Portfolio selection is a resource allocation problem in a finance market. The investor’s asset optimization requires the distribution of a set of capital (resources) among a set of entities (assets) with the trade-off between risk and return. The ANN with nonlinear capability is proven to solve a large-scale complex problem effectively. It is suitable to solve NP-hard resource allocation proble...

Abstract As it is well known there are few “starting points” in portfolio optimization process, i.e. in the stock selection process. Famous Markowitz’ optimization model is unavoidable in this job. On the other side, someone may say that the indicators of the fundamental analysis must be the starting point. Beside that, the suggestions of the technical analysis must be taken into consideration....

in the portfolio selection problem, the manager considers several objectives simultaneously such as the rate of return, the liquidity and the risk of portfolios. these objectives are conflicting and incommensurable. moreover, the objectives can be imprecise. generally, the portfolio manager seeks the best combination of the stocks that meets his investment objectives. the imprecise goal program...

In the portfolio selection problem, the manager considers several objectives simultaneously such as the rate of return, the liquidity and the risk of portfolios. These objectives are conflicting and incommensurable. Moreover, the objectives can be imprecise. Generally, the portfolio manager seeks the best combination of the stocks that meets his investment objectives. The imprecise Goal Program...

It is well known that the wealthier the household, the larger tends to be the proportion of its total capital portfolio allocated to publicly traded stock, and the larger tends to be the number of individual stock issues included in its portfolio. Using the “homogeneous securities” case of a mean-variance model originally proposed by Michael Brennan, explicit functional forms are obtained for b...

This paper describes the DEA-based investment strategy for constructing of a stock portfolio in the Croatian stock market. The relative efficiency of the DMUs, which are in this case the selected stocks from Zagreb Stock Exchange, is obtained from the output oriented CCR and BCC models. The set of inputs consists of risk measures, namely return variance, Value at Risk (VaR) and beta coefficient...

We develop and implement a portfolio optimization method for building investment portfolios that dominate a given benchmark index in terms of third-degree stochastic dominance. Our approach relies on the properties of the semi-variance function, a re nement of an existing `superconvex' dominance condition and quadratic constrained programming. To reduce the computational burden in large-scale a...

Algorithms based on the process of natural evolution are widely used to solve multi-objective optimization problems. In this paper we propose the agent-based co-evolutionary algorithm for multi-objective portfolio optimization. The proposed technique is compared experimentally to the genetic algorithm, co-evolutionary algorithm and a more classical approach—the trend-following algorithm. During...

The purpose of this study was to investigate the role of non-financial information analysis and risk-return analysis along with financial information in increasing the selected banks and financial institutions of Tehran Stock Exchange portfolio efficiency. To evaluate the efficiency of the portfolio, the Treynor's ratio was used and attempted to determine the Treynor's ratio of the selected opt...

Playing the stock market is one of the many frontiers of applied artificial intelligence. The problem of optimizing asset allocation within a portfolio to yield a return above the market is a hard problem. Current approaches include multiagent systems in which the task of gathering data, predicting trend and choosing assets are divided between specialized cooperative agents that simulate the en...