Data-based identification of short term predictors for stock market trends using heterogeneous model ensembles

Publikation: Beitrag in Buch/Bericht/TagungsbandKonferenzbeitragBegutachtung

Abstract

We here show the application of heterogeneous ensemble modeling for training short term predictors of trends in stock markets. A sliding window approach is used; model ensembles are iteratively learned and tested on subsequent data points. The goal is to predict trends (positive, neutral, or negative stock changes) for the next day, the next week, and the next month. Several machine learning approaches implemented in HeuristicLab and WEKA have been applied; the models produced using these methods have been combined to heterogeneous model ensembles. We calculate the final estimation for each sample via majority voting, and the relative ratio of a sample's majority vote is used for calculating the confidence in the final estimation; we use a confidence threshold that specifies the minimum confidence level that has to be reached. We show results of empirical tests performed using data of the Spanish stock market recorded from 2003 to 2013.

OriginalspracheEnglisch
Titel26th European Modeling and Simulation Symposium, EMSS 2014
Redakteure/-innenYuri Merkuryev, Lin Zhang, Emilio Jimenez, Francesco Longo, Michael Affenzeller, Agostino G. Bruzzone
Herausgeber (Verlag)DIME UNIVERSITY OF GENOA
Seiten40-45
Seitenumfang6
ISBN (elektronisch)9788897999324
PublikationsstatusVeröffentlicht - 2014
Veranstaltung26th European Modeling and Simulation Symposium, EMSS 2014 - Bordeaux, Frankreich
Dauer: 10 Sep 201412 Sep 2014

Publikationsreihe

Name26th European Modeling and Simulation Symposium, EMSS 2014

Konferenz

Konferenz26th European Modeling and Simulation Symposium, EMSS 2014
LandFrankreich
OrtBordeaux
Zeitraum10.09.201412.09.2014

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