Finance and Large Language Models
Description
This book highlights how AI agents and Large Language Models (LLMs) are set to revolutionize the finance and trading sectors in unprecedented ways. These technologies bring a new level of sophistication to data analysis and decision-making, enabling real-time processing of vast and complex datasets with unparalleled accuracy and speed. AI agents, equipped with advanced machine learning algorithms, can identify patterns and predict market trends with a level of precision that may soon surpass human capabilities. LLMs, on the other hand, facilitate the interpretation and synthesis of unstructured data, such as financial news, reports, and social media sentiments, providing deeper insights and more informed trading strategies. This convergence of AI and LLM technology not only enhances the efficiency and profitability of trading operations but also introduces a paradigm shift in risk management, compliance, and personalized financial services. As these technologies continue to evolve, they promise to democratize access to sophisticated trading tools and insights, leveling the playing field for individual traders and smaller financial institutions while driving innovation and growth across the entire financial ecosystem.
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Dr. Paul Moon Sub Choi has served on the business faculty of Ewha (Professor of Finance and Associate Dean), Cornell (Fulbright Visiting Scholar), and the State University of New York at Binghamton (lecturer). He earned a Ph.D. with a financial economics concentration and an A.M. in statistics from Cornell and Harvard Universities, respectively. He was an undergraduate economics major at Yonsei University before joining Deutsche Bank in equity research on the Korean technology industry. He has published numerous peer-reviewed research articles in leading journals and presented papers in some of the most prestigious conferences in finance and economics. His recent research areas include distributed ledger technology (blockchain), artificial intelligence, financial technology, etc. He is an advisor at various technology-based startups, including GoChapaa, Lozi, OrganicSmart, etc.
Dr. Seth H. Huang has over fifteen years of expertise in artificial intelligence (AI) research and financial management. He has developed large-scale fintech applications focusing on hedging, global sentiment analysis, and risk management. Currently, Dr. Huang serves as the Chief Investment Officer at Microbit Capital. Previously, he was the Director of the AI Applications Research Center at Huawei Technology. In academia, Dr. Huang has held positions such as Adjunct Associate Professor of Finance at the Hong Kong University of Science and Technology, Ewha Global Fellow at Ewha Womans University, Director at the Shanghai Advanced Institute of Finance under Shanghai Jiao Tong University, and Assistant Professor of Finance at Ewha Womans University. His academic contributions include designing and teaching courses in fintech, AI, and finance. Dr. Huang holds six patents on AI predictive systems and founded Aris Capital, a quantitative trading operation based in New York. He earned his Ph.D. in financial economics from Cornell University.
Content
Large Language Models in Finance: An Overview.- Housing price estimation and reasoning based on a large language model.- 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.- Foundations of LLMs and Financial Applications.- Voluntary Sustainability Disclosure and Third Party Assurance: A Large Language Model Perspective.- Verbal Femininity and CEOs Compensation.- Integrating LLM-Based Time Series and Regime Detection with RAG for Adaptive Trading Strategies and Portfolio Management.- Empirical Factor Identification for Artificial Intelligence in Finance: Indian Evidence.- Large Language Models in Personal Finance: Cost-Effectiveness and Quality Compared to Human Experts.- Automated Trading Techniques with AI Agents: Deep Learning Algorithms for Efficient Market Strategies.