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| financial news sentiment analysis: Data Science for Economics and Finance Sergio Consoli, Diego Reforgiato Recupero, Michaela Saisana, 2021 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. |
| financial news sentiment analysis: Pattern Recognition and Machine Intelligence Bhabesh Deka, Pradipta Maji, Sushmita Mitra, Dhruba Kumar Bhattacharyya, Prabin Kumar Bora, Sankar Kumar Pal, 2019-11-25 The two-volume set of LNCS 11941 and 11942 constitutes the refereed proceedings of the 8th International Conference on Pattern Recognition and Machine Intelligence, PReMI 2019, held in Tezpur, India, in December 2019. The 131 revised full papers presented were carefully reviewed and selected from 341 submissions. They are organized in topical sections named: Pattern Recognition; Machine Learning; Deep Learning; Soft and Evolutionary Computing; Image Processing; Medical Image Processing; Bioinformatics and Biomedical Signal Processing; Information Retrieval; Remote Sensing; Signal and Video Processing; and Smart and Intelligent Sensors. |
| financial news sentiment analysis: Economic News, Sentiment, and Behavior Juliane A. Lischka, 2015-11-04 This book explores the relations between objective, media-related, and social attitudinal as well as behavioral realities of private, expert, and corporate agents in the traditions of mass communication, journalism studies and behavioral economics. Results based on time series analyses for German data show that the news reports in a volatile manner on the economy and may influence its development through third-person effects. Bad economic news does not cause a decrease in private purchase intentions. Bad news may lead to a change in corporate decisions, such as advertising expenditures, because corporate decision makers may presume changes in consumer behavior through news. |
| financial news sentiment analysis: The Handbook of News Analytics in Finance Gautam Mitra, Leela Mitra, 2011-07-13 The Handbook of News Analytics in Finance is a landmarkpublication bringing together the latest models and applications ofNews Analytics for asset pricing, portfolio construction, tradingand risk control. The content of the Hand Book is organised to provide arapid yet comprehensive understanding of this topic. Chapter 1 setsout an overview of News Analytics (NA) with an explanation of thetechnology and applications. The rest of the chapters are presentedin four parts. Part 1 contains an explanation of methods and modelswhich are used to measure and quantify news sentiment. In Part 2the relationship between news events and discovery of abnormalreturns (the elusive alpha) is discussed in detail by the leadingresearchers and industry experts. The material in this part alsocovers potential application of NA to trading and fund management.Part 3 covers the use of quantified news for the purpose ofmonitoring, early diagnostics and risk control. Part 4 is entirelyindustry focused; it contains insights of experts from leadingtechnology (content) vendors. It also contains a discussion oftechnologies and finally a compact directory of content vendor andfinancial analytics companies in the marketplace of NA. Thebook draws equally upon the expertise of academics andpractitioners who have developed these models and is supported bytwo major content vendors - RavenPack and Thomson Reuters - leadingproviders of news analytics software and machine readablenews. The book will appeal to decision makers in the banking, finance andinsurance services industry. In particular: asset managers;quantitative fund managers; hedge fund managers; algorithmictraders; proprietary (program) trading desks; sell-side firms;brokerage houses; risk managers and research departments willbenefit from the unique insights into this new and pertinent areaof financial modelling. |
| financial news sentiment analysis: Deep Learning-Based Approaches for Sentiment Analysis Basant Agarwal, Richi Nayak, Namita Mittal, Srikanta Patnaik, 2020-01-24 This book covers deep-learning-based approaches for sentiment analysis, a relatively new, but fast-growing research area, which has significantly changed in the past few years. The book presents a collection of state-of-the-art approaches, focusing on the best-performing, cutting-edge solutions for the most common and difficult challenges faced in sentiment analysis research. Providing detailed explanations of the methodologies, the book is a valuable resource for researchers as well as newcomers to the field. |
| financial news sentiment analysis: Computing Attitude and Affect in Text: Theory and Applications James G. Shanahan, Yan Qu, Janyce Wiebe, 2006-01-17 Human Language Technology (HLT) and Natural Language Processing (NLP) systems have typically focused on the “factual” aspect of content analysis. Other aspects, including pragmatics, opinion, and style, have received much less attention. However, to achieve an adequate understanding of a text, these aspects cannot be ignored. The chapters in this book address the aspect of subjective opinion, which includes identifying different points of view, identifying different emotive dimensions, and classifying text by opinion. Various conceptual models and computational methods are presented. The models explored in this book include the following: distinguishing attitudes from simple factual assertions; distinguishing between the