Markowitz model is the first modern formulation of portfolio optimization problem. Relyingon historical return of stocks as basic information and using variance as a risk measure aretow drawbacks of this model. Since Markowitz model has been presented, many effortshave been done to remove theses drawbacks. On one hand several better risk measures havebeen introduced and proper models have been ...

Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Nature-inspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few applications of nature-inspired metaheuristics to portfolio optimization problem...

Dr. V. J.Sebastian (Corresponding author) Institute of Management Technology Gaziabad, Delhi, India E-mail: [email protected] Abstract Portfolio optimization, in case of finance, is the tradeoff between risk and return to maximize profit or return from the portfolio. Financial regulations are country specific and it depends upon the economic conditions prevailing in the country. The portfolio of...

This study first reviews fuzzy random Portfolio selection theory and describes the concept of portfolio optimization model as a useful instrument for helping finance practitioners and researchers. Second, this paper specifically aims at applying possibility-based models for transforming the fuzzy random variables to the linear programming. The harmony search algorithm approaches to resolve the ...

The portfolio optimization problem has become a standard financial engineering problem since the pioneering work of Markowitz on Modern Portfolio Theory. It aims to find an optimal allocation of capital among a set of assets by simultaneously minimizing the risk and maximizing the return of the investment. In the theoretical case of linear constraints, this problem is basically solved by quadra...

‎Linear semi-infinite programming problem is an important class of optimization problems which deals with infinite constraints‎. ‎In this paper‎, ‎to solve this problem‎, ‎we combine a discretization method and a neural network method‎. ‎By a simple discretization of the infinite constraints,we convert the linear semi-infinite programming problem into linear programming problem‎. ‎Then‎, ‎we use...

Portfolio optimization involves the optimal assignment of limited capital to different available financial assets to achieve a reasonable trade-off between profit and risk. We consider an alternative Markowitz’s mean-variance model in which the variance is replaced with an industry standard risk measure, Value-atRisk (VaR), in order to better assess market risk exposure associated with financia...

In this paper, we examine the problem of finding an optimal portfolio of securities by using the probability function of portfolio risk as a constraint. We obtain the value of the risk coefficient for which the problem of maximizing the expectation of the portfolio return with a probabilistic risk function constraint is equivalent to the maximizing the linear convolution of the criteria ”expect...

Kelly's Criterion is well known among gamblers and investors as a method for maximizing the returns one would expect to observe over long periods of betting or investing. These ideas are conspicuously absent from portfolio optimization problems in the financial and automation literature. This paper will show how Kelly's Criterion can be incorporated into standard portfolio optimization models. ...

Value-at-Risk (VaR) is one of the most widely accepted risk measures in the financial and insurance industries, yet efficient optimization of VaR remains a very difficult problem. We propose a computationally tractable approximation method for minimizing the VaR of a portfolio based on robust optimization techniques. The method results in the optimization of a modified VaR measure, Asymmetry-Ro...