Using neural networks to pick stocks : a fundamental approach

71 p.

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Bibliographic Details
Main Authors: Han, Cheryln Shiao Ian, Quak, Jin Lim, Tan, Yong Yee
Other Authors: Lim Boon Chye
Format: Final Year Project
Published: 2014
Subjects:
Online Access:http://hdl.handle.net/10356/57670
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Institution: Nanyang Technological University
id sg-ntu-dr.10356-57670
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spelling sg-ntu-dr.10356-576702023-05-19T03:30:02Z Using neural networks to pick stocks : a fundamental approach Han, Cheryln Shiao Ian Quak, Jin Lim Tan, Yong Yee Lim Boon Chye Nanyang Business School DRNTU::Business 71 p. The objective of this study is to test the applicability of artificial neural network (ANN) in choosing stocks in Singapore based on financial ratios analysis. The model used in this study draws on the financial ratios used by Yeo et. al. (1992) and Kryzanowski et. al. (1993). ACCOUNTANCY 2014-04-07T10:54:08Z 2014-04-07T10:54:08Z 1997 1997 Final Year Project (FYP) http://hdl.handle.net/10356/57670 Nanyang Technological University application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
topic DRNTU::Business
spellingShingle DRNTU::Business
Han, Cheryln Shiao Ian
Quak, Jin Lim
Tan, Yong Yee
Using neural networks to pick stocks : a fundamental approach
description 71 p.
author2 Lim Boon Chye
author_facet Lim Boon Chye
Han, Cheryln Shiao Ian
Quak, Jin Lim
Tan, Yong Yee
format Final Year Project
author Han, Cheryln Shiao Ian
Quak, Jin Lim
Tan, Yong Yee
author_sort Han, Cheryln Shiao Ian
title Using neural networks to pick stocks : a fundamental approach
title_short Using neural networks to pick stocks : a fundamental approach
title_full Using neural networks to pick stocks : a fundamental approach
title_fullStr Using neural networks to pick stocks : a fundamental approach
title_full_unstemmed Using neural networks to pick stocks : a fundamental approach
title_sort using neural networks to pick stocks : a fundamental approach
publishDate 2014
url http://hdl.handle.net/10356/57670
_version_ 1770565954780004352