author’s reports from reports of other people’s opinions; and distinguishing between explicitly and implicitly stated attitudes. In addition, many applications are described that promise to benefit from the ability to understand attitudes and affect, including indexing and retrieval of documents by opinion; automatic question answering about opinions; analysis of sentiment in the media and in discussion groups about consumer products, political issues, etc. ; brand and reputation management; discovering and predicting consumer and voting trends; analyzing client discourse in therapy and counseling; determining relations between scientific texts by finding reasons for citations; generating more appropriate texts and making agents more believable; and creating writers’ aids. The studies reported here are carried out on different languages such as English, French, Japanese, and Portuguese. Difficult challenges remain, however. It can be argued that analyzing attitude and affect in text is an “NLP”-complete problem. |
| financial news sentiment analysis: Handbook of Sentiment Analysis in Finance Gautam Mitra, Yu Xiang, 2016 |
| financial news sentiment analysis: MarketPsych Richard L. Peterson, Frank F. Murtha, 2010-07-30 An investor's guide to understanding the most elusive (yet most important) aspect of successful investing - yourself. Why is it that the investing performance of so many smart people reliably and predictably falls short? The answer is not that they know too little about the markets. In fact, they know too little about themselves. Combining the latest findings from the academic fields of behavioral finance and experimental psychology with the down-and-dirty real-world wisdom of successful investors, Drs. Richard Peterson and Frank Murtha guide both new and experienced investors through the psychological learning process necessary to achieve their financial goals. In an easy and entertaining style that masks the book’s scientific rigor, the authors make complex scientific insights readily understandable and actionable, shattering a number of investing myths along the way. You will gain understanding of your true investing motivations, learn to avoid the unseen forces that subvert your performance, and build your investor identity - the foundation for long-lasting investing success. Replete with humorous games, insightful self-assessments, entertaining exercises, and concrete planning tools, this book goes beyond mere education. MarketPsych: How to Manage Fear and Build Your Investor Identity functions as a psychological outfitter for your unique investing journey, providing the tools, training and equipment to help you navigate the right paths, stay on them, and see your journey through to success. |
| financial news sentiment analysis: Advanced Data Mining and Applications Shuigeng Zhou, Songmao Zhang, George Karypis, 2012-12-09 This book constitutes the refereed proceedings of the 8th International Conference on Advanced Data Mining and Applications, ADMA 2012, held in Nanjing, China, in December 2012. The 32 regular papers and 32 short papers presented in this volume were carefully reviewed and selected from 168 submissions. They are organized in topical sections named: social media mining; clustering; machine learning: algorithms and applications; classification; prediction, regression and recognition; optimization and approximation; mining time series and streaming data; Web mining and semantic analysis; data mining applications; search and retrieval; information recommendation and hiding; outlier detection; topic modeling; and data cube computing. |
| financial news sentiment analysis: Affective Computing and Sentiment Analysis Khurshid Ahmad, 2011-08-24 This volume maps the watershed areas between two 'holy grails' of computer science: the identification and interpretation of affect – including sentiment and mood. The expression of sentiment and mood involves the use of metaphors, especially in emotive situations. Affect computing is rooted in hermeneutics, philosophy, political science and sociology, and is now a key area of research in computer science. The 24/7 news sites and blogs facilitate the expression and shaping of opinion locally and globally. Sentiment analysis, based on text and data mining, is being used in the looking at news and blogs for purposes as diverse as: brand management, film reviews, financial market analysis and prediction, homeland security. There are systems that learn how sentiments are articulated. This work draws on, and informs, research in fields as varied as artificial intelligence, especially reasoning and machine learning, corpus-based information extraction, linguistics, and psychology. |
