Mathematical programming methods dominate in the portfolio optimization problems, but they cannot be used if we introduce a constraint limiting the number of different assets included in the portfolio. To solve this model some of the heuristics methods (such as genetic algorithm, neural networks and particle swarm optimization algorithm) must be used. In this paper we utilize binary particle sw...

This paper applies growth optimization with downside protection as a portfolio selection technique. The model is based on power-log utility functions that combine portfolio growth maximization with the behavioural tenets of prospect theory. We use three assets (a farm return index, a stock market index, and a Treasury bond index) to illustrate how effective this technique is compared to the sta...

Mean-Variance (MV) theory for portfolio selection is based on assumptions involving parameters that have to be estimated using historical data. Depending on the method of estimation, the estimates will suffer from estimation error and/or specification error, both of which will effect the portfolio optimization in such a way that the resulting optimal portfolio is not the true optimal portfolio....

In this paper we implement an algorithm for the optimal selection of a portfolio of stock and risk-free asset under the stochastic volatility (SV) model with discrete observation and trading. The SV model extends the classical Black-Scholes model by allowing the noise intensity (volatility) to be random. The main assumption is that the portfolio manager has discrete access to the continuous-tim...

Data Mining is being actively applied to stock market since 1980s. It has been used to predict stock prices, stock indexes, for portfolio management, trend detection and for developing recommender systems. The various algorithms which have been used for the same include ANN, SVM, ARIMA, GARCH etc. Different hybrid models have been developed by combining these algorithms with other algorithms li...

Multiobjective portfolio optimization problem is the portfolio process of the highest expected return among the various financial commodities of the capital market to meet the expected return objectives. And one of the most important and common management issues lies in determining the best portfolio out of a given set of investment proposals. As we know, modern portfolio theory provides a well...

– Diversification of an investment into independently fluctuating assets reduces its risk. In reality, movements of assets are mutually correlated and therefore knowledge of cross-correlations among asset price movements are of great importance. Our results support the possibility that the problem of finding an investment in stocks which exposes invested funds to a minimum level of risk is anal...

the markowitz issue of optimization can’t be solved by precise mathematical methods such as second order schematization, when real world condition and limitations are considered. on the other hand, most managers prefer to manage a small portfolio of available assets in place of a huge portfolio. it can be analogized to cardinal constrains, that is, constrains related to minimum and maximum curr...

Traditional techniques in portfolio management rely on the precise knowledge of the underlying probability distributions; in practice, however, such information is difficult to obtain because multiple factors affect stock prices on a daily basis and unexpected events might affect the price dynamics. To address this issue, we propose an approach to dynamic portfolio management based on the seque...