scholarly journals Mean-Variance portfolio optimization by using non constant mean and volatility based on the negative exponential utility function

2017 ◽  
Author(s):  
Endang Soeryana ◽  
Nurfadhlina Bt Abdul Halim ◽  
Sukono ◽  
Endang Rusyaman ◽  
Sudradjat Supian
Keyword(s):  
Utility Function ◽  
Portfolio Optimization ◽  
Exponential Utility ◽  
Negative Exponential ◽  
Mean Variance Portfolio ◽  
Mean Variance ◽  
Exponential Utility Function

scholarly journals An Improvement of Stochastic Gradient Descent Approach for Mean-Variance Portfolio Optimization Problem

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Stephanie S. W. Su ◽  
Sie Long Kek
Keyword(s):  
Portfolio Optimization ◽  
Standard Error ◽  
Gradient Descent ◽  
Optimization Problem ◽  
Stochastic Gradient Descent ◽  
Moment Estimation ◽  
The Mean ◽  
Portfolio Optimization Problem ◽  
Mean Variance Portfolio ◽  
Mean Variance

In this paper, the current variant technique of the stochastic gradient descent (SGD) approach, namely, the adaptive moment estimation (Adam) approach, is improved by adding the standard error in the updating rule. The aim is to fasten the convergence rate of the Adam algorithm. This improvement is termed as Adam with standard error (AdamSE) algorithm. On the other hand, the mean-variance portfolio optimization model is formulated from the historical data of the rate of return of the S&P 500 stock, 10-year Treasury bond, and money market. The application of SGD, Adam, adaptive moment estimation with maximum (AdaMax), Nesterov-accelerated adaptive moment estimation (Nadam), AMSGrad, and AdamSE algorithms to solve the mean-variance portfolio optimization problem is further investigated. During the calculation procedure, the iterative solution converges to the optimal portfolio solution. It is noticed that the AdamSE algorithm has the smallest iteration number. The results show that the rate of convergence of the Adam algorithm is significantly enhanced by using the AdamSE algorithm. In conclusion, the efficiency of the improved Adam algorithm using the standard error has been expressed. Furthermore, the applicability of SGD, Adam, AdaMax, Nadam, AMSGrad, and AdamSE algorithms in solving the mean-variance portfolio optimization problem is validated.

Information in Fleeting Opportunities

2013 ◽  
pp. 110-153
Author(s):  
Ibrahim Almojel ◽  
Jim Matheson ◽  
Pelin Canbolat
Keyword(s):  
Utility Function ◽  
Decision Maker ◽  
Optimal Policy ◽  
Value Of Information ◽  
Risk Preference ◽  
Dynamic Program ◽  
Exponential Utility ◽  
Venture Capitalists ◽  
Exponential Utility Function ◽  
Over Time

This paper focuses on the study of information in fleeting opportunities. An application example is the evaluation of business proposals by venture capitalists. The authors formulate the generic problem as a dynamic program where the decision maker can either accept a given deal directly, reject it directly, or seek further information on its potential and then decide whether to accept it or not. Results show well behaved characteristics of the optimal policy, deal flow value, and the value of information over time and capacity. It is presumed that the risk preference of the decision maker follows a linear or an exponential utility function. This approach is illustrated through several examples.

scholarly journals Mean-Variance Optimization Is a Good Choice, But for Other Reasons than You Might Think

Risks ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 29 ◽  
Author(s):  
Andrea Rigamonti
Keyword(s):  
Portfolio Optimization ◽  
Asset Allocation ◽  
Risk Measures ◽  
Traditional Approach ◽  
Real Data ◽  
Downside Risk ◽  
Good Choice ◽  
Mean Variance Portfolio ◽  
Mean Variance ◽  
Allocation Decisions

Mean-variance portfolio optimization is more popular than optimization procedures that employ downside risk measures such as the semivariance, despite the latter being more in line with the preferences of a rational investor. We describe strengths and weaknesses of semivariance and how to minimize it for asset allocation decisions. We then apply this approach to a variety of simulated and real data and show that the traditional approach based on the variance generally outperforms it. The results hold even if the CVaR is used, because all downside risk measures are difficult to estimate. The popularity of variance as a measure of risk appears therefore to be rationally justified.

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