Prediction of stock market indices using machine learning

Description

Financial time series prediction is a very important economical problem but the data available is very noisy. In this thesis, we explain the use of statistical and machine learning methods for stock market prediction and we evaluate the performance of these methods on data from the S&P/TSX 60 stock index. We use both linear regression and support vector regression, a state-of-art machine learning method, which is usually robust to noise. The results are mixed, illustrating the difficulty of the problem. We discuss the utility of using different types of data pre-processing for this task as well. / La prediction des series de donnees economiques est un probleme tres important, mais les donnees disponiblessont tres aleatoires. Dans cette these, nous expliquons l'utilisation des statistiques et des methodes d'apprentissage automatique en vue de prevoir la valuer prochaine du S&P/TSX60. Nous utilisons deux methodes: la regression lineaire et les machines a vecteur de support pour la regression, une methode d'apprentissagemoderne, qui est tres robuste. Les resultats sont mitiges, illustrant la difficulte du probleme. Nous discutons l'utilite des differents types de donnees et le pre-traitement necessaire pour cette tache.}

Links & Downloads

1. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=40795

Tags

Applied Sciences - Computer Science

Additional Fields

Identifer	oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:QMM.40795
Date	January 2009
Creators	Modarres Najafabadi, Sayed Reza
Contributors	Doina Precup (Internal/Supervisor), Russell Steele (Internal/Cosupervisor2)
Publisher	McGill University
Source Sets	Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada
Language	English
Detected Language	English
Type	Electronic Thesis or Dissertation
Format	application/pdf
Coverage	Master of Science (School of Computer Science)
Rights	All items in eScholarship@McGill are protected by copyright with all rights reserved unless otherwise indicated.
Relation	Electronically-submitted theses.