Machine learning, and in particular deep learning, has shown outstanding performance to solving a wide variety of tasks from almost all fields of science. Application areas in quantitative finance include algorithmic trading, risk management, economic impact studies, asset allocation, and more. Deep learning can detect and exploit complex relationships in the data which are neglected by — or are even unknown to — standard quantitative finance methods today.
Xcelerit get clients ready to apply deep learning algorithms to their financial applications through an in-depth training, consisting of a mix of theoretical and hands-on sessions. Our trainers give practical advice on where to start, which algorithms to apply, and how to tune the learning systems. Topics covered include, but are not limited to, deep neural networks (DNNs), recurrent neural networks (RNNs), long short term memory models (LSTMs), and auto-encoders. The hands-on sessions are using tensorflow, but can be adapted to other frameworks such as Torch, Theano, or Caffe on request. The training also covers performance optimisations of training and inference using GPUs and high-performance hardware.
In this paper, we explore the application of machine learning to quantitative finance. Typical quant finance applications depend on vast amounts of economic data with complex relationships which are hard to grasp by humans or traditional quantitative finance approaches. Machine learning has tremendous potential here, producing results far superiour to traditional methods. In particular, machine learning can detect and exploit complex relationships in the data which are neglected by – or are even unknown to – standard quantitative finance methods today.
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