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Finance and Large Language Models.pdf
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The integration of artificial intelligence (AI) agents and large language models (LLMs) is transforming the finance and trading sectors. These technologies enhance data analysis and decision-making by processing vast datasets with unparalleled speed and accuracy. AI agents identify patterns and predict market trends, while LLMs interpret unstructured data, providing deeper insights for trading strategies. This convergence improves trading efficiency and profitability, reshapes risk management and compliance, and personalizes financial services. As AI and LLMs evolve, they democratize access to advanced trading tools, benefiting individual traders and smaller institutions while driving innovation across the financial ecosystem. This book delves into the foundational principles and recent advancements of LLMs and their integration into financial systems and managerial environments. It explores how these models enhance decision-making, improve predictive accuracy, and streamline operations. Each chapter focuses on a specific application of LLMs in finance, offering insights, methodologies, and case studies that illustrate their transformative potential. LLMs are revolutionizing the financial industry by enhancing decision-making, predictive accuracy, and operational efficiency. Their capabilities include processing vast amounts of data, understanding complex financial concepts, and providing actionable insights. However, their integration also presents challenges, such as data privacy concerns, the need for significant computational resources, and ensuring model interpretability. One notable application of LLMs is LLM-based time series analysis and regime detection, enhanced by Retrieval-Augmented Generation (RAG), contributing to adaptive trading strategies by enabling machines to better understand market contexts, conditions, and the implications of political and economic events. Fine-tuned, open-source LLMs can also enhance quantitative trading strategies by integrating numerical and textual data through techniques such as Low-Rank Adaptation (LoRA) and RAG. Another important application is in housing price appraisal, where models like ChatGPT demonstrate impressive reasoning capabilities and accuracy in real estate valuation. These advancements enable sophisticated and adaptive trading strategies, optimizing portfolio management LLMs also play a vital role in analyzing voluntary sustainability disclosures, assessing the impact of third-party assurance on corporate transparency, and examining the relationship between verbal masculinity in corporate communications and CEO compensation. Empirical evidence from India highlights factors influencing AI adoption in finance, while the intersection of federated learning and blockchain technology offers innovative solutions for collaborative AI model training. Finally, AI agents and deep learning algorithms are revolutionizing automated trading, driving the development of efficient market strategies. This book is tailored for researchers, financial professionals, and enthusiasts eager to understand the transformative impact of LLMs on the financial industry and managerial decision-making. Through detailed explanations, practical examples, and forward-looking insights, readers will gain the knowledge and tools to harness the power of LLMs in their financial pursuits.
Large Language Models in Finance: An Overview .................... 1
Paul Moon Sub Choi, Seth H. Huang, and Qishu Wang
Housing Price Estimation and Reasoning Based on a Large
Language Model .................................................. 27
Seongeun Bae, Leehyun Jung, Sukyung Nam, Sihyun An,
and Kwangwon Ahn
Advancing Quantitative Trading Strategies Using Fine-Tuned
Open-Source Large Language Models: A Hybrid Approach
with Numerical and Textual Data Integration Using RAG
and LoRA Techniques ............................................. 43
Seth H. Huang, Jimin Kim, and Ka Lok Kellogg Wong
Foundations of LLMs and Financial Applications .................... 59
Yoonseo Chung, Jeonghyun Kim, MiYeon Kim, Minsuh Joo,
and Hyunsoo Cho
Voluntary Sustainability Disclosure and Third-Party Assurance:
A Large Language Model Perspective ............................... 91
SoHyeon Kang and Sewon Kwon
Verbal Femininity and CEOs Compensation ......................... 111
Sang-Joon Kim and Juil Lee
Integrating LLM-Based Time Series and Regime Detection
with RAG for Adaptive Trading Strategies and Portfolio
Management ...................................................... 129
Chenkai Li, Chi Ho Roger Chan, Seth H. Huang,
and Paul Moon Sub Choi
Empirical Factor Identification for Artificial Intelligence
in Finance: Indian Evidence ........................................ 147
Large Language Models in Finance: An Overview .................... 1
Paul Moon Sub Choi, Seth H. Huang, and Qishu Wang
Housing Price Estimation and Reasoning Based on a Large
Language Model .................................................. 27
Seongeun Bae, Leehyun Jung, Sukyung Nam, Sihyun An,
and Kwangwon Ahn
Advancing Quantitative Trading Strategies Using Fine-Tuned
Open-Source Large Language Models: A Hybrid Approach
with Numerical and Textual Data Integration Using RAG
and LoRA Techniques ............................................. 43
Seth H. Huang, Jimin Kim, and Ka Lok Kellogg Wong
Foundations of LLMs and Financial Applications .................... 59
Yoonseo Chung, Jeonghyun Kim, MiYeon Kim, Minsuh Joo,
and Hyunsoo Cho
Voluntary Sustainability Disclosure and Third-Party Assurance:
A Large Language Model Perspective ............................... 91
SoHyeon Kang and Sewon Kwon
Verbal Femininity and CEOs Compensation ......................... 111
Sang-Joon Kim and Juil Lee
Integrating LLM-Based Time Series and Regime Detection
with RAG for Adaptive Trading Strategies and Portfolio
Management ...................................................... 129
Chenkai Li, Chi Ho Roger Chan, Seth H. Huang,
and Paul Moon Sub Choi
Empirical Factor Identification for Artificial Intelligence
in Finance: Indian Evidence ........................................ 147
Rohit Kaushik
Federated and Decentralized Finance: Decentralized Reward
Mechanisms for Advanced AI Learning ............................. 157
Hyoseok Jang, Sangchul Lee, Haneol Cho, and Chansoo Kim
AI-Driven Financial Chart Analysis with Benchmarks:
A Domain-Specific Large Language Model Approach ................. 173
Hyoseok Jang, Sangchul Lee, Haneol Cho, and Chansoo Kim
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