| financial news sentiment analysis: Trading on Sentiment Richard L. Peterson, 2016-03-21 In his debut book on trading psychology, Inside the Investor’s Brain, Richard Peterson demonstrated how managing emotions helps top investors outperform. Now, in Trading on Sentiment, he takes you inside the science of crowd psychology and demonstrates that not only do price patterns exist, but the most predictable ones are rooted in our shared human nature. Peterson’s team developed text analysis engines to mine data - topics, beliefs, and emotions - from social media. Based on that data, they put together a market-neutral social media-based hedge fund that beat the S&P 500 by more than twenty-four percent—through the 2008 financial crisis. In this groundbreaking guide, he shows you how they did it and why it worked. Applying algorithms to social media data opened up an unprecedented world of insight into the elusive patterns of investor sentiment driving repeating market moves. Inside, you gain a privileged look at the media content that moves investors, along with time-tested techniques to make the smart moves—even when it doesn’t feel right. This book digs underneath technicals and fundamentals to explain the primary mover of market prices - the global information flow and how investors react to it. It provides the expert guidance you need to develop a competitive edge, manage risk, and overcome our sometimes-flawed human nature. Learn how traders are using sentiment analysis and statistical tools to extract value from media data in order to: Foresee important price moves using an understanding of how investors process news. Make more profitable investment decisions by identifying when prices are trending, when trends are turning, and when sharp market moves are likely to reverse. Use media sentiment to improve value and momentum investing returns. Avoid the pitfalls of unique price patterns found in commodities, currencies, and during speculative bubbles Trading on Sentiment deepens your understanding of markets and supplies you with the tools and techniques to beat global markets— whether they’re going up, down, or sideways. |
| financial news sentiment analysis: Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data Andreas Holzinger, Gabriella Pasi, 2013-06-19 This book constitutes the refereed proceedings of the Third Workshop on Human-Computer Interaction and Knowledge Discovery, HCI-KDD 2013, held in Maribor, Slovenia, in July 2013, at SouthCHI 2013. The 20 revised papers presented were carefully reviewed and selected from 68 submissions. The papers are organized in topical sections on human-computer interaction and knowledge discovery, knowledge discovery and smart homes, smart learning environments, and visualization data analytics. |
| financial news sentiment analysis: Support Vector Machines Naiyang Deng, Yingjie Tian, Chunhua Zhang, 2012-12-17 Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions presents an accessible treatment of the two main components of support vector machines (SVMs)-classification problems and regression problems. The book emphasizes the close connection between optimization theory and SVMs since optimization is one of the pillars on which |
| financial news sentiment analysis: Sentiment Analysis and Ontology Engineering Witold Pedrycz, Shyi-Ming Chen, 2016-03-22 This edited volume provides the reader with a fully updated, in-depth treatise on the emerging principles, conceptual underpinnings, algorithms and practice of Computational Intelligence in the realization of concepts and implementation of models of sentiment analysis and ontology –oriented engineering. The volume involves studies devoted to key issues of sentiment analysis, sentiment models, and ontology engineering. The book is structured into three main parts. The first part offers a comprehensive and prudently structured exposure to the fundamentals of sentiment analysis and natural language processing. The second part consists of studies devoted to the concepts, methodologies, and algorithmic developments elaborating on fuzzy linguistic aggregation to emotion analysis, carrying out interpretability of computational sentiment models, emotion classification, sentiment-oriented information retrieval, a methodology of adaptive dynamics in knowledge acquisition. The third part includes a plethora of applications showing how sentiment analysis and ontologies becomes successfully applied to investment strategies, customer experience management, disaster relief, monitoring in social media, customer review rating prediction, and ontology learning. This book is aimed at a broad audience of researchers and practitioners. Readers involved in intelligent systems, data analysis, Internet engineering, Computational Intelligence, and knowledge-based systems will benefit from the exposure to the subject matter. The book may also serve as a highly useful reference material for graduate students and senior undergraduate students. |
| financial news sentiment analysis: 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC) IEEE Staff, 2021-10-17 An international forum for researchers, educators and practitioners to learn, share knowledge, report most recent innovations and developments, and to exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics |
| financial news sentiment analysis: Intelligent Sustainable Systems Atulya K. Nagar, Dharm Singh Jat, Gabriela Marín-Raventós, Durgesh Kumar Mishra, 2021-12-16 This book provides insights of World Conference on Smart Trends in Systems, Security and Sustainability (WS4 2021) which is divided into different sections such as Smart IT Infrastructure for Sustainable Society; Smart Management prospective for Sustainable Society; Smart Secure Systems for Next Generation Technologies; Smart Trends for Computational Graphics and Image Modeling; and Smart Trends for Biomedical and Health Informatics. The proceedings is presented in two volumes. The book is helpful for active researchers and practitioners in the field. |
