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Reseach Article

Deep Investment in Financial Markets using Deep Learning Models

by Saurabh Aggarwal, Somya Aggarwal
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

@article{ 10.5120/ijca2017913283,
author = { Saurabh Aggarwal, Somya Aggarwal },
title = { Deep Investment in Financial Markets using Deep Learning Models },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2017 },
volume = { 162 },
number = { 2 },
month = { Mar },
year = { 2017 },
issn = { 0975-8887 },
pages = { 40-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume162/number2/27218-2017913283/ },
doi = { 10.5120/ijca2017913283 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:07:54.957058+05:30
%A Saurabh Aggarwal
%A Somya Aggarwal
%T Deep Investment in Financial Markets using Deep Learning Models
%J International Journal of Computer Applications
%@ 0975-8887
%V 162
%N 2
%P 40-43
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
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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Index Terms

Computer Science
Information Sciences

Keywords

Machine learning deep learning artificial intelligence neural network credit risk stock market