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Deep_Learning_for_Multivariate.pdf

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    omslag Gilberto
    inlägg Gilberto
    School of Engineering Sciences
    Rapport Gilberto
    omslag Gilberto
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    DEGREE PROJECT, IN STOCKHOLM, SWEDEN2015 MATHEMATICAL STATISTICS , SECOND LEVEL Deep Learning for Multivariate Financial Time Series GILBERTO BATRES-ESTRADA KTH ROYAL INSTITUTE OF TECHNOLOGY SCI SCHOOL OF ENGINEERING SCIENCES www.bigquant.com 人工智能量化平台
    www.bigquant.com 人工智能量化平台
    Deep learning for multivariate financial time series G I L B E R T O B A T R E S - E S T R A D A Degree Project in Mathematical Statistics (30 ECTS credits) Degree Programme in Engineering Physics (270 credits) Royal Institute of Technology year 2015 Supervisor at Söderberg & Partners: Peng Zhou Supervisor at KTH: Jonas Hallgren and Filip Lindskog Examiner: Filip Lindskog TRITA-MAT-E 2015:40 ISRN-KTH/MAT/E--15/40--SE Royal Institute of Technology School of Engineering Sciences KTH SCI SE-100 44 Stockholm, Sweden URL: www.kth.se/sci www.bigquant.com 人工智能量化平台
    www.bigquant.com 人工智能量化平台
    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 DBN coupled to a 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 bench- marks 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 learn- ing methods will find their way in finance due to their reliability and good performance. Keywords: Back-Propagation Algorithm, Neural networks, Deep Belief Net- works, Multilayer Perceptron, Deep Learning, Contrastive Divergence, Greedy Layer-wise Pre-training. www.bigquant.com 人工智能量化平台
    www.bigquant.com 人工智能量化平台
    Acknowledgements I would like to thank Söderberg & Partners, my supervisor Peng Zhou at Söderberg & Partners, my supervisor Jonas Hallgren and examiner Filip Lindskog at KTH Royal Institute of Technology for their support and guid- ance during the course of this interesting project. Stockholm, May 2015 Gilberto Batres-Estrada iv www.bigquant.com 人工智能量化平台
    www.bigquant.com 人工智能量化平台
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