A risk-aware fuzzy linguistic knowledge-based recommender system for hedge funds
作者： Álvaro Tejeda-Lorente , Juan Bernabé-Moreno , Julio Herce-Zelaya , Carlos Porcel , Enrique Herrera-Viedma
DOI: 10.1016/J.PROCS.2019.12.068
关键词:
摘要: Abstract One of the most difficult tasks for hedge funds investors is selecting a proper fund with just right level risk. Often times, issue not only quantifying risk, but also consider right. To support this decision, we propose novel recommender system, which aware risks associated to different funds, considering multiple factors, such as current yields, historic performance, diversification by industry, etc. Our system captures preferences (e.g. industries, desired risk) applying fuzzy linguistic modeling and provides personalized recommendations matching funds. demonstrate how our approach works, have first profiled more than 4000 top based on their composition performance second, created simulated investment profiles tested them.
sci-hub.se 参考文章(22)
William Fung Arms, David A Hsieh, None, Empirical Characteristics of Dynamic Trading Strategies: The Case of Hedge Funds Review of Financial Studies. ,vol. 10, pp. 275- 302 ,(1997) , 10.1093/RFS/10.2.275
L.A. Zadeh, The concept of a linguistic variable and its application to approximate reasoning—II☆ Information Sciences. ,vol. 8, pp. 199- 249 ,(1975) , 10.1016/0020-0255(75)90017-1
Álvaro Tejeda-Lorente, Juan Bernabé-Moreno, Carlos Porcel, Pablo Galindo-Moreno, Enrique Herrera-Viedma, A Dynamic Recommender System as Reinforcement for Personalized Education by a Fuzzly Linguistic Web System Procedia Computer Science. ,vol. 55, pp. 1143- 1150 ,(2015) , 10.1016/J.PROCS.2015.07.084
F. J. Cabrerizo, R. Al-Hmouz, A. Morfeq, A. S. Balamash, M. A. Martínez, E. Herrera-Viedma, Soft consensus measures in group decision making using unbalanced fuzzy linguistic information soft computing. ,vol. 21, pp. 3037- 3050 ,(2017) , 10.1007/S00500-015-1989-6
Santiago Hors-Fraile, Octavio Rivera-Romero, Francine Schneider, Luis Fernandez-Luque, Francisco Luna-Perejon, Anton Civit-Balcells, Hein de Vries, Analyzing recommender systems for health promotion using a multidisciplinary taxonomy: A scoping review. International Journal of Medical Informatics. ,vol. 114, pp. 143- 155 ,(2017) , 10.1016/J.IJMEDINF.2017.12.018
Francisco Javier Cabrerizo, Juan Antonio Morente-Molinera, Witold Pedrycz, Atefe Taghavi, Enrique Herrera-Viedma, Granulating linguistic information in decision making under consensus and consistency Expert Systems With Applications. ,vol. 99, pp. 83- 92 ,(2018) , 10.1016/J.ESWA.2018.01.030
Fatima Zohra Benkaddour, Noria Taghezout, Fatima Zahra Kaddour-Ahmed, Ilyes-Ahmed Hammadi, An Adapted Approach for User Profiling in a Recommendation System: Application to Industrial Diagnosis International Journal of Interactive Multimedia and Artificial Intelligence. ,vol. 5, pp. 118- 130 ,(2018) , 10.9781/IJIMAI.2018.06.003
Edward Rolando Núñez-Valdez, David Quintana, Ruben González Crespo, Pedro Isasi, Enrique Herrera-Viedma, A recommender system based on implicit feedback for selective dissemination of ebooks Information Sciences. ,vol. 467, pp. 87- 98 ,(2018) , 10.1016/J.INS.2018.07.068
Yin Zhang, Haider Abbas, Yi Sun, Smart e-commerce integration with recommender systems Electronic Markets. ,vol. 29, pp. 219- 220 ,(2019) , 10.1007/S12525-019-00346-X
Seth Siyuan Li, Elena Karahanna, Online Recommendation Systems in a B2C E-Commerce Context: A Review and Future Directions Journal of the Association for Information Systems. ,vol. 16, pp. 2- ,(2015) , 10.17705/1JAIS.00389