Tracking federal funds target rate movements using artificial neural networks

The Federal Reserve determines federal funds target rate (FFTR), which is one of the most publicized and anticipated economic indicator in the financial world. As the decision making process is complex due to unknown functions, it has been a difficult and challenging process for the researcher to...

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Main Author:	V Hemamalini
Other Authors:	Quah Tong Seng
Format:	Theses and Dissertations
Language:	English
Published:	2010
Subjects:	DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Online Access:	http://hdl.handle.net/10356/41409
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Institution:	Nanyang Technological University
Language:	English

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spelling	sg-ntu-dr.10356-414092023-07-04T16:30:17Z Tracking federal funds target rate movements using artificial neural networks V Hemamalini Quah Tong Seng School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems The Federal Reserve determines federal funds target rate (FFTR), which is one of the most publicized and anticipated economic indicator in the financial world. As the decision making process is complex due to unknown functions, it has been a difficult and challenging process for the researcher to model the thoughts of the FOMC members using statistical methods and hence predict the changes in FFTR. With artificial neural networks evolving as an efficient and promising methodology, it is possible to emulate the decision making of FOMC. Master of Science (Communication Software and Networks) 2010-07-02T06:48:08Z 2010-07-02T06:48:08Z 2008 2008 Thesis http://hdl.handle.net/10356/41409 en 87 p. application/pdf
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
language	English
topic	DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
spellingShingle	DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems V Hemamalini Tracking federal funds target rate movements using artificial neural networks
description	The Federal Reserve determines federal funds target rate (FFTR), which is one of the most publicized and anticipated economic indicator in the financial world. As the decision making process is complex due to unknown functions, it has been a difficult and challenging process for the researcher to model the thoughts of the FOMC members using statistical methods and hence predict the changes in FFTR. With artificial neural networks evolving as an efficient and promising methodology, it is possible to emulate the decision making of FOMC.
author2	Quah Tong Seng
author_facet	Quah Tong Seng V Hemamalini
format	Theses and Dissertations
author	V Hemamalini
author_sort	V Hemamalini
title	Tracking federal funds target rate movements using artificial neural networks
title_short	Tracking federal funds target rate movements using artificial neural networks
title_full	Tracking federal funds target rate movements using artificial neural networks
title_fullStr	Tracking federal funds target rate movements using artificial neural networks
title_full_unstemmed	Tracking federal funds target rate movements using artificial neural networks
title_sort	tracking federal funds target rate movements using artificial neural networks
publishDate	2010
url	http://hdl.handle.net/10356/41409
_version_	1772825613864796160

Tracking federal funds target rate movements using artificial neural networks

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