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- Publisher Website: 10.1109/GCAT47503.2019.8978299
- Scopus: eid_2-s2.0-85084812559
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Conference Paper: LIBOR Prediction Using Genetic Algorithm and Genetic Algorithm Integrated with Recurrent Neural Network
Title | LIBOR Prediction Using Genetic Algorithm and Genetic Algorithm Integrated with Recurrent Neural Network |
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Authors | |
Keywords | Machine learning Libor genetic algorithm genetic algorithm integrated with recurrent neural network |
Issue Date | 2020 |
Publisher | IEEE. |
Citation | 2019 Global Conference for Advancement in Technology (GCAT), Bangaluru, India, 18-20 October 2019, p. 1-8 How to Cite? |
Abstract | LIBOR (London Inter Banking Offered Rate) market, as one of the most important interest rate market to display the liquidity of the currency flow globally, is a huge market with global stake holders to be involved. The rate is a significant indicator for the liquidity risk of global markets in different currencies and for the stability of the finance markets in main countries. This thesis is to predict the LIBOR in USD with two algorithms: the pure Genetic Algorithm ('GA') and the Genetic Algorithm integrated with Recurrent Neural Network ('GA + RNN'). The pro and cons of these two algorithms will be compared, with fitness values and Mean Squared Error ('MSE'). 50 test cases will be created and tested randomly to verify the performance of the predictions. In the algorithm of RNN+GA, the target variance between predication and real value is no more than 0.015. |
Persistent Identifier | http://hdl.handle.net/10722/289175 |
ISBN |
DC Field | Value | Language |
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dc.contributor.author | Tan, X | - |
dc.date.accessioned | 2020-10-22T08:08:54Z | - |
dc.date.available | 2020-10-22T08:08:54Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | 2019 Global Conference for Advancement in Technology (GCAT), Bangaluru, India, 18-20 October 2019, p. 1-8 | - |
dc.identifier.isbn | 9781728136950 | - |
dc.identifier.uri | http://hdl.handle.net/10722/289175 | - |
dc.description.abstract | LIBOR (London Inter Banking Offered Rate) market, as one of the most important interest rate market to display the liquidity of the currency flow globally, is a huge market with global stake holders to be involved. The rate is a significant indicator for the liquidity risk of global markets in different currencies and for the stability of the finance markets in main countries. This thesis is to predict the LIBOR in USD with two algorithms: the pure Genetic Algorithm ('GA') and the Genetic Algorithm integrated with Recurrent Neural Network ('GA + RNN'). The pro and cons of these two algorithms will be compared, with fitness values and Mean Squared Error ('MSE'). 50 test cases will be created and tested randomly to verify the performance of the predictions. In the algorithm of RNN+GA, the target variance between predication and real value is no more than 0.015. | - |
dc.language | eng | - |
dc.publisher | IEEE. | - |
dc.relation.ispartof | 2019 Global Conference for Advancement in Technology (GCAT) | - |
dc.rights | 2019 Global Conference for Advancement in Technology (GCAT). Copyright © IEEE. | - |
dc.rights | ©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | - |
dc.subject | Machine learning | - |
dc.subject | Libor | - |
dc.subject | genetic algorithm | - |
dc.subject | genetic algorithm integrated with recurrent neural network | - |
dc.title | LIBOR Prediction Using Genetic Algorithm and Genetic Algorithm Integrated with Recurrent Neural Network | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/GCAT47503.2019.8978299 | - |
dc.identifier.scopus | eid_2-s2.0-85084812559 | - |
dc.identifier.hkuros | 317073 | - |
dc.identifier.spage | 1 | - |
dc.identifier.epage | 8 | - |
dc.publisher.place | Bangaluru, India | - |