Consider a discrete-time risk model in which the insurer is allowed to invest a proportion of its wealth in a risky stock and keep the rest in a risk-free bond. Assume that the claim amounts within individual periods follow an autoregressive process with heavy-tailed innovations and that the log-returns of the stock follow another autoregressive process, independent of the former one. We derive...

Finding the best way to optimize the portfolio after Markowitz's 1952 article has always been and will continue to be one of the concerns of activists in the investment management industry. Researchers have come up with different solutions to overcome this problem. The introduction of mathematical models and meta-heuristic models is one of the activities that has influenced portfolio optimizati...

Genetic network programming (GNP) as an evolutionary computation method has been used for stock trading recently. Former researches confirm the efficiency of trading rules which are created by GNP. In this paper, GNP has been applied for stock portfolio optimization by generating risk-adjusted trading rules. There are two main novelties in this paper: 1) we use conditional Sharp ratio as a risk...

In this paper a data mining approach for classification of stocks into clusters is presented. After classification, the stocks could be selected from these groups for building a portfolio. It meets the criterion of minimizing the risk by diversification of a portfolio. The clustering approach categorizes stocks on certain investment criteria. We have used stock returns at different times along ...

the portfolio is a perfect combination of stock or assets, which an investor buys them. the objective of the portfolio is to divide the investment risk among several shares. using non-parametric dea and dea-r methods can be of great significance in estimating portfolio. in the present paper, the efficient portfolio is estimated by using non-radial dea and dea-r models. by proposing non-radial m...

the markowitz’s optimization problem is considered as a standard quadratic programming problem that has exact mathematical solutions. considering real world limits and conditions, the portfolio optimization problem is a mixed quadratic and integer programming problem for which efficient algorithms do not exist. therefore, the use of meta-heuristic methods such as neural networks and evolutionar...

Introduction One of the basic problems of applied finance is the optimal selection of stocks by conflicting objective of maximizing future return and minimizing investment risk. The first systematic treatment of this dilemma is the mean variance approach proposed by Markowitz. Markowitz combined the optimization and probability theory to solve the dilemma. In Markowitz’s mean variance model, th...

Aim of this paper is to characterize different risk measures in portfolio construction on seven Central and South-East European stock markets; Slovenia, Croatia, Hungary, Poland, Chez Republic, Romania and Turkey. Selected countries are members of EU, except Croatia and Turkey which have candidate status. Empirical part of this paper consists of three stages; at first descriptive statistics on ...

We study the partial equilibrium portfolio optimization problem for a myopic CRRA investor who can trade options on individual stocks. Applying the parametric portfolio approach of Brandt, Santa-Clara, and Valkanov (forthcoming) to derivatives we show that options characteristics (such as implied volatility and IV smile skew) convey information about the mispricing in the option portfolios. We ...

We present a novel efficient algorithm for portfolio selection which theoretically attains two desirable properties: 1. Worst-case guarantee: the algorithm is universal in the sense that it asymptotically performs almost as well as the best constant rebalanced portfolio determined in hindsight from the realized market prices. Furthermore, it attains the tightest known bounds on the regret, or t...