Volatility autocorrelation in the stock market with artificial neural networks

Predicting the trend of financial features in complex financial systems is important and challenging, one useful tool is looking at the autocorrelation function, used in technical analysis as it shows how closely related a pattern reappears in the future. In this paper, we demonstrate a way to op...

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Main Author:	Tham, Zhi Rong
Other Authors:	Cheong Siew Ann
Format:	Final Year Project
Language:	English
Published:	Nanyang Technological University 2024
Subjects:	Physics Volatility clustering Artificial neural network
Online Access:	https://hdl.handle.net/10356/175690
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Institution:	Nanyang Technological University
Language:	English

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spelling	sg-ntu-dr.10356-1756902024-05-06T15:37:30Z Volatility autocorrelation in the stock market with artificial neural networks Tham, Zhi Rong Cheong Siew Ann School of Physical and Mathematical Sciences cheongsa@ntu.edu.sg Physics Volatility clustering Artificial neural network Predicting the trend of financial features in complex financial systems is important and challenging, one useful tool is looking at the autocorrelation function, used in technical analysis as it shows how closely related a pattern reappears in the future. In this paper, we demonstrate a way to optimise the autocorrelation of a linear combination of a stock’s volatility in prices and volumes, lagged at different times using regression neural networks. Bachelor's degree 2024-05-03T04:06:52Z 2024-05-03T04:06:52Z 2024 Final Year Project (FYP) Tham, Z. R. (2024). Volatility autocorrelation in the stock market with artificial neural networks. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175690 https://hdl.handle.net/10356/175690 en application/pdf Nanyang Technological University
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
language	English
topic	Physics Volatility clustering Artificial neural network
spellingShingle	Physics Volatility clustering Artificial neural network Tham, Zhi Rong Volatility autocorrelation in the stock market with artificial neural networks
description	Predicting the trend of financial features in complex financial systems is important and challenging, one useful tool is looking at the autocorrelation function, used in technical analysis as it shows how closely related a pattern reappears in the future. In this paper, we demonstrate a way to optimise the autocorrelation of a linear combination of a stock’s volatility in prices and volumes, lagged at different times using regression neural networks.
author2	Cheong Siew Ann
author_facet	Cheong Siew Ann Tham, Zhi Rong
format	Final Year Project
author	Tham, Zhi Rong
author_sort	Tham, Zhi Rong
title	Volatility autocorrelation in the stock market with artificial neural networks
title_short	Volatility autocorrelation in the stock market with artificial neural networks
title_full	Volatility autocorrelation in the stock market with artificial neural networks
title_fullStr	Volatility autocorrelation in the stock market with artificial neural networks
title_full_unstemmed	Volatility autocorrelation in the stock market with artificial neural networks
title_sort	volatility autocorrelation in the stock market with artificial neural networks
publisher	Nanyang Technological University
publishDate	2024
url	https://hdl.handle.net/10356/175690
_version_	1814047183566012416

Volatility autocorrelation in the stock market with artificial neural networks

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