Abstract

The aim of this paper is to layout deep investment techniques in financial markets using deep learning models. Financial prediction problems usually involve huge variety of data-sets with complex data interactions which makes it difficult to design an economic model. Applying deep learning models to such problems can exploit potentially non-linear patterns in data. In this paper author introduces deep learning hierarchical decision models for prediction analysis and better decision making for financial domain problem set such as pricing securities, risk factor analysis and portfolio selection. The Section 3 includes architecture as well as detail on training a financial domain deep learning neural network. It further lays out different models such as- LSTM, auto-encoding, smart indexing, credit risk analysis model for solving the complex data interactions. The experiments along with their results show how these models can be useful in deep investments for financial domain problems.

References

• The behavior of stock market prices, by Eugene F. Fama, Journal of Business, Volume 38, Issue 1, Jan 1965, 34-105.
• Deep Learning for Event-Driven Stock Prediction, by Xiao Ding, Yue Zhang , Ting Liu , Junwen Duan, Harbin Institute of Technology, China, Singapore University of Technology and Design
• An Artificial Neural Network Approach for Credit Risk Management By- Vincenzo Pacelli1, Michele Azzollini, Journal of Intelligent Learning Systems and Applications, 2011, 3, 103-112
• Srivastava et al.: Dropout: a simple way to prevent neural networks from overfitting, Journal of Machine Learning Research, 15, 1929-1958, 2014.
• J. M. Hutchinson, A. W. Lo, and T. Poggio: A Nonparametric approach to pricing and hedging derivative securities via learning networks, Journal of Finance, Vol. 48(3), pp. 851-889, 1994.
• Learning Deep Architectures for AI, Yoshua Bengio, Foundations and Trends in Machine Learning
• Rectified Linear Units Improve Restricted Boltzmann Machines, By Vinod Nair and Geoffrey E. Hinton
• Learning Deep Architectures for AI, By Yoshua Bengio, Foundations and TrendsR in Machine Learning Vol. 2, No. 1 (2009) 1–127
• Tutorial on Deep Learning Part 2: Autoencoders, Convolutional Neural Networks and Recurrent Neural Networks, By Quoc V. Le, Stanford.
• Long Short-Term Memory Recurrent Neural Network Architectures for Large Scale Acoustic Modeling, Has¸im Sak, Andrew Senior, Franc¸oise Beaufays, Google
• An Artificial Neural Network Approach for Credit Risk Management Vincenzo Pacelli1, Michele Azzollini Journal of Intelligent Learning Systems and Applications, 2011, 3, 103-112
• Deep Learning in Finance, By- J. B. Heaton, N. G. Polson, J. H. Witte, 23-Feb 2016
• Deep vs. Shallow Networks: an Approximation Theory Perspective By Hrushikesh N. Mhaskar1 and Tomaso Poggio, Aug 2016
• Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identification By Tong Xiao ,Hongsheng Li ,Wanli Ouyang ,Xiaogang Wang