| financial news sentiment analysis: Data Mining Yue Xu, Rosalind Wang, Anton Lord, Yee Ling Boo, Richi Nayak, Yanchang Zhao, Graham Williams, 2021-12-08 This book constitutes the refereed proceedings of the 19th Australasian Conference on Data Mining, AusDM 2021, held in Brisbane, Queensland, Australia, in December 2021.* The 16 revised full papers presented were carefully reviewed and selected from 32 submissions. The papers are organized in sections on research track and application track. *Due to the COVID-19 pandemic the conference was held online. |
| financial news sentiment analysis: Media Sentiment and International Asset Prices Samuel P. Fraiberger, Do Lee, Mr.Damien Puy, Mr.Romain Ranciere, 2018-12-10 We assess the impact of media sentiment on international equity prices using more than 4.5 million Reuters articles published across the globe between 1991 and 2015. News sentiment robustly predicts daily returns in both advanced and emerging markets, even after controlling for known determinants of stock prices. But not all news-sentiment is alike. A local (country-specific) increase in news optimism (pessimism) predicts a small and transitory increase (decrease) in local returns. By contrast, changes in global news sentiment have a larger impact on equity returns around the world, which does not reverse in the short run. We also find evidence that news sentiment affects mainly foreign – rather than local – investors: although local news optimism attracts international equity flows for a few days, global news optimism generates a permanent foreign equity inflow. Our results confirm the value of media content in capturing investor sentiment. |
| financial news sentiment analysis: From Opinion Mining to Financial Argument Mining Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen, Xinxi Chen, 2021 Opinion mining is a prevalent research issue in many domains. In the financial domain, however, it is still in the early stages. Most of the researches on this topic only focus on the coarse-grained market sentiment analysis, i.e., 2-way classification for bullish/bearish. Thanks to the recent financial technology (FinTech) development, some interdisciplinary researchers start to involve in the in-depth analysis of investors' opinions. These works indicate the trend toward fine-grained opinion mining in the financial domain. When expressing opinions in finance, terms like bullish/bearish often spring to mind. However, the market sentiment of the financial instrument is just one type of opinion in the financial industry. Like other industries such as manufacturing and textiles, the financial industry also has a large number of products. Financial services are also a major business for many financial companies, especially in the context of the recent FinTech trend. For instance, many commercial banks focus on loans and credit cards. Although there are a variety of issues that could be explored in the financial domain, most researchers in the AI and NLP communities only focus on the market sentiment of the stock or foreign exchange. This open access book addresses several research issues that can broaden the research topics in the AI community. It also provides an overview of the status quo in fine-grained financial opinion mining to offer insights into the futures goals. For a better understanding of the past and the current research, it also discusses the components of financial opinions one-by-one with the related works and highlights some possible research avenues, providing a research agenda with both micro- and macro-views toward financial opinions. |
| financial news sentiment analysis: Social Big Data Analytics Bilal Abu-Salih, Pornpit Wongthongtham, Dengya Zhu, Kit Yan Chan, Amit Rudra, 2021-03-10 This book focuses on data and how modern business firms use social data, specifically Online Social Networks (OSNs) incorporated as part of the infrastructure for a number of emerging applications such as personalized recommendation systems, opinion analysis, expertise retrieval, and computational advertising. This book identifies how in such applications, social data offers a plethora of benefits to enhance the decision making process. This book highlights that business intelligence applications are more focused on structured data; however, in order to understand and analyse the social big data, there is a need to aggregate data from various sources and to present it in a plausible format. Big Social Data (BSD) exhibit all the typical properties of big data: wide physical distribution, diversity of formats, non-standard data models, independently-managed and heterogeneous semantics but even further valuable with marketing opportunities. The book provides a review of the current state-of-the-art approaches for big social data analytics as well as to present dissimilar methods to infer value from social data. The book further examines several areas of research that benefits from the propagation of the social data. In particular, the book presents various technical approaches that produce data analytics capable of handling big data features and effective in filtering out unsolicited data and inferring a value. These approaches comprise advanced technical solutions able to capture huge amounts of generated data, scrutinise the collected data to eliminate unwanted data, measure the quality of the inferred data, and transform the amended data for further data analysis. Furthermore, the book presents solutions to derive knowledge and sentiments from BSD and to provide social data classification and prediction. The approaches in this book also incorporate several technologies such as semantic discovery, sentiment analysis, affective computing and machine learning. This book has additional special feature enriched with numerous illustrations such as tables, graphs and charts incorporating advanced visualisation tools in accessible an attractive display. |
