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Information Extraction in Finance by M. Costantino,Paolo Coletti Pdf
Professional financial traders are currently overwhelmed with news and extracting relevant information is a long and hard task, whilst trading decisions require immediate actions. Primarily intended for financial organizations and business analysts, this book provides an introduction to the algorithmic solutions to automatically extract the desired information from Internet news and obtain it in a well structured form. It places emphasis on the principles of the method rather than its numerical implementation, omitting the mathematical details that might otherwise obscure the text, and focuses on the advantages and on the problems of each method. The authors also include many practical examples with complete references and algorithms for similar problems, which may be useful in the financial field, and basic techniques applied in other information extraction fields which may be imported into the financial news analysis.
Data Science for Economics and Finance by Sergio Consoli,Diego Reforgiato Recupero,Michaela Saisana Pdf
This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.
Handbook on Information Technology in Finance by Detlef Seese,Christof Weinhardt,Frank Schlottmann Pdf
This handbook contains surveys of state-of-the-art concepts, systems, applications, best practices as well as contemporary research in the intersection between IT and finance. Included are recent trends and challenges, IT systems and architectures in finance, essential developments and case studies on management information systems, and service oriented architecture modeling. The book shows a broad range of applications, e.g. in banking, insurance, trading and in non-financial companies. Essentially, all aspects of IT in finance are covered.
Expert Systems in Finance by Noura Metawa,Mohamed Elhoseny,Aboul Ella Hassanien,M. Kabir Hassan Pdf
Throughout the industry, financial institutions seek to eliminate cumbersome authentication methods, such as PINs, passwords, and security questions, as these antiquated tactics prove increasingly weak. Thus, many organizations now aim to implement emerging technologies in an effort to validate identities with greater certainty. The near instantaneous nature of online banking, purchases, transactions, and payments puts tremendous pressure on banks to secure their operations and procedures. In order to reduce the risk of human error in financial domains, expert systems are seen to offer a great advantage in big data environments. Besides their efficiency in quantitative analysis such as profitability, banking management, and strategic financial planning, expert systems have successfully treated qualitative issues including financial analysis, investment advisories, and knowledge-based decision support systems. Due to the increase in financial applications’ size, complexity, and number of components, it is no longer practical to anticipate and model all possible interactions and data processing in these applications using the traditional data processing model. The emergence of new research areas is clear evidence of the rise of new demands and requirements of modern real-life applications to be more intelligent. This book provides an exhaustive review of the roles of expert systems within the financial sector, with particular reference to big data environments. In addition, it offers a collection of high-quality research that addresses broad challenges in both theoretical and application aspects of intelligent and expert systems in finance. The book serves to aid the continued efforts of the application of intelligent systems that respond to the problem of big data processing in a smart banking and financial environment.
Building a Data Culture in the Ministry of Finance by Dody Dharma Hutabarat,Canrakerta,Lazuardi Zulfikar Wicaksana,Dimas Rahadian,Lysa Novita Sirait Pdf
The book is prepared as a general guide for stakeholders in the Ministry of Finance, especially the leaders, on how to lead their working units to be data-driven. In the Ministry of Finance, the volume of data grows massively. The data grow so rapidly that the Minister of Finance illustrates the condition by stating that “We, at the Ministry of Finance, are actually sitting on a large pile of data. This is a new type of mine. In digital era, the mine refers to the mine of data. However, of course they have to be the data we process and understand.” Ideally, the availability of data will encourage better formulation of policies and decision making. However, such effort is not an easy task, it is a challenging one instead. One of the main challenges in data utilization is that data culture has not been developed yet. The opportunity to optimize data utilization gets fresh air as awareness and understanding of data start to grow in some internal areas of the Ministry of Finance. Starting from the background, the book is compiled to become a guide for leaders and employees of the Ministry of Finance in building data culture in the Ministry of Finance. The book introduces cultural approach to develop and utilize data analytics skills in the Ministry of Finance. Hopefully, the book will keep being renewed in accordance with the development of science, technology, needs, and public discussion.
Machine Learning and Data Sciences for Financial Markets by Agostino Capponi,Charles-Albert Lehalle Pdf
Leveraging the research efforts of more than sixty experts in the area, this book reviews cutting-edge practices in machine learning for financial markets. Instead of seeing machine learning as a new field, the authors explore the connection between knowledge developed by quantitative finance over the past forty years and techniques generated by the current revolution driven by data sciences and artificial intelligence. The text is structured around three main areas: 'Interactions with investors and asset owners,' which covers robo-advisors and price formation; 'Risk intermediation,' which discusses derivative hedging, portfolio construction, and machine learning for dynamic optimization; and 'Connections with the real economy,' which explores nowcasting, alternative data, and ethics of algorithms. Accessible to a wide audience, this invaluable resource will allow practitioners to include machine learning driven techniques in their day-to-day quantitative practices, while students will build intuition and come to appreciate the technical tools and motivation for the theory.
