Financial Statement Analysis With Large Language Models
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Financial Information Analysis
Accounting information is one of the most vital and important sources of information in the modern world. Those who understand this and can analyse its context and content have the power to influence key decision-making processes. Philip O’Regan’s authoritative and important textbook provides readers with the tools and techniques to analyse accounting information and present it in a manner that is clear, focused and valuable. Updated to reflect changes in IFRS, governance codes and regulatory frameworks, as well as new sustainability reporting rules, the text sheds light on the role of accounting information in modern society and the many ways in which it can be used by a variety of stakeholders, including shareholders, investors, employees, environmentalists and others. For readers across the UK, Ireland and continental European, this new edition is completely updated to incorporate relevant accounting standards, sustainability/ESG regulations and advanced analytical techniques. Features which add unique value to this fourth edition of Financial Information Analysis include case studies in every chapter with numerous supporting articles from the major financial presses, questions for review, and comprehensive online support and materials. This essential textbook is core reading for advanced undergraduate and postgraduate students wishing to understand the role of accounting information in modern society.
Machine Learning and Modeling Techniques in Financial Data Science
The integration of machine learning and modeling in finance is transforming how data is analyzed, enabling more accurate predictions, risk assessments, and strategic planning. These advanced techniques empower financial professionals to uncover hidden patterns, automate complex processes, and enhance decision-making in volatile markets. As industries increasingly rely on data-driven insights, the adoption of these tools contributes to greater efficiency, reduced uncertainty, and competitive advantage. This technological shift not only drives innovation within financial sectors but also supports broader economic stability and growth by improving forecasting and mitigating risks. Machine Learning and Modeling Techniques in Financial Data Science provides an updated review and highlights recent theoretical advances and breakthroughs in professional practices within financial data science, exploring the strategic roles of machine learning and modeling techniques across various domains in finance. It offers a comprehensive collection that brings together a wealth of knowledge and experience. Covering topics such as algorithmic trading, financial technology (FinTech), and natural language processing (NLP), this book is an excellent resource for business professionals, leaders, policymakers, researchers, academicians, and more.
Document Analysis and Recognition - ICDAR 2023
This six-volume set of LNCS 14187, 14188, 14189, 14190, 14191 and 14192 constitutes the refereed proceedings of the 17th International Conference on Document Analysis and Recognition, ICDAR 2021, held in San José, CA, USA, in August 2023. The 53 full papers were carefully reviewed and selected from 316 submissions, and are presented with 101 poster presentations. The papers are organized into the following topical sections: Graphics Recognition, Frontiers in Handwriting Recognition, Document Analysis and Recognition.