| financial news sentiment analysis: Opinion Mining and Sentiment Analysis Bo Pang, Lillian Lee, 2008 This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. |
| financial news sentiment analysis: Semantic Web Challenges Mauro Dragoni, Monika Solanki, Eva Blomqvist, 2017-10-30 This book constitutes the thoroughly refereed post conference proceedings of the 4th edition of the Semantic Web Evaluation Challenge, SemWebEval 2017, co-located with the 14th European Semantic Web conference, held in Portoroz, Slovenia, in May/June 2017. This book includes the descriptions of all methods and tools that competed at SemWebEval 2017, together with a detailed description of the tasks, evaluation procedures and datasets. The 11 revised full papers presented in this volume were carefully reviewed and selected from 21 submissions. The contributions are grouped in the areas: the mighty storage challenge; open knowledge extraction challenge; question answering over linked data challenge; semantic sentiment analysis. |
| financial news sentiment analysis: Proceedings of Integrated Intelligence Enable Networks and Computing Krishan Kant Singh Mer, Vijay Bhaskar Semwal, Vishwanath Bijalwan, Rubén González Crespo, 2021-04-23 This book presents best selected research papers presented at the First International Conference on Integrated Intelligence Enable Networks and Computing (IIENC 2020), held from May 25 to May 27, 2020, at the Institute of Technology, Gopeshwar, India (Government Institute of Uttarakhand Government and affiliated to Uttarakhand Technical University). The book includes papers in the field of intelligent computing. The book covers the areas of machine learning and robotics, signal processing and Internet of things, big data and renewable energy sources. |
| financial news sentiment analysis: Intelligent Computing and Innovation on Data Science Sheng-Lung Peng, Sun-Yuan Hsieh, Suseendran Gopalakrishnan, Balaganesh Duraisamy, 2021-09-27 This book gathers high-quality papers presented at 2nd International Conference on Technology Innovation and Data Sciences (ICTIDS 2021), organized by Lincoln University, Malaysia from 19 – 20 February 2021. It covers wide range of recent technologies like artificial intelligence and machine learning, big data and data sciences, Internet of Things (IoT), and IoT-based digital ecosystem. The book brings together works from researchers, scientists, engineers, scholars and students in the areas of engineering and technology, and provides an opportunity for the dissemination of original research results, new ideas, research and development, practical experiments, which concentrate on both theory and practices, for the benefit of common man. |
| financial news sentiment analysis: Sentiment in the Forex Market Jamie Saettele, 2017-11-06 Crowds move markets and at major market turning points, the crowds are almost always wrong. When crowd sentiment is overwhelmingly positive or overwhelmingly negative ? it's a signal that the trend is exhausted and the market is ready to move powerfully in the opposite direction. Sentiment has long been a tool used by equity, futures, and options traders. In Sentiment in the Forex Market, FXCM analyst Jaime Saettele applies sentiment analysis to the currency market, using both traditional and new sentiment indicators, including: Commitment of Traders reports; time cycles; pivot points; oscillators; and Fibonacci time and price ratios. He also explains how to interpret news coverage of the markets to get a sense of when participants have become overly bullish or bearish. Saettele points out that several famous traders such as George Soros and Robert Prechter made huge profits by identifying shifts in crowd sentiment at major market turning points. Many individual traders lose money in the currency market, Saettele asserts, because they are too short-term oriented and trade impulsively. He believes retail traders would be much more successful if they adopted a longer-term, contrarian approach, utilizing sentiment indicators to position themselves at the beginning points of major trends. |
| financial news sentiment analysis: Artificial Neural Network Modelling Subana Shanmuganathan, Sandhya Samarasinghe, 2016-02-03 This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. |
| financial news sentiment analysis: Handbook of Natural Language Processing Nitin Indurkhya, Fred J. Damerau, 2010-02-22 The Handbook of Natural Language Processing, Second Edition presents practical tools and techniques for implementing natural language processing in computer systems. Along with removing outdated material, this edition updates every chapter and expands the content to include emerging areas, such as sentiment analysis.New to the Second EditionGreater |
