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A comparative study on one-day-ahead stock prediction using regression tree and artificial neural network
KTH, School of Computer Science and Communication (CSC).
KTH, School of Computer Science and Communication (CSC).
2016 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Making good predictions for stock prices is an important task for the financial industry. The way these predictions are carried out is often by using artificial intelligence that can learn from the data using machine learning algorithms. This study compares three different approaches in this area, using a regression tree and two artificial neural networks with two different learning algorithms. The learning algorithms used was Levenberg-Marquardt and Bayesian regularization. These three approaches was evaluated using the average misprediction and worst misprediction they made in the selected interval from two different indexes, OMXS30 and S&P-500. Out of these three approaches the artificial neural networks outperformed the regression tree and the Bayesian regularization algorithms performed the best out of the two learning algorithms. The conclusions did support the usage of artificial neural networks but was not able to fully establish that the Bayesian regularization algorithm would be the best performing in the general case.

Place, publisher, year, edition, pages
National Category
Computer Science
URN: urn:nbn:se:kth:diva-186499OAI: diva2:927459
Available from: 2016-05-12 Created: 2016-05-12 Last updated: 2016-05-12Bibliographically approved

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