Prediction accuracy improvement for Bitcoin market prices based on symmetric volatility information using artificial neural network approach

Generally, information is the fundamental driver of assets pricing volatility in the financial market. This information can enter into the market either symmetrically or asymmetrically. The financial literature shows that Bitcoin market volatility is symmetrically informative and has a long memory t...

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Main Authors:	Othman, Anwar Hasan Abdullah, Kassim, Salina, Rosman, Romzie, Redzuan, Nur Harena
Format:	Article
Language:	English English
Published:	Springer Nature 2020
Subjects:	HB131 Methodology.Mathematical economics. Quantitative methods HB221 Price
Online Access:	http://irep.iium.edu.my/78301/ http://irep.iium.edu.my/78301/ http://irep.iium.edu.my/78301/ http://irep.iium.edu.my/78301/1/78301-Prediction%20accuracy%20improvement%20for%20Botcoin%20market%20prices...%20-%20scopus.pdf http://irep.iium.edu.my/78301/2/78301-%20Prediction%20accuracy%20improvement%20for%20Bitcoin%20market%20prices.pdf

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recordtype	eprints
spelling	iium-783012020-02-03T09:11:10Z http://irep.iium.edu.my/78301/ Prediction accuracy improvement for Bitcoin market prices based on symmetric volatility information using artificial neural network approach Othman, Anwar Hasan Abdullah Kassim, Salina Rosman, Romzie Redzuan, Nur Harena HB131 Methodology.Mathematical economics. Quantitative methods HB221 Price Generally, information is the fundamental driver of assets pricing volatility in the financial market. This information can enter into the market either symmetrically or asymmetrically. The financial literature shows that Bitcoin market volatility is symmetrically informative and has a long memory to persist in the future. Additionally, the symmetricity of volatility has been revealed to be of greater sensitivity to its past values compared to the new shock of the market values. This study therefore applied the symmetric volatility structure of Bitcoin currency which can be measured through four input attributes such as open price (OP), high price (HP), low price (LP), and close price (CP) for predicting its price future trend. The study uses Rapid-Miner programme based on artificial neural network (ANN) algorithm. The optimal model employs a multilayer neural network (NN) along with an “optimised operator” with the ability to locate the optimal factor loading of the applied algorithm. The findings indicate that ANN is an effective and adequate model for correctly predicting Bitcoin market prices using symmetric volatility attributes with accuracy level of 92.15% against the actual price, whereas the low price attribute is found to be the major promoter for Bitcoin price trend with percentage of 63%. This is followed by close price, high price, and open price with percentages of 49%, 46%, and 37%, respectively. The findings of the study therefore would be a valuable and significant input for commercial purposes among the cryptocurrency market players. In other worlds, based on these outcomes investors will proactively predicate the Bitcoin price trend and make the right investment decision either to buy, hold, or sale to gain up normal market return. This is considered a pioneering study that predicates the Bitcoin price trend based on its symmetric volatility structure. As these replication findings demonstrate, the proposed model is highly promising and applicable in a real-time trading system for predicting Bitcoin price future trend and maximising investment profits in Cryptocurrency markets. Springer Nature 2020-01-28 Article PeerReviewed application/pdf en http://irep.iium.edu.my/78301/1/78301-Prediction%20accuracy%20improvement%20for%20Botcoin%20market%20prices...%20-%20scopus.pdf application/pdf en http://irep.iium.edu.my/78301/2/78301-%20Prediction%20accuracy%20improvement%20for%20Bitcoin%20market%20prices.pdf Othman, Anwar Hasan Abdullah and Kassim, Salina and Rosman, Romzie and Redzuan, Nur Harena (2020) Prediction accuracy improvement for Bitcoin market prices based on symmetric volatility information using artificial neural network approach. Journal of Revenue and Pricing Management. ISSN 1476-6930 E-ISSN 1477-657X https://link.springer.com/epdf/10.1057/s41272-020-00229-3?author_access_token=L2DMxsXLPVIsJmH_5IOjuVxOt48VBPO10Uv7D6sAgHtSnGVFWNTxpwE4wrOv6_erM2oJhoPCSYSF6fjDTV9fqKqZE9oiGh_WUomMvvP80X8HVDUwwwsKc4KL4uXIVv777KmpMpZhT8k9TM33s6cmEw%3D%3D https://doi.org/10.1057/s41272-020-00229-3
