The problem of portfolio optimization which deals with the twin objectives of minimizing risk and maximizing expected portfolio return can turn complex when constraints that model investor preferences and market norms such as bounding, cardinality and class constraints, and short sales are included in it. A complex-constrained portfolio optimization such as this has been beyond the reach of sol...

In single-period portfolio optimization several facets of the problem may influence the goodness of the portfolios selected. Despite that, some of these facets are frequently ignored when the optimization problem is solved. In this thesis, we aim at investigating the impact of these facets on the optimization problem and on the performances of the portfolios selected. Firstly, we consider the p...

Multistage portfolio optimization models are difficult to solve when market risk is measured by Value-at-Risk (VaR), this paper proposes a soft method for solving VaR-based portfolio optimization models based on a soft optimization approach. In order to demonstrate the validity of the proposed soft method, we perform portfolio management experiments with real data from the New York stock market...

this research analyze the resource and structure of cross-autocorrelation in returns and volatility of stocks that listed in the tehran stock exchange during the period farvardin 1382–eisfand 1386, with employing the garch model. at the first, the results show that, in down(bear) market, return on high trading volume portfolio lead return on low trading volume portfolio, when controlled for fir...

This paper addresses about an approach that suggests for stock portfolio optimization using the combination of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Capital Asset Pricing Model (CAPM). Stock portfolio optimization aims to determine which of the stocks to be added to a portfolio based on the investor’s needs, changing economic and market conditions. In order to construct an efficient...

Data mining is being actively applied to stock market since 1980s. The various aspects of stock market to which data mining has been applied include predicting stock indices, predicting stock prices, portfolio management, portfolio risk management, trend detection, designing recommender systems etc. The various algorithms and methods which have been used for the same include neural networks, as...