| financial news sentiment analysis: Financial Stability Reports Ms.Sònia Muñoz, Mr.Samir Jahjah, Mr.Martin Cihak, Ms.Sharika Teh Sharifuddin, Mr.Kalin Tintchev, 2012-01-01 The global financial crisis has renewed policymakers' interest in improving the policy framework for financial stability, and an open question is to what extent and in what form should financial stability reports be part of it. We examine the recent experience with central banks' financial stability reports, and find?despite some progress in recent years?that forward-looking perspective and analysis of financial interconnectedness are often lacking. We also find that higher-quality reports tend to be associated with more stable financial environments. However, there is only a weak empirical link between financial stability report publication per se and financial stability. This suggests room for improvement in terms of the quality of financial stability reports. |
| financial news sentiment analysis: Research Design & Statistical Analysis Arnold D. Well, Jerome L. Myers, 2003-01-30 Free CD contains several real and artificial data sets used in the book in SPSS, SYSTAT, and ASCII formats--Cover |
| financial news sentiment analysis: Deep Learning Li Deng, Dong Yu, 2014 Provides an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks |
| financial news sentiment analysis: Sentiment Analysis Bing Liu, 2020-10-15 Sentiment analysis is the computational study of people's opinions, sentiments, emotions, moods, and attitudes. This fascinating problem offers numerous research challenges, but promises insight useful to anyone interested in opinion analysis and social media analysis. This comprehensive introduction to the topic takes a natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs commonly used to express opinions, sentiments, and emotions. The book covers core areas of sentiment analysis and also includes related topics such as debate analysis, intention mining, and fake-opinion detection. It will be a valuable resource for researchers and practitioners in natural language processing, computer science, management sciences, and the social sciences. In addition to traditional computational methods, this second edition includes recent deep learning methods to analyze and summarize sentiments and opinions, and also new material on emotion and mood analysis techniques, emotion-enhanced dialogues, and multimodal emotion analysis. |
| financial news sentiment analysis: Linear Factor Models in Finance John Knight, 2004-12-01 The determination of the values of stocks, bonds, options, futures, and derivatives is done by the scientific process of asset pricing, which has developed dramatically in the last few years due to advances in financial theory and econometrics. This book covers the science of asset pricing by concentrating on the most widely used modelling technique called: Linear Factor Modelling.Linear Factor Models covers an important area for Quantitative Analysts/Investment Managers who are developing Quantitative Investment Strategies. Linear factor models (LFM) are part of modern investment processes that include asset valuation, portfolio theory and applications, linear factor models and applications, dynamic asset allocation strategies, portfolio performance measurement, risk management, international perspectives, and the use of derivatives. The book develops the building blocks for one of the most important theories of asset pricing - Linear Factor Modelling. Within this framework, we can include other asset pricing theories such as the Capital Asset Pricing Model (CAPM), arbitrage pricing theory and various pricing formulae for derivatives and option prices. As a bare minimum, the reader of this book must have a working knowledge of basic calculus, simple optimisation and elementary statistics. In particular, the reader must be comfortable with the algebraic manipulation of means, variances (and covariances) of linear combination(s) of random variables. Some topics may require a greater mathematical sophistication.* Covers the latest methods in this area.* Combines actual quantitative finance experience with analytical research rigour* Written by both quantitative analysts and academics who work in this area |
| financial news sentiment analysis: Modern Equity Investing Strategies Anatoly B Schmidt, 2021 This book will satisfy the demand among college majors in Finance and Financial Engineering, and mathematically-versed practitioners for description of both the classical approaches to equity investing and new investment strategies scattered in the periodic literature. Besides the major portfolio management theories (mean variance theory, CAPM, and APT), the book addresses several important topics: portfolio diversification, optimal ESG portfolios, factor models (smart betas), robust portfolio optimization, risk-based asset allocation, statistical arbitrage, alternative data based investing, back-testing of trading strategies, modern market microstructure, algorithmic trading, and agent-based modeling of financial markets. The book also includes the basic elements of time series analysis in the Appendix for self-contained presentation of the material. While the book covers technical concepts and models, it will not overburden the reader with math beyond the Finance undergraduates' curriculum. |
| financial news sentiment analysis: First International Conference on Sustainable Technologies for Computational Intelligence Ashish Kumar Luhach, Janos Arpad Kosa, Ramesh Chandra Poonia, Xiao-Zhi Gao, Dharm Singh, 2019-11-01 This book gathers high-quality papers presented at the First International Conference on Sustainable Technologies for Computational Intelligence (ICTSCI 2019), which was organized by Sri Balaji College of Engineering and Technology, Jaipur, Rajasthan, India, on March 29–30, 2019. It covers emerging topics in computational intelligence and effective strategies for its implementation in engineering applications. |
