Penka Georgieva

A Multi-Step Procedure for Asset Allocation in Case of Limited Resources

Abstract Portfolio management is a process involving decision making in dynamic and unpredictable environment. Asset allocation plays a key role in this process, since the optimal use of the capital is a complex and resource-consuming problem. During our research in this field we have detected some problems that lead to biased results and one of them occurs in case of limited financial resources. In this paper a mathematical assessment of the dependence of the capital, used on asset prices is derived, and a multi-step procedure for asset allocation, aiming at optimization of the investor’s utility in case of limited resources is described. The procedure is implemented as a module in a decision support system based on fuzzy logic. The paper contains comparison of the obtained test results with results from the classical Markowitz portfolio model. The conducted tests are on real data from the Bulgarian stock exchange.

Cardinality Problem in Portfolio Selection

There is a variety of models for portfolio selection. However, in portfolio theory applications little or no attention is paid to the cardinality problem. In this paper, an algorithm for dealing with this problem is presented. The proposed allocation algorithm is implemented in a software system, which is based on the Fuzzy Logic Q-measure Model and manages financial investments in real time. Tests on real data from Bulgarian Stock Exchange are presented as illustration to the solution.

A Fuzzy Approach to Solving Multicriteria Investment Problems

Financial mangers have to make decisions under many restrictions and often have to deal with vague and imprecise information. In multicriteria problems, the best solution is sought in a set X of alternatives under certain constraints with rates of contentment μ t : X → [0,1]. Considering these rates as membership functions, the tools that fuzzy logic provides is adequate to solving a multicriteria investment problem. In this paper, a fuzzy approach for assessing the quality of an asset and making an investment decision based on this assessment is proposed.

A genetic fuzzy system for asset allocation

The decisions of financial investors are often made in real-time under many constrains in an environment containing vague and imprecise information. The great amount of financial data brings additional problems in the decision making process. Currently, there are various and diverse software systems to support this process, some based on fundamental analysis and others - on technical analysis. Fuzzy rule-based systems provide the tools to overcome the above difficulties to some extent, due to the fact that dynamic models of complex and non-deterministic systems with unstable and uncertain parameters can be relatively easily. However, fuzzy systems have a serious disadvantage and it is the lack of flexibility. In this work, a hybrid genetic fuzzy system for managing financial assets and test results are presented.

Computer system for navigating a mobile robot

In this paper a fuzzy and communication system for managing and controlling the basic movements of a mobile robot is proposed. Several ultrasound and infrared sensors are installed on the robot and the collected data is operated with the means of fuzzy logic and thus decisions for the robot route are made. This intelligent system is created to navigate the mobile robot indoors in an unknown environment. The application areas of the robot are for embedded and mobile device, guiding of visual impaired people, mobile vehicle in technological lines, etc.∗

Parameters of GFSSAM: coding the parameters of a hybrid genetic fuzzy system

Context-driven systems provide information and/or services to the user and have specific features: a vaguely defined range of variables, contextual model, and context-sensitive services. This paper is focused on coding the parameters of a hybrid genetic fuzzy system that is designed to use a genetic algorithm for optimizing the knowledge base of a previously created fuzzy system. The hybrid context-driven rule-based system (GFSSAM=Genetic Fuzzy Software System for Asset Management) is a real-time software system for supporting the decision process in managing investment assets. In the investment process stakeholders analyse, model, use raw data, and make decisions in which the perspective context plays a key role and so any software system in this area has to provide reliable context-aware results. GFSSAM is developed and tested on real-time data from the stock exchange in a project for extensive research on optimization of the inference machine and knowledge base of a fuzzy system through hybridization.

Genetic Fuzzy System for Financial Management

Abstract This paper discusses genetic fuzzy systems – hybrid systems of artificial intelligence combining the potential of fuzzy sets for modeling approximate reasoning with the abilities of genetic algorithms for finding optimal solutions. The use of genetic algorithms for optimizing the parameters of a fuzzy system is demonstrated on GFSSAM.