scholarly journals A Personalized Mean-CVaR Portfolio Optimization Model for Individual Investment

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Chunxia Yu ◽  
Yuru Liu
Keyword(s):  
Portfolio Optimization ◽  
Optimization Model ◽  
Evaluation Method ◽  
Garch Model ◽  
Fuzzy Comprehensive Evaluation ◽  
Risk Tolerance ◽  
Financial Assets ◽  
Investment Portfolio ◽  
Portfolio Optimization Model ◽  
Investment Portfolio Optimization

Investment as an important issue in daily life is accompanied by the occurrence of various financial assets, such as stocks, bonds, and mutual funds. However, risk tolerances vary across individuals. Individual investors have to select corresponding personalized investment portfolios to satisfy their own needs. Moreover, it is difficult for ordinary people to select a personalized investment portfolio by themselves, and it is too expensive and inefficient to look for professional consultation. Therefore, the objective of this research is to propose a personalized portfolio recommendation model, which can build the personalized portfolio based on investors’ risk tolerances. In this research, investors’ risk tolerance is determined by the fuzzy comprehensive evaluation method based on investors’ demographic characteristics. The CVaR is used as the risk measurement of financial assets. The dynamics of the distribution of returns are described in the combined Copula-GARCH model, and the future scenarios of returns are generated by the Monte Carlo simulation based on the combined Copula-GARCH model to estimate CVaR. The mean-CVaR portfolio optimization model is used to find out the best personalized portfolio. Finally, experiments are conducted to validate the applicability and feasibility of the personalized investment portfolio optimization model. Results show that the proposed investment portfolio optimization model can recommend personalized investment portfolio according to investor’s risk tolerance.

Constraint Tracking Error for Investment Portfolio Optimization Model and Algorithm of VaR in Additional Transaction Costs

2014 ◽  
Vol 543-547 ◽  
pp. 1811-1816 ◽  
Author(s):  
Dong Zheng ◽  
Xi Kun Liang
Keyword(s):  
Transaction Costs ◽  
Portfolio Optimization ◽  
Optimization Model ◽  
Tracking Error ◽  
Linear Matrix ◽  
Investment Portfolio ◽  
Expected Return ◽  
Portfolio Optimization Model ◽  
The One ◽  
Investment Portfolio Optimization

Based on the uncertainty of covariant matrix and value of expected return in risk assets, constraint tracking error for investment portfolio optimization model of VaR in additional transaction costs is constructed in this paper. The validity is proved by using the method of linear matrix inequality. According to empirical analysis, the results of different investment models are analyzed and compared with the one gotten by the method in this paper. It is concluded that the distribution of weights of the model in this paper is more reasonable and its final return is better than other models. Moreover, it may be closer to the modern financial markets for its transaction cost.

A portfolio optimization model for corporate bonds subject to credit risk

2004 ◽  
Vol 6 (2) ◽  
pp. 31-48 ◽  
Author(s):  
Nagisa Akutsu ◽  
Masaaki Kijima ◽  
Katsuya Komoribayashi
Keyword(s):  
Credit Risk ◽  
Portfolio Optimization ◽  
Optimization Model ◽  
Corporate Bonds ◽  
Portfolio Optimization Model

Portfolio Optimization Model with Transaction Costs

2002 ◽  
Vol 18 (2) ◽  
pp. 231-248 ◽  
Author(s):  
Shu-ping Chen ◽  
Chong Li ◽  
Sheng-hong Li ◽  
Xiong-wei Wu
Keyword(s):  
Transaction Costs ◽  
Portfolio Optimization ◽  
Optimization Model ◽  
Portfolio Optimization Model

A mean-absolute deviation-skewness portfolio optimization model

1993 ◽  
Vol 45 (1) ◽  
pp. 205-220 ◽  
Author(s):  
Hiroshi Konno ◽  
Hiroshi Shirakawa ◽  
Hiroaki Yamazaki
Keyword(s):  
Portfolio Optimization ◽  
Optimization Model ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Portfolio Optimization Model

scholarly journals Whale Optimization Algorithm for Multiconstraint Second-Order Stochastic Dominance Portfolio Optimization

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Q. H. Zhai ◽  
T. Ye ◽  
M. X. Huang ◽  
S. L. Feng ◽  
H. Li
Keyword(s):  
Portfolio Optimization ◽  
Optimization Algorithm ◽  
Stochastic Dominance ◽  
Optimization Model ◽  
Second Order ◽  
Whale Optimization Algorithm ◽  
Order Stochastic Dominance ◽  
Whale Optimization ◽  
Portfolio Optimization Model ◽  
Second Order Stochastic Dominance

In the field of asset allocation, how to balance the returns of an investment portfolio and its fluctuations is the core issue. Capital asset pricing model, arbitrage pricing theory, and Fama–French three-factor model were used to quantify the price of individual stocks and portfolios. Based on the second-order stochastic dominance rule, the higher moments of return series, the Shannon entropy, and some other actual investment constraints, we construct a multiconstraint portfolio optimization model, aiming at comprehensively weighting the returns and risk of portfolios rather than blindly maximizing its returns. Furthermore, the whale optimization algorithm based on FTSE100 index data is used to optimize the above multiconstraint portfolio optimization model, which significantly improves the rate of return of the simple diversified buy-and-hold strategy or the FTSE100 index. Furthermore, extensive experiments validate the superiority of the whale optimization algorithm over the other four swarm intelligence optimization algorithms (gray wolf optimizer, fruit fly optimization algorithm, particle swarm optimization, and firefly algorithm) through various indicators of the results, especially under harsh constraints.

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