| financial news sentiment analysis: Sentiment Analysis and its Application in Educational Data Mining Soni Sweta, |
| financial news sentiment analysis: Mexican Financial System , 1993 |
| financial news sentiment analysis: State of The Global Workplace Gallup, 2017-12-19 Only 15% of employees worldwide are engaged at work. This represents a major barrier to productivity for organizations everywhere – and suggests a staggering waste of human potential. Why is this engagement number so low? There are many reasons — but resistance to rapid change is a big one, Gallup’s research and experience have discovered. In particular, organizations have been slow to adapt to breakneck changes produced by information technology, globalization of markets for products and labor, the rise of the gig economy, and younger workers’ unique demands. Gallup’s 2017 State of the Global Workplace offers analytics and advice for organizational leaders in countries and regions around the globe who are trying to manage amid this rapid change. Grounded in decades of Gallup research and consulting worldwide -- and millions of interviews -- the report advises that leaders improve productivity by becoming far more employee-centered; build strengths-based organizations to unleash workers’ potential; and hire great managers to implement the positive change their organizations need not only to survive – but to thrive. |
| financial news sentiment analysis: Analysis of Financial Time Series Ruey S. Tsay, 2001-11-01 Fundamental topics and new methods in time series analysis Analysis of Financial Time Series provides a comprehensive and systematic introduction to financial econometric models and their application to modeling and prediction of financial time series data. It utilizes real-world examples and real financial data throughout the book to apply the models and methods described. The author begins with basic characteristics of financial time series data before covering three main topics: analysis and application of univariate financial time series; the return series of multiple assets; and Bayesian inference in finance methods. Timely topics and recent results include: Value at Risk (VaR) High-frequency financial data analysis Markov Chain Monte Carlo (MCMC) methods Derivative pricing using jump diffusion with closed-form formulas VaR calculation using extreme value theory based on a non-homogeneous two-dimensional Poisson process Multivariate volatility models with time-varying correlations Ideal as a fundamental introduction to time series for MBA students or as a reference for researchers and practitioners in business and finance, Analysis of Financial Time Series offers an in-depth and up-to-date account of these vital methods. |
| financial news sentiment analysis: AI-Based Data Analytics Kiran Chaudhary, Mansaf Alam, 2023-12-29 Apply analytics to improve customer experience, AI applied to targeted and personalized marketing Debugging and simulation tools and techniques for massive data systems |
| financial news sentiment analysis: Experimental IR Meets Multilinguality, Multimodality, and Interaction K. Selçuk Candan, Bogdan Ionescu, Lorraine Goeuriot, Birger Larsen, Henning Müller, Alexis Joly, Maria Maistro, Florina Piroi, Guglielmo Faggioli, Nicola Ferro, 2021-09-14 This book constitutes the refereed proceedings of the 12th International Conference of the CLEF Association, CLEF 2021, held virtually in September 2021. The conference has a clear focus on experimental information retrieval with special attention to the challenges of multimodality, multilinguality, and interactive search ranging from unstructured to semi structures and structured data. The 11 full papers presented in this volume were carefully reviewed and selected from 21 submissions. This year, the contributions addressed the following challenges: application of neural methods for entity recognition as well as misinformation detection in the health area, skills extraction in job-match databases, stock market prediction using financial news, and extraction of audio features for podcast retrieval. In addition to this, the volume presents 5 “best of the labs” papers which were reviewed as full paper submissions with the same review criteria. 12 lab overview papers were accepted and represent scientific challenges based on new data sets and real world problems in multimodal and multilingual information access. |
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Google Finance provides real-time market quotes, international exchanges, up-to-date financial news, and analytics to help you make more …
Home Page - APG Federal Credit Union
APGFCU offers checking, savings, loans, and business banking services in Maryland to help you achieve your …
Stock Markets, Business News, Financials, Earnings - CNBC
Global Business and Financial News, Stock Quotes, and Market Data and Analysis. CNBC is the world leader in …
MarketWatch: Stock Market News - Financial News
Americans spend $10 billion more on Mother’s Day than Father’s Day. What’s going on? So your company offered you a buyout. Should you take it? Here’s …
