Investing in financial market require the reliable predicting of expecting returns, assessment of risk and reliability. Principle of portfolio orthogonality was using to reduce the risk of the investment. An artificial intelligence system may reveal new opportunities for using this principle. Prediction of recurrent neural networks ensemble is stochastically informative distribution, which is h...

Portfolio Selection: How to Integrate Complex Constraints For the standard Mean-Variance model for portfolio selection with linear constraints, there are several algorithms that can efficiently compute both a single point on the Pareto front and even the whole front. Unfortunately, commonly used constraints (e.g. cardinality constraints or buy-in thresholds) result in the optimization problem t...

This paper presents portfolio selection problems with ambiguous returns assumed as “return is about ξ” which is neither estimated by randomness nor fuzziness. Portfolio selection problems in uncertain environment are formulated as nonlinear programming models based on uncertain programming approaches. Since there is no efficient solution method to solve these problems directly, original problem...

One central and important requirement for IS project portfolio selection is the adequate consideration of project interactions. However, the IS discipline notably lacks a common understanding of the nature of project interactions and their impact on IS project portfolio selection. To remedy this we conduct a systematic and interdisciplinary literature review thereby providing a starting point f...

Over sixty years ago, Markowitz introduced the mean-variance efficient frontier to finance. While mean-variance is still the predominant model in portfolio selection, it has endured many criticisms. One serious one is that it does not allow for additional criteria. The difficulty is that the efficient frontier becomes a surface. With it now possible to compute such a surface, we provide an over...

The popularity of data centers in scientific computing has led to new architectures, new workload structures, and growing customerbases. As a consequence, the selection of efficient scheduling algorithms for the data center is an increasingly costlier and more difficult challenge. To address this challenge, and contrasting previous work on scheduling for scientific workloads, we focus in this w...

This paper uses the concept of possibilistic risk aversion to propose a new approach for portfolio selection in fuzzy environment. Using possibility theory, the possibilistic mean, variance, standard deviation and risk premium of a fuzzy number are established. Possibilistic Sharpe ratio is defined as the ratio of possibilistic risk premium and possibilistic standard deviation of a portfolio. T...

The literature on portfolio analysis assumes that the securities returns are random variables with fixed expected returns and variances values (see Bachelier [1], Briec et al. [4] and Markowitz [10]). However, since investors receive efficient or inefficient information from the real world, ambiguous factors usually exist in it. Consequently, we need to consider not only random conditions but a...