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	HB131 Methodology.Mathematical economics. Quantitative methods HB221 Price
spellingShingle	HB131 Methodology.Mathematical economics. Quantitative methods HB221 Price Othman, Anwar Hasan Abdullah Kassim, Salina Rosman, Romzie Redzuan, Nur Harena Prediction accuracy improvement for Bitcoin market prices based on symmetric volatility information using artificial neural network approach
description	Generally, information is the fundamental driver of assets pricing volatility in the financial market. This information can enter into the market either symmetrically or asymmetrically. The financial literature shows that Bitcoin market volatility is symmetrically informative and has a long memory to persist in the future. Additionally, the symmetricity of volatility has been revealed to be of greater sensitivity to its past values compared to the new shock of the market values. This study therefore applied the symmetric volatility structure of Bitcoin currency which can be measured through four input attributes such as open price (OP), high price (HP), low price (LP), and close price (CP) for predicting its price future trend. The study uses Rapid-Miner programme based on artificial neural network (ANN) algorithm. The optimal model employs a multilayer neural network (NN) along with an “optimised operator” with the ability to locate the optimal factor loading of the applied algorithm. The findings indicate that ANN is an effective and adequate model for correctly predicting Bitcoin market prices using symmetric volatility attributes with accuracy level of 92.15% against the actual price, whereas the low price attribute is found to be the major promoter for Bitcoin price trend with percentage of 63%. This is followed by close price, high price, and open price with percentages of 49%, 46%, and 37%, respectively. The findings of the study therefore would be a valuable and significant input for commercial purposes among the cryptocurrency market players. In other worlds, based on these outcomes investors will proactively predicate the Bitcoin price trend and make the right investment decision either to buy, hold, or sale to gain up normal market return. This is considered a pioneering study that predicates the Bitcoin price trend based on its symmetric volatility structure. As these replication findings demonstrate, the proposed model is highly promising and applicable in a real-time trading system for predicting Bitcoin price future trend and maximising investment profits in Cryptocurrency markets.
format	Article
author	Othman, Anwar Hasan Abdullah Kassim, Salina Rosman, Romzie Redzuan, Nur Harena
author_facet	Othman, Anwar Hasan Abdullah Kassim, Salina Rosman, Romzie Redzuan, Nur Harena
author_sort	Othman, Anwar Hasan Abdullah
title	Prediction accuracy improvement for Bitcoin market prices based on symmetric volatility information using artificial neural network approach
title_short	Prediction accuracy improvement for Bitcoin market prices based on symmetric volatility information using artificial neural network approach
title_full	Prediction accuracy improvement for Bitcoin market prices based on symmetric volatility information using artificial neural network approach
title_fullStr	Prediction accuracy improvement for Bitcoin market prices based on symmetric volatility information using artificial neural network approach
title_full_unstemmed	Prediction accuracy improvement for Bitcoin market prices based on symmetric volatility information using artificial neural network approach
title_sort	prediction accuracy improvement for bitcoin market prices based on symmetric volatility information using artificial neural network approach
publisher	Springer Nature
publishDate	2020
url	http://irep.iium.edu.my/78301/ http://irep.iium.edu.my/78301/ http://irep.iium.edu.my/78301/ http://irep.iium.edu.my/78301/1/78301-Prediction%20accuracy%20improvement%20for%20Botcoin%20market%20prices...%20-%20scopus.pdf http://irep.iium.edu.my/78301/2/78301-%20Prediction%20accuracy%20improvement%20for%20Bitcoin%20market%20prices.pdf
first_indexed	2023-09-18T21:50:20Z
last_indexed	2023-09-18T21:50:20Z
_version_	1777413726377345024

Prediction accuracy improvement for Bitcoin market prices based on symmetric volatility information using artificial neural network approach

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