Financial Decision Making Using Computational Intelligence by Michael Doumpos,Constantin Zopounidis,Panos M. Pardalos Pdf
The increasing complexity of financial problems and the enormous volume of financial data often make it difficult to apply traditional modeling and algorithmic procedures. In this context, the field of computational intelligence provides an arsenal of particularly useful techniques. These techniques include new modeling tools for decision making under risk and uncertainty, data mining techniques for analyzing complex data bases, and powerful algorithms for complex optimization problems. Computational intelligence has also evolved rapidly over the past few years and it is now one of the most active fields in operations research and computer science. This volume presents the recent advances of the use of computation intelligence in financial decision making. The book covers all the major areas of computational intelligence and a wide range of problems in finance, such as portfolio optimization, credit risk analysis, asset valuation, financial forecasting, and trading.
The Impact of Artificial Intelligence on Governance, Economics and Finance, Volume 2 by Sezer Bozkuş Kahyaoğlu Pdf
This book continues the discussion of the effects of artificial intelligence in terms of economics and finance. In particular, the book focuses on the effects of the change in the structure of financial markets, institutions and central banks, along with digitalization analyzed based on fintech ecosystems. In addition to finance sectors, other sectors, such as health, logistics, and industry 4.0, all of which are undergoing an artificial intelligence induced rapid transformation, are addressed in this book. Readers will receive an understanding of an integrated approach towards the use of artificial intelligence across various industries and disciplines with a vision to address the strategic issues and priorities in the dynamic business environment in order to facilitate decision-making processes. Economists, board members of central banks, bankers, financial analysts, regulatory authorities, accounting and finance professionals, chief executive officers, chief audit officers and chief financial officers, chief financial officers, as well as business and management academic researchers, will benefit from reading this book.
Business Information Systems by Witold Abramowicz,Robert Tolksdorf Pdf
This book contains the refereed proceedings of the 13th International Conference on Business Information Systems, BIS 2010, held in Berlin, Germany, in May 2010. The 25 revised full papers were carefully reviewed and selected from more than 80 submissions. Following the theme of the conference "Future Internet Business Services", the contributions detail recent research results and experiences and were grouped in eight sections on search and knowledge sharing, data and information security, Web experience modeling, business processes and rules, services and repositories, data mining for processes, visualization in business process management, and enterprise resource planning and supply chain management.
Detecting Regime Change in Computational Finance by Jun Chen,Edward P K Tsang Pdf
Based on interdisciplinary research into "Directional Change", a new data-driven approach to financial data analysis, Detecting Regime Change in Computational Finance: Data Science, Machine Learning and Algorithmic Trading applies machine learning to financial market monitoring and algorithmic trading. Directional Change is a new way of summarising price changes in the market. Instead of sampling prices at fixed intervals (such as daily closing in time series), it samples prices when the market changes direction ("zigzags"). By sampling data in a different way, this book lays out concepts which enable the extraction of information that other market participants may not be able to see. The book includes a Foreword by Richard Olsen and explores the following topics: Data science: as an alternative to time series, price movements in a market can be summarised as directional changes Machine learning for regime change detection: historical regime changes in a market can be discovered by a Hidden Markov Model Regime characterisation: normal and abnormal regimes in historical data can be characterised using indicators defined under Directional Change Market Monitoring: by using historical characteristics of normal and abnormal regimes, one can monitor the market to detect whether the market regime has changed Algorithmic trading: regime tracking information can help us to design trading algorithms It will be of great interest to researchers in computational finance, machine learning and data science. About the Authors Jun Chen received his PhD in computational finance from the Centre for Computational Finance and Economic Agents, University of Essex in 2019. Edward P K Tsang is an Emeritus Professor at the University of Essex, where he co-founded the Centre for Computational Finance and Economic Agents in 2002.
