Artificial Intelligence Applications in Financial Technology, 2nd Edition

Topic Information

Dear Colleagues,

This is the second edition of the previous successful Topic “Artificial Intelligence Applications in Financial Technology”. Financial technology (fintech) refers to the use of information technology to simplify, improve, reshape, and automate financial processes and services for businesses and customers. In the financial world, many processes and services rely heavily on humans, resulting in mistakes, inefficiency, compliance issues, and penalty fines. They may involve document handling and communications between agents and customers, supervisors and subordinates, and institutions and regulators. Fintech allows various financial institutions to manipulate many of these processes and services with electronic devices, which can work 24/7 at the same standard more efficiently. In particular, artificial intelligence (AI) equips machines with human cognitive skills so that certain tasks can now be automated, especially in relation to image, natural language, and speech. For example, we can covert handwritten documents or printouts into electronic formats for further analysis. Natural language processing facilitates the extraction of useful information in a piece of text, and speech recognition allows us to analyze a conversation. Fintech has become an essential tool to the global BFSI (banking, financial services, and insurance) industry, and it has branched out into specific disciplines, e.g., regtech for the management of regulatory processes, suptech for regulatory supervision and oversight, and insurtech for new insurance product and solution designs. This Topic therefore seeks to contribute to the agenda of AI applications in fintech through enhanced scientific and multidisciplinary knowledge to improve performance and deployment by bringing focus to various AI technologies suitable for BFSI in order to meet technical, social, and economic goals. We are particularly interested in investigating how AI technologies contribute to the financial industry and vice versa. We therefore invite you to submit papers on innovative technical developments, reviews, and analytical as well as assessment papers from different disciplines which are relevant to the integration of AI and fintech. Topics of interest for publication include, but are not limited to, the following:

• Chatbots in fintech;
• Natural language processing;
• Speech cognition and synthesis;
• Image recognition;
• AI-powered personalized banking;
• Complex system applications (including ESG);
• User behavior analysis;
• Fraud detection;
• Anti-money laundering;
• Consistent customer services;
• Cryptocurrency;
• Cybersecurity.

Prof. Dr. Albert Y.S. Lam
Prof. Dr. Andy Chun
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	3.1	7.2	2020	18.9 Days	CHF 1600	Submit
Big Data and Cognitive Computing BDCC	3.7	7.1	2017	25.3 Days	CHF 1800	Submit
FinTech fintech	-	-	2022	20.4 Days	CHF 1000	Submit
International Journal of Financial Studies ijfs	2.1	3.7	2013	24.8 Days	CHF 1800	Submit
Journal of Theoretical and Applied Electronic Commerce Research jtaer	5.1	9.5	2006	34 Days	CHF 1000	Submit
Risks risks	2.0	3.8	2013	20.5 Days	CHF 1800	Submit

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27 pages, 4436 KiB  

Open AccessArticle

Leveraging Large Language Models for Sentiment Analysis and Investment Strategy Development in Financial Markets

by Yejoon Mun and Namhyoung Kim

J. Theor. Appl. Electron. Commer. Res. 2025, 20(2), 77; https://doi.org/10.3390/jtaer20020077 - 20 Apr 2025

Viewed by 1138

Abstract

This study investigates the application of large language models (LLMs) in sentiment analysis of financial news and their use in developing effective investment strategies. We conducted sentiment analysis on news articles related to the top 30 companies listed on Nasdaq using both discriminative [...] Read more.

This study investigates the application of large language models (LLMs) in sentiment analysis of financial news and their use in developing effective investment strategies. We conducted sentiment analysis on news articles related to the top 30 companies listed on Nasdaq using both discriminative models such as BERT and FinBERT, and generative models including Llama 3.1, Mistral, and Gemma 2. To enhance the robustness of the analysis, advanced prompting techniques—such as Chain of Thought (CoT), Super In-Context Learning (SuperICL), and Bootstrapping—were applied to generative LLMs. The results demonstrate that long strategies generally yield superior portfolio performance compared to short and long–short strategies. Notably, generative LLMs outperformed discriminative models in this context. We also found that the application of SuperICL to generative LLMs led to significant performance improvements, with further enhancements noted when both SuperICL and Bootstrapping were applied together. These findings highlight the profitability and stability of the proposed approach. Additionally, this study examines the explainability of LLMs by identifying critical data considerations and potential risks associated with their use. The research highlights the potential of integrating LLMs into financial strategy development to provide a data-driven foundation for informed decision-making in financial markets. Full article

(This article belongs to the Topic Artificial Intelligence Applications in Financial Technology, 2nd Edition)

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