A Bayesian-adaboost model for stock trading rule discovery

Zhoufan Kong, Jie Yang, Qinghua Huang, Xuelong Li

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

Abstract

Detecting the trading patterns with different technical indicators from the historical financial data is an efficient way to forecast the trading decisions in the financial market. In most cases, the trading patterns which consist of some specific combinations of technical indicators are significant in predicting the efficient trading decisions. However, discovering those combinations is a rather challenge assignment. In this paper, we propose a novel method to detect the trading patterns and later the Naive bayes with Adaboost method was employed to determine the trading decisions. The proposed method has been implemented on two historical stock datasets, the experimental results demonstrate that the proposed algorithm outperforms the other three algorithms and could provide a worthwhile reference for the financial investments.

Original languageEnglish
Title of host publicationProceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017
EditorsQingli Li, Lipo Wang, Mei Zhou, Li Sun, Song Qiu, Hongying Liu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781538619377
DOIs
StatePublished - 2 Jul 2017
Externally publishedYes
Event10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017 - Shanghai, China
Duration: 14 Oct 201716 Oct 2017

Publication series

NameProceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017
Volume2018-January

Conference

Conference10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017
Country/TerritoryChina
CityShanghai
Period14/10/1716/10/17

Keywords

  • Adaboost
  • biclusters
  • Naive Bayeians method
  • stock prediction

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