KTH Matematik  

Matematisk Statistik

Tid: 12 juni 2015 kl 14.15-15.00.

Seminarierummet 3721, Institutionen för matematik, KTH, Lindstedtsvägen 25, plan 7. Karta!

Föredragshållare: Gilberto Batres-Estrada

Titel: Deep learning for multivariate financial time series (Master's thesis)

Abstract 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.

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Sidansvarig: Filip Lindskog
Uppdaterad: 25/02-2009