Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/41409
Title: Tracking federal funds target rate movements using artificial neural networks
Authors: V Hemamalini
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Issue Date: 2008
Abstract: 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.
URI: http://hdl.handle.net/10356/41409
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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