Neural network prediction and decision making system for investment assets

Cesar Valencia N, Alfredo Sanabria, Hernando González A, Carlos Arizmendi P, David Orjuela C

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

Abstract

The problem is to test the weak hypothesis of efficient markets through three neural networks that can predict the trends of investment assets such as: The Dow Jones, gold and Euro dollar, according to theories of technical analysis to automate positions of both long and short investment in the Spot market. With regard to forecasting time series, multiple approaches have been tested, through statistical models such as [1–3], where forecasts are made from different information sources with characteristics differentiated (sasonality, tendency, periodicity), however, other actors have begun to gain strength by getting the first places in international competitions, this is the case of Neural Networks, in works published as [4–6] the results have shown that this type of model offers a real opportunity to work with time series of different characteristics.

Original languageEnglish
Title of host publicationAETA 2019 - Recent Advances in Electrical Engineering and Related Sciences
Subtitle of host publicationTheory and Application
EditorsDario Fernando Cortes Tobar, Vo Hoang Duy, Tran Trong Dao
PublisherSpringer
Pages262-272
Number of pages11
ISBN (Print)9783030530204
DOIs
StatePublished - 2021
Event6th International Conference on Advanced Engineering Theory and Applications, AETA 2019 - Bogota, Colombia
Duration: 6 Nov 20198 Nov 2019

Publication series

NameLecture Notes in Electrical Engineering
Volume685 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference6th International Conference on Advanced Engineering Theory and Applications, AETA 2019
Country/TerritoryColombia
CityBogota
Period6/11/198/11/19

Keywords

  • Assests
  • Decision making
  • Invesment
  • Neural network
  • Prediction

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