Finance and Large Language Models / / edited by Paul Moon Sub Choi, Seth H. Huang

• Record Nr.: UNINA9911022159703321
• Autore: Choi Paul Moon Sub
• Pubbl/distr/stampa: Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025
• ISBN: 981-9658-33-0
• Edizione: [1st ed. 2025.]
• Descrizione fisica: 1 online resource (245 pages)
• Collana: Blockchain Technologies, , 2661-8346
• Altri autori (Persone): HuangSeth H
• Disciplina: 005.824; 005.74
• Soggetti: Blockchains (Databases); Financial engineering; Financial risk management; Artificial intelligence; Blockchain; Financial Technology and Innovation; Risk Management; Artificial Intelligence
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: 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.
• Sommario/riassunto: 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.