This paper considers robust mean-variance portfolio selection problems including uncertainty sets and fuzzy factors. Since these problems are not well-defined problems due to fuzzy factors, it is hard to solve them directly. Therefore, introducing chance constraints, fuzzy goals and possibility measures, the proposed models are transformed into the deterministic equivalent problems. Furthermore...

This research studies a single-period expected utility-based optimal portfolio problem. Assets are divided into different groups. It is assumed that the actual dependency structure of the asset returns within each group is unknown, but assets belonging to different groups have independent returns. Instead of assuming any particular dependency structure within each group, we propose the maximin ...

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...

Portfolio selection addresses the problem of how to diversify investments in the most efficient and profitable way possible. Portfolio selection is a field of study that has been broached from several perspectives, including, among others, recommender systems. This paper presents SINVLIO (Semantic INVestment portfoLIO), a tool based on semantic technologies and fuzzy logic techniques that recom...

The old adage about putting all of one’s eggs in one basket was thought wise long before the advent of modern portfolio theory and the important results of Markowitz (1959) and others. However, despite the anecdotal imperative to investors to diversify their portfolios, the empirical reality is that diversification rarely takes place as recommended by portfolio theory. In this paper, I demonstr...

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...