Evaluating efficiency of ensemble classifiers in predicting the JSE all-share index attitude
Date
2017
Authors
Ramsumar, Shaun
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Abstract
The prediction of stock price and index level in a financial market is an interesting
but highly complex and intricate topic. Advancements in prediction models leading
to even a slight increase in performance can be very profitable. The number of studies
investigating models in predicting actual levels of stocks and indices however, far
exceed those predicting the direction of stocks and indices. This study evaluates the
performance of ensemble prediction models in predicting the daily direction of the
JSE All-Share index. The ensemble prediction models are benchmarked against three
common prediction models in the domain of financial data prediction namely, support
vector machines, logistic regression and k-nearest neighbour. The results indicate that
the Boosted algorithm of the ensemble prediction model is able to predict the index
direction the best, followed by k-nearest neighbour, logistic regression and support
vector machines respectively. The study suggests that ensemble models be considered
in all stock price and index prediction applications.
Description
A research report submitted to the Faculty of Commerce, Law and Management, University
of the Witwatersrand, Johannesburg, in partial fulfillment of the requirements for the degree
of Master of Management in Finance and Investment.
Johannesburg, 2016
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Citation
Ramsumar, Shaun (2017) Evaluating efficiency of ensemble classifiers in predicting the JSE all-share index attitude, University of the Witwatersrand, Johannesburg, <http://hdl.handle.net/10539/23366>