Horizontal generalization properties of fuzzy rule-based trading models

被引：0
|
作者：
Pereira, Celia da Costa [1 ]
Tettamanzi, Andrea G. B. [1 ]
机构：
[1] Univ Milan, Dipartimento Tecnol Informazione, I-26013 Crema, Italy
来源：
APPLICATIONS OF EVOLUTIONARY COMPUTING, PROCEEDINGS | 2008年 / 4974卷
关键词：
data mining; modeling; trading; evolutionary algorithms;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We investigate the generalization properties of a data-mining approach to single-position day trading which uses an evolutionary algorithm to construct fuzzy predictive models of financial instruments. The models, expressed as fuzzy rule bases, take a number of popular technical indicators on day t as inputs and produce a trading signal for day t + 1 based on a dataset of past observations of which actions would have been most profitable. The approach has been applied to trading several financial instruments (large-cap stocks and indices), in order to study the horizontal, i.e., cross-market, generalization capabilities of the models.
引用
收藏
页码：93 / 102
页数：10
相关论文
共 50 条
  • [31] Summarizer: Fuzzy Rule-Based Classification Systems for Vertical and Horizontal Big Data
    Tuy, Petala G. da S. E.
    Rios, Tatiane Nogueira
    [J]. 2020 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE), 2020,
  • [32] Effects of three-objective genetic rule selection on the generalization ability of fuzzy rule-based systems
    Ishibuchi, H
    Yamamoto, T
    [J]. EVOLUTIONARY MULTI-CRITERION OPTIMIZATION, PROCEEDINGS, 2003, 2632 : 608 - 622
  • [33] Rule-based generalization of threat without similarity
    Marstaller, Lars
    Al-Jiboury, Rizah
    Kemp, Andrew H.
    Dymond, Simon
    [J]. BIOLOGICAL PSYCHOLOGY, 2021, 160
  • [34] A RULE-BASED APPROACH FOR MERGING GENERALIZATION HIERARCHIES
    MANNINO, MV
    NAVATHE, SB
    EFFELSBERG, W
    [J]. INFORMATION SYSTEMS, 1988, 13 (03) : 257 - 272
  • [35] Fuzzy rule-based models via space partition and information granulation
    Pang, Yunhui
    Wang, Lidong
    Liu, Yifei
    Guo, Jiayi
    [J]. NEURAL COMPUTING & APPLICATIONS, 2022, 34 (19): : 16199 - 16211
  • [36] A Granular Approach to Interval Output Estimation for Rule-Based Fuzzy Models
    Zhu, Xiubin
    Pedrycz, Witold
    Li, Zhiwu
    [J]. IEEE TRANSACTIONS ON CYBERNETICS, 2022, 52 (07) : 7029 - 7038
  • [37] Design and Development of Granular Fuzzy Rule-Based Models for Knowledge Transfer
    Zhu, Xiubin
    Wang, Dan
    Pedrycz, Witold
    Li, Zhiwu
    [J]. IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2023, 53 (02): : 704 - 715
  • [38] Assessment of uncertainties in expert knowledge, illustrated in fuzzy rule-based models
    Janssen, J. A. E. B.
    Krol, M. S.
    Schielen, R. M. J.
    Hoekstra, A. Y.
    de Kok, J. -L.
    [J]. ECOLOGICAL MODELLING, 2010, 221 (09) : 1245 - 1251
  • [39] A Hybrid Learning Method for Constructing Compact Rule-Based Fuzzy Models
    Zhao, Wanqing
    Niu, Qun
    Li, Kang
    Irwin, George W.
    [J]. IEEE TRANSACTIONS ON CYBERNETICS, 2013, 43 (06) : 1807 - 1821
  • [40] Simplifying fuzzy rule-based models using orthogonal transformation methods
    Yen, J
    Wang, L
    [J]. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 1999, 29 (01): : 13 - 24
12345