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            <title>Proceedings of the Joint Workshop of the 9th Financial Technology and Natural Language Processing (FinNLP), the 6th Financial Narrative Processing (FNP), and the 1st Workshop on Large Language Models for Finance and Legal (LLMFinLegal)</title>
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            <namePart type="given">Chung-Chi</namePart>
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    <abstract>In this paper, we address the Agent-Based Sin- gle Cryptocurrency Trading Challenge, focus- ing on decision-making for trading Bitcoin and Etherium. Our approach utilizes fine- tuning a Mistral AI model on a dataset com- prising summarized cryptocurrency news, en- abling it to make informed “buy,” “sell,” or “hold” decisions and articulate its reasoning. The model integrates textual sentiment analysis and contextual reasoning with real-time mar- ket trends, demonstrating the potential of Large Language Models (LLMs) in high-stakes finan- cial decision-making. The model achieved a notable accuracy, highlighting its capacity to manage risk while optimizing returns. This work contributes to advancing AI-driven so- lutions for cryptocurrency markets and offers insights into the practical deployment of LLMs in real-time trading environments. We made our model publicly available.</abstract>
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%0 Conference Proceedings
%T 300k/ns team at the Crypto Trading Challenge Task: Enhancing the justification of accurate trading decisions through parameter-efficient fine-tuning of reasoning models
%A Agarkov, Artem
%A Kulik, Mihail
%A Shmyrkov, Leonid
%Y Chen, Chung-Chi
%Y Moreno-Sandoval, Antonio
%Y Huang, Jimin
%Y Xie, Qianqian
%Y Ananiadou, Sophia
%Y Chen, Hsin-Hsi
%S Proceedings of the Joint Workshop of the 9th Financial Technology and Natural Language Processing (FinNLP), the 6th Financial Narrative Processing (FNP), and the 1st Workshop on Large Language Models for Finance and Legal (LLMFinLegal)
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F agarkov-etal-2025-300k
%X In this paper, we address the Agent-Based Sin- gle Cryptocurrency Trading Challenge, focus- ing on decision-making for trading Bitcoin and Etherium. Our approach utilizes fine- tuning a Mistral AI model on a dataset com- prising summarized cryptocurrency news, en- abling it to make informed “buy,” “sell,” or “hold” decisions and articulate its reasoning. The model integrates textual sentiment analysis and contextual reasoning with real-time mar- ket trends, demonstrating the potential of Large Language Models (LLMs) in high-stakes finan- cial decision-making. The model achieved a notable accuracy, highlighting its capacity to manage risk while optimizing returns. This work contributes to advancing AI-driven so- lutions for cryptocurrency markets and offers insights into the practical deployment of LLMs in real-time trading environments. We made our model publicly available.
%U https://aclanthology.org/2025.finnlp-1.48/
%P 414-422

Markdown (Informal)

[300k/ns team at the Crypto Trading Challenge Task: Enhancing the justification of accurate trading decisions through parameter-efficient fine-tuning of reasoning models](https://aclanthology.org/2025.finnlp-1.48/) (Agarkov et al., FinNLP 2025)

• 300k/ns team at the Crypto Trading Challenge Task: Enhancing the justification of accurate trading decisions through parameter-efficient fine-tuning of reasoning models (Agarkov et al., FinNLP 2025)

ACL

• Artem Agarkov, Mihail Kulik, and Leonid Shmyrkov. 2025. 300k/ns team at the Crypto Trading Challenge Task: Enhancing the justification of accurate trading decisions through parameter-efficient fine-tuning of reasoning models. In Proceedings of the Joint Workshop of the 9th Financial Technology and Natural Language Processing (FinNLP), the 6th Financial Narrative Processing (FNP), and the 1st Workshop on Large Language Models for Finance and Legal (LLMFinLegal), pages 414–422, Abu Dhabi, UAE. Association for Computational Linguistics.