Harnessing the Power of AI in Investigating Trend of Lithium-ion Batteries Related Stock Market to Support the Sustainable Clean Energy Future

Kianrad, Ahmad

Harnessing the Power of AI in Investigating Trend of Lithium-ion Batteries Related Stock Market to Support the Sustainable Clean Energy Future

Kianrad, Ahmad

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Publication Type:: Thesis
Issue Date:: 2025

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Field	Value	Language
dc.contributor.author	Kianrad, Ahmad
dc.date.accessioned	2026-05-11T01:36:29Z
dc.date.available	2026-05-11T01:36:29Z
dc.date.issued	2025
dc.identifier.uri	http://hdl.handle.net/10453/194924
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	This thesis presents a comprehensive investigation into the factors influencing the stock market performance of lithium-related companies, with a particular focus on firms operating in Australia. Recognising the increasing significance of lithium in the global transition to clean energy and the dynamic nature of its market, this study explores how machine learning models can be leveraged to assess the impact of historical stock trends, formal corporate announcements, and social media content on stock price fluctuations. This research demonstrates the value of using machine learning techniques to support data-driven decision-making in the lithium-related stock market. By integrating structured financial data, formal corporate disclosures, and unstructured social media content, the study provides a holistic framework for stock price analysis and investment analysis. Furthermore, the methodologies employed show strong applicability across other sectors and markets, offering a replicable approach for future research. This thesis contributes to the fields of Information Systems and Computer Science by developing a multi-source, machine learning–driven framework for analysing stock price movements in the lithium sector. Through the integration of structured historical data, formal corporate disclosures, and unstructured social media content, the study demonstrates how advanced computational techniques can support intelligent information processing and decision-making in volatile, data-rich environments. Moreover, this thesis highlights the role of corporate communications as formal information flows within socio-technical systems, providing new insights into how disclosure mechanisms and digital signals affect perception and valuation. The findings support the development of intelligent systems that combine internal and external data streams for adaptive forecasting and strategic decision-making. This thesis contributes to Information Systems and Computer Science fields by advancing techniques in data-driven modelling, demonstrating cross-domain machine learning applications, and offering a replicable framework for integrating heterogeneous data sources to support market understanding and system behaviour analysis in complex, evolving domains.	en_US.UTF-8
dc.format	Thesis (PhD)
dc.language.iso	en_US	en_US.UTF-8
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/194924/1/thesis.pdf
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	© 2025 Ahmad Kianrad
dc.rights	au.edu.uts.lib/cph
dc.title	Harnessing the Power of AI in Investigating Trend of Lithium-ion Batteries Related Stock Market to Support the Sustainable Clean Energy Future	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

This thesis presents a comprehensive investigation into the factors influencing the stock market performance of lithium-related companies, with a particular focus on firms operating in Australia. Recognising the increasing significance of lithium in the global transition to clean energy and the dynamic nature of its market, this study explores how machine learning models can be leveraged to assess the impact of historical stock trends, formal corporate announcements, and social media content on stock price fluctuations. This research demonstrates the value of using machine learning techniques to support data-driven decision-making in the lithium-related stock market. By integrating structured financial data, formal corporate disclosures, and unstructured social media content, the study provides a holistic framework for stock price analysis and investment analysis. Furthermore, the methodologies employed show strong applicability across other sectors and markets, offering a replicable approach for future research. This thesis contributes to the fields of Information Systems and Computer Science by developing a multi-source, machine learning–driven framework for analysing stock price movements in the lithium sector. Through the integration of structured historical data, formal corporate disclosures, and unstructured social media content, the study demonstrates how advanced computational techniques can support intelligent information processing and decision-making in volatile, data-rich environments. Moreover, this thesis highlights the role of corporate communications as formal information flows within socio-technical systems, providing new insights into how disclosure mechanisms and digital signals affect perception and valuation. The findings support the development of intelligent systems that combine internal and external data streams for adaptive forecasting and strategic decision-making. This thesis contributes to Information Systems and Computer Science fields by advancing techniques in data-driven modelling, demonstrating cross-domain machine learning applications, and offering a replicable framework for integrating heterogeneous data sources to support market understanding and system behaviour analysis in complex, evolving domains.

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http://hdl.handle.net/10453/194924