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Deep Learning for Multivariate Financial Time Series
Gilberto Batres-Estrada
Descriptin
Deep learning is a framework for training and modelling neural networkswhich recently have surpassed all conventional methods in many learning tasks, prominently image and voice recognition.
This textbook 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 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.
Keywords:
Back-Propagation Algorithm, Neural networks, Deep Belief Net-works, Multilayer Perceptron, Deep Learning, Contrastive Divergence, Greedy Layer-wise Pre-training.
Deep Learning for Multivariate Financial Time Series.pdf
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