧﺪﮔﯽ ﮐﻤﺘﺮي دارﻧﺪ و ﺑﻪ ﻫﻤﺮاه اﻓﺰاﯾﺶ رﯾﺴﮏ، ﺑﺎزده ﭘﺮﺗﻔﻮي ﺑﻬﯿﻨﻪ ﻧﯿﺰ در اﯾﻦ ﺣﺎﻟﺖ اﻓﺰاﯾﺶ ﻣﯽﯾﺎﺑﺪ. ﻫﻤﭽﻨﯿﻦ ﺑﺎ ﺣﺬف ﻣﺤﺪودﯾﺖ ﺷﺎﺧﺺ ﻣﺘﻨﻮعﺳﺎزي ﺷﺎﻧﻮن، ﻧﺘﺎﯾﺞ ﺧﺮوﺟﯽ ﻧﺸﺎن ﻣﯽدﻫﻨﺪ اﯾﻦ ﻣﺤﺪودﯾﺖ ﺗﻘﺮﯾﺒﺎً ﺗﺎﺛﯿﺮي ﺑﺮ اوزان ﺑﻬﯿﻨﻪ ﺣﺪاﻗﻞ در اﯾﻦ ﻣﺪل ﻧﻤﯽﮔﺬارد.
چكيده لاتين :
For the growth and development of countries, companies, and even individuals, investment on their part is necessary and vital, and these investments should be optimal for more benefit and effectiveness. Since the introduction of Markowitz's theory and even before that, the concept of optimal investment as a compromise between risk and return has been considered. During several decades after that, new definitions and dimensions of optimal criteria and especially risk have been proposed.
In this article, an attempt has been made to present a model of liquidity risk using the concept of diversification in the form of Shannon's entropy and an econometric approach, an optimal portfolio of investments with the lowest risk and the highest return, in the form of a portfolio of 4 industrial groups of the Tehran Stock Exchange, including metal groups. Essentially, banks, oil products and metal ores, which have the highest market value of the Iranian stock market, should be provided.
The statistical data of this research for selected industries include daily price index return and daily price gap return between 2015 and the end of 2019. To calculate the liquidity risk, using multivariate GARCH methods, the variance-covariance matrix of price index return and price gap, calculated and used in the presented model, and finally the optimal weight using coding in MATLAB software and using algorithm optimization method The genetics of non-excessive ranking of the second edition has been calculated for selected industries.
The output results of the model show that the optimal weight of the groups with less variance in the optimal portfolio is higher. Besides, the effect of removing the concept of liquidity from the model leads to an increase in the weight of industries that have less liquidity, and along with the increase in risk, the return of the optimal portfolio also increases in this case. Also, by removing the limitation of Shannon's diversification index, the output results show that this limitation has almost no effect on the optimal weights (at least in this model).
