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Deeplearning in finance
The document discusses the application of deep learning in finance, highlighting its growth and potential within various sectors such as medical imaging, robotics, and military. Key advancements in artificial neural networks, including historical developments and major breakthroughs, are explored, alongside their applications in trading systems and data analysis. Additionally, it mentions challenges in deep learning, such as slow convergence and the difficulty of training deep networks, while also presenting alternative approaches like particle swarm optimization for improving neural network training.
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Deeplearning in finance
- 1. Deep learning infinance Data science in finance 28 / 07 Sébastien Jehan
- 2. CAPITAL FUND MANAGEMENT Fundamental research applied to financial markets CFM invests in Science, Technology and Finance 23 years of experience in managing trading systems A rational process that produces robust Trading Algorithms Proprietary software able to execute & control large volumes Opportunities : • Software Engineers mastering C++, Python… • System, Network & Database Administrators • PhDs in theoretical physics, applied mathematics, informatics… PROPRIETARY AND CONFIDENTIAL - NOT FOR REDISTRIBUTION
- 3. Artificial Neural Networks -History of Artificial Neural Networks - Recurrent Neural Networks - Applications in Finance AI for the enterprise 2015 $220 millions 2025 $11.1 billions (+56% / year)
- 4. Current applications toneural networks • Medical image processing: mostly feed forward neural network • Robokinetics and robovision: smooth moves and object detection • Military: DARPA Synapse, objective 10 billion neurons (86 billions human brain) in 2 liters space for end of 2016. • Email spam detection, Image classification • Text recognition
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- 11. Applications Is A orB Linear problem
- 12. Applications Is A orB Not linearly separable => no convergence
- 13. 1986: Multilayers perceptron inputvector hidden layers outputs
- 14. 1986: Multilayers perceptron inputvector hidden layers outputs +29 ANS
- 15. 1986: Multilayers perceptron inputvector hidden layers outputs Back- propagate error signal
- 16. Back propagation Activations The error: Update Weights: 0 1 0 .5 -55 Slide credit : Geoffrey H
- 17. Back propagation Activations The error: Update Weights: 0 1 0 .5 -55 Slide credit : Geoffrey Hinton errors
- 18. Drawbacks GRADIENT NATURE =>SLOW CONVERGENCE => SLOW LEARNING
- 19. Drawbacks GRADIENT NATURE =>SLOW CONVERGENCE => SLOW LEARNING LEARNING DECREASE WITH HIDDEN LAYERS NUMBERS
- 20. Drawbacks GRADIENT NATURE =>SLOW CONVERGENCE => SLOW LEARNING LEARNING DECREASE WITH HIDDEN LAYERS NUMBERS CAN FIND ONLY LOCAL MINIMA OF ERRORS
- 21. Before 2006: Thedeapest, the worst DEEP NETWORKS (LOT OF HIDDEN LAYERS) ARE WORSE THAN WITH ONE OR TWO LAYERS ! SLOWER AND LESS ACCURATE
- 22. Deep ? Howdeep ? DEEP IS >=5 LAYERS
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- 24. Who cares ? 2013:Director of Facebook AI research Google distinguished researcher Montreal University
- 25. The effect ofunsupervised learning WITHOUT UNSUPERVISED LEARNING (top quartiles) WITH UNSUPERVISED LEARNING
- 28. Becomes more non-linear,and this is good: it prevents the gradient learning to be transferred to previous layers for local optima The first layer should react to input changes.
- 29. Becomes more non-linear,and this is good: it prevents the gradient learning to be Yet we don’t know how. Just represent the dominant factors of variation of the input.
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- 38. Infrastructure costs • Thebad news In 2012, It took Google 16.000 CPU to have a single process real time cat face identifier http://hexus.net/tech/news/software/41 537-googles-16000-cpu-neural-network- can-identify-cat/ • The good news In 2017, public Beta testing of HP “The machine”, based on Memristors replacing transistors for some parts of the chip. http://insidehpc.com/2015/01/video- memristor-research-at-hp-labs/
- 39. Imagenet classification results 2012 2014Deep Learning GoogleNet 6.66% 2015 Microsoft Research 4.94 % ai is learned http://arxiv.org/pdf/1502.01852v1.pdf
- 40. Reverse engineering deeplearning results(2012) SUPERVISED IMAGE CLASSIFICATIONS + OTHER TRAININGS First layer: always Gabor Filters like or Color Blob
- 41. Reverse engineering deeplearning results(Nov 2014) SUPERVISED IMAGE CLASSIFICATIONS + OTHER TRAININGS General or Specific Layer ?? Transfer ANN layers among trained models
- 42. Static deep learningin finance + Person detection PMFG Performance heat map … Trade opportunity detection Market States Current markets … Reduce information redundancy for a goal
- 43. PMFG: Planar MaximallyFiltered Graph of the correlation matrix Correlation, hierarchies and Networks in Financial Markets
- 44. “market states” Cluster time periodsby correlation matrix similarity
- 45. Heat map 1year perf, 23/07
- 46. 1990: RNN nonstationary I/Omapping, Y(t)f(X(t)), X(t) and Y(t) are timevarying patterns
- 47. 1990: RNN nonstationary I/Omapping, Y(t)f(X(t)), X(t) and Y(t) are timevarying patterns The closest thing to computer dreams
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- 49. Problems in RNN Timelag > 5 => Difficult to learn The error either vanishes or explodes in the learning process Divergent behavior
- 50. RNN for GARCH(1,1)predictions IN SAMPLE: 665 observations 01/2011 09/2013 OUT OF SAMPLE: 252 observations 09/2013 09/2014 LAG=1, NO PROBLEM
- 51. A new approach:RNN with wavelet sigmoid (2D, time and frequency) April 2015 PREDICT “SEMI-CHAOTIC” TIME SERIE (MACKEY-GLASS), SOLUTION OF Rapidly vanishing property of wavelet function => No divergent behavior
- 52. Particle swarm optimization •Concepts • Applications: Training a neural network using PSO instead of backpropagation
- 53. PSO principles Here Iam! The best perf. of my neighbours Mybest perf. x pg pi v Collective Intelligence: Particles Adjust their positions according to a ``Psychosocial compromise’’ between what an individual is comfortable with, and what society reckons => Solve continuous optimization problems
- 54. Training neural networkwith PSO (started in 2006) Unsupervised training with PSO: Not trapped in local minima Faster than back propagation
- 55. PSO ANN accuracy(July 2014) Levenberg-Marquadt: second order in BP error evaluation Credit Approval dataset, UCI
- 56. The challenge of multidimensionality -Dimensionality reduction techniques
- 57. Dimension reduction techniques PCAusing Neural Networks(2012) PCA with Gaussian assumption: Training set: 1/16 compression, 4x4 blocks Result
- 58. Radial Basis FunctionNeural Network with (2D)^2 PCA (April 2015, Shangai index) TRAIN TEST
- 59. Radial Basis FunctionNeural Network with (2D)^2 PCA (April 2015, Shangai index) TRAIN TEST Exercise: find the issue
- 60. Thank you International workshopon Deep Learning, Lille, France (July 10/11 2015): https://sites.google.com/site/deeplearning2015/accepted- papers