2,011 research outputs found
Recommended from our members
An intelligent system for risk classification of stock investment projects
The proposed paper demonstrates that a hybrid fuzzy neural network can serve as a risk classifier of stock investment projects. The training algorithm for the regular part of the network is based on bidirectional incremental evolution proving more efficient than direct evolution. The approach is compared with other crisp and soft investment appraisal and trading techniques, while building a multimodel domain representation for an intelligent decision support system. Thus the advantages of each model are utilised while looking at the investment problem from different perspectives. The empirical results are based on UK companies traded on the London Stock Exchange
SINVLIO: using semantics and fuzzy logic to provide individual investment portfolio recommendations
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 recommends investments grounded in both psychological aspects of the investor and traditional financial parameters of the investments. The results are very encouraging and reveal that SINVLIO makes good recommendations, according to the high degree of agreement between SINVLIO and expert recommendationsThis work is supported by the Spanish Ministry of Industry, Tourism, and Commerce under the projects SONAR2 (TSI-020100-2008-665) and the Spanish Ministry of Science and Innovation under the project âFINANCIAL LINKED OPEN DATA REASONING AND MANAGEMENT FOR WEB SCIENCEâ (TIN2011-27405).Publicad
Recommended from our members
Investment Risk Appraisal
Standard financial techniques neglect extreme situations and regards large market shifts as too unlikely to matter. This
approach may account for what occurs most of the time in the market, but the picture it presents does not reflect the reality, as the
major events happen in the rest of the time and investors are âsurprisedâ by âunexpectedâ market movements. An alternative fuzzy
approach permits fluctuations well beyond the probability type of uncertainty and allows one to make fewer assumptions about the
data distribution and market behaviour. Fuzzifying the present value criteria, we suggest a measure of the risk associated with each
investment opportunity and estimate the projectâs robustness towards market uncertainty. The procedure is applied to thirty-five UK
companies and a neural network solution to the fuzzy criterion is provided to facilitate the decision-making process. Finally, we
discuss the grounds for classical asset pricing model revision and argue that the demand for relaxed assumptions appeals for another
approach to modelling the market environment
Asset allocation with multiple analystsâ views: a robust approach
Retail investors often make decisions based on professional analystsâ investment recommendations. Although these recommendations contain up-to-date financial information, they are usually expressed in sophisticated but vague forms. In addition, the quality differs from analyst to analyst and recommendations may even be mutually conflicting. This paper addresses these issues by extending the BlackâLitterman (BL) method and developing a multi-analyst portfolio selection method, balanced against any over-optimistic forecasts. Our methods accommodate analystsâ ambiguous investment recommendations and the heterogeneity of data from disparate sources. We prove the validity of our model, using an empirical analysis of around 1000 daily financial newsletters collected from two top 10 Taiwanese brokerage firms over a 2-year period. We conclude that analystsâ views contribute to the investment allocation process and enhance the portfolio performance. We confirm that the degree of investorsâ confidence in these views influences the portfolio outcome, thus extending the idea of the BL model and improving the practicality of robust optimisation
PB-ADVISOR: A private banking multi-investment porfolio.
Private banking is a business area in which the investor requires tailor-made advice. Because of the current market situation, investors are requiring answers to difficult questions and looking for assurance from wealth managers. Private bankers need to have deep knowledge about an innumerable list of products and their characteristics as well as the suitability of each product for the clientâs characteristics to be able to offer an optimal portfolio according to client expectations. Client and portfolio diversity calls for new recommendation and advice systems focused on their specific characteristics. This paper presents PB-ADVISOR, a system aimed at recommending investment portfolios based on fuzzy and semantic technologies to private bankers. The proposed system provides private bankers with a powerful tool to support their decision process and help deal with complex investment portfolios. The system has been evaluated in a real scenario obtaining promising results
A Credit Rating Model in a Fuzzy Inference System Environment
One of the most important functions of an export credit agency (ECA) is to act as an intermediary between national governments and exporters. These organizations provide financing to reduce the political and commercial risks in international trade. The agents assess the buyers based on financial and non-financial indicators to determine whether it is advisable to grant them credit. Because many of these indicators are qualitative and inherently linguistically ambiguous, the agents must make decisions in uncertain environments. Therefore, to make the most accurate decision possible, they often utilize fuzzy inference systems. The purpose of this research was to design a credit rating model in an uncertain environment using the fuzzy inference system (FIS). In this research, we used suitable variables of agency ratings from previous studies and then screened them via the Delphi method. Finally, we created a credit rating model using these variables and FIS including related IF-THEN rules which can be applied in a practical setting
Building an ANFIS-based Decision Support System for Regional Growth: The Case of European Regions
This paper proposes a Decision Support System that can provide European policy makers with systematic guidance in allocating and prioritizing scant public resources. We do so by taking the stance of the Smart Specialisation Strategies which aim at consolidating the regional strengths and make effective and efficient use of public investment in R&D. By applying the ANFIS method we were able to understand how â and to what extent â the competitiveness drivers promoted technological development and how the latter contributes to the economic growth of European regions. We used socio-economic, spatial, and patent-based data to train, test and validate the models. What emerges is that an increase of R&D investments enhances the regional employment rate and the number of patents per capita; in turn, by taking into account the several combinations of specialization and diversification indicators, this leads to an increase of the regional GDP
EmC-ICDSST 2019: 5th International Conference on Decision Support System Technology - ICDSST 2019 & EURO Mini Conference 2019 on "Decision Support Systems: Main Developments & Future Trends"
info:eu-repo/semantics/publishedVersio
Model and management indicators in industrial omnichannel (B2B)
The COVID-19 pandemic has driven increases in the provision of services through digital channels, even by more traditional companies. An Omnichannel model of service provision poses new management challenges for companies. This research reviews the literature on Omnichannel Management by companies whose clients are other companies (B2B) and classifies the different areas of research to date. The principal finding is that, despite considerable academic interest in Omnichannel management, there have been few studies of Omnichannel in the B2B field. This emphasizes a significant research gap to address. We have also outlined the Research Agenda to highlight future lines of research
- âŠ