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Deep learning for multivariate financial time series
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2015 (English)Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE creditsStudent thesisAlternative title
Deep Learning för finansiella tidsserier (Swedish)
Abstract [en]

Deep learning is a framework for training and modelling neural networks which recently have surpassed all conventional methods in many learning tasks, prominently image and voice recognition. This thesis uses deep learning algorithms to forecast financial data. The deep learning framework is used to train a neural network. The deep neural network is a Deep Belief Network (DBN) coupled to a Multilayer Perceptron (MLP). It is used to choose stocks to form portfolios. The portfolios have better returns than the median of the stocks forming the list. The stocks forming the S&P 500 are included in the study. The results obtained from the deep neural network are compared to benchmarks from a logistic regression network, a multilayer perceptron and a naive benchmark. The results obtained from the deep neural network are better and more stable than the benchmarks. The findings support that deep learning methods will find their way in finance due to their reliability and good performance.

Place, publisher, year, edition, pages
TRITA-MAT-E, 2015:40
National Category
Probability Theory and Statistics
URN: urn:nbn:se:kth:diva-168751OAI: diva2:820891
Subject / course
Mathematical Statistics
Educational program
Master of Science in Engineering -Engineering Physics
Available from: 2015-06-12 Created: 2015-06-08 Last updated: 2015-06-12Bibliographically approved

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