Neural networks, fuzzy inference systems and adaptive-neuro fuzzy inference systems for financial decision making

Pretesh B. Patel, Tshilidzi Marwala

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

23 Citations (Scopus)

Abstract

This paper employs pattern classification methods for assisting investors in making financial decisions. Specifically, the problem entails the categorization of investment recommendations. Based on the forecasted performance of certain indices, the Stock Quantity Selection Component is to recommend to the investor to purchase stocks, hold the current investment position or sell stocks in possession. Three designs of the component were implemented and compared in terms of their complexity as well as scalability. Designs that utilized 1, 4 and 16 classifiers, respectively, were developed. These designs were implemented using Artificial Neural Networks, Fuzzy Inference Systems as well as Adaptive Neuro-Fuzzy Inference Systems. The design that employed 4 classifiers achieved low complexity and high scalability. As a result, this design is most appropriate for the application of concern.

Original languageEnglish
Title of host publicationNeural Information Processing - 13th International Conference, ICONIP 2006, Proceedings
PublisherSpringer Verlag
Pages430-439
Number of pages10
ISBN (Print)3540464840, 9783540464846
DOIs
Publication statusPublished - 2006
Externally publishedYes
Event13th International Conference on Neural Information Processing, ICONIP 2006 - Hong Kong, China
Duration: 3 Oct 20066 Oct 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4234 LNCS - III
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Conference on Neural Information Processing, ICONIP 2006
Country/TerritoryChina
CityHong Kong
Period3/10/066/10/06

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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