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Deep Investment in Financial Markets using Deep Learning Models
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 162 - Number 2 |
| Year of Publication: 2017 |
| Authors: Saurabh Aggarwal, Somya Aggarwal |
10.5120/ijca2017913283
|
Saurabh Aggarwal, Somya Aggarwal . Deep Investment in Financial Markets using Deep Learning Models. International Journal of Computer Applications. 162, 2 ( Mar 2017), 40-43. DOI=10.5120/ijca2017913283
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.
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