Home > Research > Publications & Outputs > Fusion of multiple diverse predictors in stock ...

Electronic data

  • accepted manuscript

    Rights statement: This is the author’s version of a work that was accepted for publication in Information Fusion. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information Fusion, 36, 2017 DOI: 10.1016/j.inffus.2016.11.006

    Accepted author manuscript, 1.93 MB, PDF document

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

Links

Text available via DOI:

View graph of relations

Fusion of multiple diverse predictors in stock market

Research output: Contribution to journalJournal article

Published
  • Sasan Barak
  • Azadeh Arjmand
  • Sergio Ortobelli
Close
<mark>Journal publication date</mark>1/07/2017
<mark>Journal</mark>Information Fusion
Volume36
Number of pages13
Pages (from-to)90-102
Publication statusPublished
Early online date9/11/16
Original languageEnglish

Abstract

Forecasting stock returns and their risk represents one of the most important concerns of market decision makers. Although many studies have examined single classifiers of stock returns and risk methods, fusion methods, which have only recently emerged, require further study in this area. The main aim of this paper is to propose a fusion model based on the use of multiple diverse base classifiers that operate on a common input and a Meta classifier that learns from base classifiers’ outputs to obtain more precise stock return and risk predictions. A set of diversity methods, including Bagging, Boosting and AdaBoost, is applied to create diversity in classifier combinations. Moreover, the number and procedure for selecting base classifiers for fusion schemes is determined using a methodology based on dataset clustering and candidate classifiers’ accuracy. The results demonstrate that Bagging exhibited superior performance within the fusion scheme and could achieve a maximum of 83.6% accuracy with Decision Tree, LAD Tree and Rep Tree for return prediction and 88.2% accuracy with BF Tree, DTNB and LAD Tree in risk prediction. For feature selection part, a wrapper-GA algorithm is developed and compared with the fusion model. This paper seeks to help researcher select the best individual classifiers and fuse the proper scheme in stock market prediction. To illustrate the approach, we apply it to Tehran Stock Exchange (TSE) data for the period from 2002 to 2012.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Information Fusion. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information Fusion, 36, 2017 DOI: 10.1016/j.inffus.2016.11.006