Evaluating efficiency of ensemble classifiers in predicting the JSE all-share index attitude

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dc.contributor.author Ramsumar, Shaun
dc.date.accessioned 2017-11-06T06:30:36Z
dc.date.available 2017-11-06T06:30:36Z
dc.date.issued 2017
dc.identifier.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>
dc.identifier.uri http://hdl.handle.net/10539/23366
dc.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 en_ZA
dc.description.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. en_ZA
dc.format.extent Online resource (ix, 59 leaves)
dc.language.iso en en_ZA
dc.subject.lcsh Johannesburg Stock Exchange
dc.subject.lcsh Stocks--Prices--South Africa
dc.subject.lcsh Stocks--Prices--Mathematical models
dc.subject.lcsh Stock price forecasting--Mathematical models
dc.title Evaluating efficiency of ensemble classifiers in predicting the JSE all-share index attitude en_ZA
dc.type Thesis en_ZA
dc.description.librarian MT2017 en_ZA


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