Pattern Recognition. ICPR International Workshops and Challenges by Alberto Del Bimbo,Rita Cucchiara,Stan Sclaroff,Giovanni Maria Farinella,Tao Mei,Marco Bertini,Hugo Jair Escalante,Roberto Vezzani Pdf
This 8-volumes set constitutes the refereed of the 25th International Conference on Pattern Recognition Workshops, ICPR 2020, held virtually in Milan, Italy and rescheduled to January 10 - 11, 2021 due to Covid-19 pandemic. The 416 full papers presented in these 8 volumes were carefully reviewed and selected from about 700 submissions. The 46 workshops cover a wide range of areas including machine learning, pattern analysis, healthcare, human behavior, environment, surveillance, forensics and biometrics, robotics and egovision, cultural heritage and document analysis, retrieval, and women at ICPR2020.
Proceedings of the 3rd International Conference on Internet, Education and Information Technology (IEIT 2023) by Dhananjay Kumar,Pavel Loskot,Qingliang Chen Pdf
This is an open access book. The 3rd International Conference on Internet, Education and Information Technology (IEIT 2023) was held on April 28–30, 2023 at the Xiamen, China. With the development of science and technology, information technology and information resources should be actively developed and fully applied in all fields of education and teaching, so as to promote the modernization of education and cultivate talents to meet the needs of society. From the technical point of view, the basic characteristics of educational informatization are digitalization, networking, intelligentization and multi-media. From the perspective of education, the basic characteristics of educational information are openness, sharing, interaction and cooperation. With the advantage of the network, it can provide students with a large amount of information and knowledge by combining different knowledge and information from various aspects in a high frequency. Therefore, we have intensified efforts to reform the traditional teaching methods and set up a new teaching concept, from the interaction between teachers and students in the past to the sharing between students. In short, it forms a sharing learning mode. For all students, strive to achieve students' learning independence, initiative and creativity. To sum up, we will provide a quick exchange platform between education and information technology, so that more scholars in related fields can share and exchange new ideas. The 3rd International Conference on Internet, Education and Information Technology (IEIT 2023) was held on April 28-30, 2023 in Xiamen, China. IEIT 2023 is to bring together innovative academics and industrial experts in the field of Internet, Education and Information Technology to a common forum. The primary goal of the conference is to promote research and developmental activities in Internet, Education and Information Technology and another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world. The conference will be held every year to make it an ideal platform for people to share views and experiences in international conference on Internet, Education and Information Technology and related areas.
Alpha Machines: Inside the AI-Driven Future of Finance by Gaurav Garg Pdf
The world of finance has been transformed by the emergence of artificial intelligence and machine learning. Advanced algorithms are now routinely applied across the industry for everything from high frequency trading to credit risk modeling. Yet despite its widespread impact, AI trading remains an often misunderstood field full of misconceptions. This book aims to serve as an accessible introduction and guide to the real-world practices, opportunities, and challenges associated with applying artificial intelligence to financial markets. Across different chapters, we explore major applications of AI in algorithmic trading, common technologies and techniques, practical implementation considerations, and case studies of successes and failures. Key topics covered include data analysis, feature engineering, major machine learning models, neural networks and deep learning, natural language processing, reinforcement learning, portfolio optimization, algorithmic trading strategies, backtesting methods, and risk management best practices when deploying AI trading systems. Each chapter provides sufficient technical detail for readers new to computer science and machine learning while emphasizing practical aspects relevant to practitioners. Code snippets and mathematical derivations illustrate key concepts. Significant attention is dedicated to real-world challenges, risks, regulatory constraints, and procedures required to operationalize AI in live trading. The goal is to provide readers with an accurate picture of current best practices that avoids overstating capabilities or ignoring pitfalls. Ethics and responsible AI development are highlighted given societal impacts. Ultimately this book aims to dispel myths, ground discussions in data-driven evidence, and present a balanced perspective on leveraging AI safely and effectively in trading. Whether an experienced practitioner looking to enhance trading strategies with machine learning or a curious student interested in exploring this intriguing field, readers across backgrounds will find an accessible synthesis of core topics and emerging developments in AI-powered finance. The book distills decades of research and industry lessons into a compact guide. Complimented by references for further reading, it serves as a valuable launchpad for readers seeking to gain a holistic understanding of this future-oriented domain at the nexus of computing and financial markets.
Information Extraction: A Multidisciplinary Approach to an Emerging Information Technology by Maria T. Pazienza Pdf
Information extraction (IE) is a new technology enabling relevant content to be extracted from textual information available electronically. IE essentially builds on natural language processing and computational linguistics, but it is also closely related to the well established area of information retrieval and involves learning. In concert with other promising and emerging information engineering technologies like data mining, intelligent data analysis, and text summarization, IE will play a crucial role for scientists and professionals as well as other end-users who have to deal with vast amounts of information, for example from the Internet. As the first book solely devoted to IE, it is of relevance to anybody interested in new and emerging trends in information processing technology.