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An artificial intelligence approach for investment decision making / Walaa Moshref Osman Mohamed ; Supervised Hegazy Zaher , Naglaa Ragaa Saeid Hassan

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Walaa Moshref Osman Mohamed , 2019Description: 108 Leaves : charts , facsimiles ; 30cmOther title:
  • اسلوب ذكاء اصطناعي لاتخاذ قرارات استثماريه [Added title page title]
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  • Issued also as CD
Dissertation note: Thesis (M.Sc.) - Cairo University - Institute of Statistical Studies and Research - Department of Operations Research Summary: In this thesis, a portfolio selection with an asset allocation management is applied on investment in fuzzy environment. Fuzzy Logic Control (FLC) is applied to real life problem to solve Client asset allocation problem (CAA) as Banking Advisory model with fuzzy returns.Asset allocation is well known to be one of the most influential determinants of portfolio risk and return. Key factors that help investors to determine their preferred asset allocation appropriate to the investor{u2019}s risk tolerance, time horizon and financial goals. The proposed client asset allocation model is based on Mamdani Fuzzy Inference System (MFIS). The proposed model can be used in case a client at a bank asked a help : how to invest portions of his investment in 3 asset classes saving account, investment certificate and investment fund. The suggested optimization model is based on maximizing the expected returns appropriate to client{u2019}s risk tolerance and time horizon. The proposed Client asset allocation model introduced through 2 studies to choose the optimal type and number of membership functions (MF). The first study investigates the effect of changing the number of membership function (MF) on the percentage of expected returns. The second study contains comparing different types of MF in all the variables as triangular and trapezoidal MF. The comparative study investigates the optimal number of rules. The proposed MFIS model succeeded in helping clients to allocate portions of their investment in the three asset classes through giving the highest expected return percentage
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Item type Current library Home library Call number Copy number Status Date due Barcode
Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.18.05.M.Sc.2019.Wa.A (Browse shelf(Opens below)) Not for loan 01010110079114000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.18.05.M.Sc.2019.Wa.A (Browse shelf(Opens below)) 79114.CD Not for loan 01020110079114000

Thesis (M.Sc.) - Cairo University - Institute of Statistical Studies and Research - Department of Operations Research

In this thesis, a portfolio selection with an asset allocation management is applied on investment in fuzzy environment. Fuzzy Logic Control (FLC) is applied to real life problem to solve Client asset allocation problem (CAA) as Banking Advisory model with fuzzy returns.Asset allocation is well known to be one of the most influential determinants of portfolio risk and return. Key factors that help investors to determine their preferred asset allocation appropriate to the investor{u2019}s risk tolerance, time horizon and financial goals. The proposed client asset allocation model is based on Mamdani Fuzzy Inference System (MFIS). The proposed model can be used in case a client at a bank asked a help : how to invest portions of his investment in 3 asset classes saving account, investment certificate and investment fund. The suggested optimization model is based on maximizing the expected returns appropriate to client{u2019}s risk tolerance and time horizon. The proposed Client asset allocation model introduced through 2 studies to choose the optimal type and number of membership functions (MF). The first study investigates the effect of changing the number of membership function (MF) on the percentage of expected returns. The second study contains comparing different types of MF in all the variables as triangular and trapezoidal MF. The comparative study investigates the optimal number of rules. The proposed MFIS model succeeded in helping clients to allocate portions of their investment in the three asset classes through giving the highest expected return percentage

Issued also as CD

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