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Market Efficiency In The Age Of Big Data
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Book Synopsis Market Efficiency in the Age of Big Data by : Ian Martin
Download or read book Market Efficiency in the Age of Big Data written by Ian Martin and published by . This book was released on 2019 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern investors face a high-dimensional prediction problem: thousands of observable variables are potentially relevant for forecasting. We reassess the conventional wisdom on market efficiency in light of this fact. In our model economy, which resembles a typical machine learning setting, N assets have cash flows that are a linear function of J firm characteristics, but with uncertain coefficients. Risk-neutral Bayesian investors impose shrinkage (ridge regression) or sparsity (Lasso) when they estimate the J coefficients of the model and use them to price assets. When J is comparable in size to N, returns appear cross-sectionally predictable using firm characteristics to an econometrician who analyzes data from the economy ex post. A factor zoo emerges even without p-hacking and data-mining. Standard in-sample tests of market efficiency reject the no-predictability null with high probability, despite the fact that investors optimally use the information available to them in real time. In contrast, out-of-sample tests retain their economic meaning.
Book Synopsis Reinventing Capitalism in the Age of Big Data by : Viktor Mayer-Schönberger
Download or read book Reinventing Capitalism in the Age of Big Data written by Viktor Mayer-Schönberger and published by Basic Books. This book was released on 2018-02-27 with total page 239 pages. Available in PDF, EPUB and Kindle. Book excerpt: From the New York Times bestselling author of Big Data, a prediction for how data will revolutionize the market economy and make cash, banks, and big companies obsolete In modern history, the story of capitalism has been a story of firms and financiers. That's all going to change thanks to the Big Data revolution. As Viktor Mayer-Schörger, bestselling author of Big Data, and Thomas Ramge, who writes for The Economist, show, data is replacing money as the driver of market behavior. Big finance and big companies will be replaced by small groups and individual actors who make markets instead of making things: think Uber instead of Ford, or Airbnb instead of Hyatt. This is the dawn of the era of data capitalism. Will it be an age of prosperity or of calamity? This book provides the indispensable roadmap for securing a better future.
Book Synopsis The Effects of Big Data and Social Media on Stock Market Efficiency by : Gaurang K. Patel
Download or read book The Effects of Big Data and Social Media on Stock Market Efficiency written by Gaurang K. Patel and published by . This book was released on 2014 with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis The Efficiency Paradox by : Edward Tenner
Download or read book The Efficiency Paradox written by Edward Tenner and published by Vintage. This book was released on 2018-04-17 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: A bold challenge to our obsession with efficiency—and a new understanding of how to benefit from the powerful potential of serendipity. Algorithms, multitasking, the sharing economy, life hacks: our culture can't get enough of efficiency. One of the great promises of the Internet and big data revolutions is the idea that we can improve the processes and routines of our work and personal lives to get more done in less time than we ever have before. There is no doubt that we're performing at higher levels and moving at unprecedented speed, but what if we're headed in the wrong direction? Melding the long-term history of technology with the latest headlines and findings of computer science and social science, The Efficiency Paradox questions our ingrained assumptions about efficiency, persuasively showing how relying on the algorithms of digital platforms can in fact lead to wasted efforts, missed opportunities, and, above all, an inability to break out of established patterns. Edward Tenner offers a smarter way of thinking about efficiency, revealing what we and our institutions, when equipped with an astute combination of artificial intelligence and trained intuition, can learn from the random and unexpected.
Book Synopsis Machine Learning in Asset Pricing by : Stefan Nagel
Download or read book Machine Learning in Asset Pricing written by Stefan Nagel and published by Princeton University Press. This book was released on 2021-05-11 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: A groundbreaking, authoritative introduction to how machine learning can be applied to asset pricing Investors in financial markets are faced with an abundance of potentially value-relevant information from a wide variety of different sources. In such data-rich, high-dimensional environments, techniques from the rapidly advancing field of machine learning (ML) are well-suited for solving prediction problems. Accordingly, ML methods are quickly becoming part of the toolkit in asset pricing research and quantitative investing. In this book, Stefan Nagel examines the promises and challenges of ML applications in asset pricing. Asset pricing problems are substantially different from the settings for which ML tools were developed originally. To realize the potential of ML methods, they must be adapted for the specific conditions in asset pricing applications. Economic considerations, such as portfolio optimization, absence of near arbitrage, and investor learning can guide the selection and modification of ML tools. Beginning with a brief survey of basic supervised ML methods, Nagel then discusses the application of these techniques in empirical research in asset pricing and shows how they promise to advance the theoretical modeling of financial markets. Machine Learning in Asset Pricing presents the exciting possibilities of using cutting-edge methods in research on financial asset valuation.
Book Synopsis Big Data by : Viktor Mayer-Schönberger
Download or read book Big Data written by Viktor Mayer-Schönberger and published by Houghton Mifflin Harcourt. This book was released on 2013 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: A exploration of the latest trend in technology and the impact it will have on the economy, science, and society at large.
Book Synopsis Big Data, Big Analytics by : Michael Minelli
Download or read book Big Data, Big Analytics written by Michael Minelli and published by John Wiley & Sons. This book was released on 2013-01-22 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unique prospective on the big data analytics phenomenon for both business and IT professionals The availability of Big Data, low-cost commodity hardware and new information management and analytics software has produced a unique moment in the history of business. The convergence of these trends means that we have the capabilities required to analyze astonishing data sets quickly and cost-effectively for the first time in history. These capabilities are neither theoretical nor trivial. They represent a genuine leap forward and a clear opportunity to realize enormous gains in terms of efficiency, productivity, revenue and profitability. The Age of Big Data is here, and these are truly revolutionary times. This timely book looks at cutting-edge companies supporting an exciting new generation of business analytics. Learn more about the trends in big data and how they are impacting the business world (Risk, Marketing, Healthcare, Financial Services, etc.) Explains this new technology and how companies can use them effectively to gather the data that they need and glean critical insights Explores relevant topics such as data privacy, data visualization, unstructured data, crowd sourcing data scientists, cloud computing for big data, and much more.
Book Synopsis Understanding Machine Learning by : Shai Shalev-Shwartz
Download or read book Understanding Machine Learning written by Shai Shalev-Shwartz and published by Cambridge University Press. This book was released on 2014-05-19 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Book Synopsis eBook: Corporate Finance 5e by : David Hillier
Download or read book eBook: Corporate Finance 5e written by David Hillier and published by McGraw Hill. This book was released on 2024-02-12 with total page 955 pages. Available in PDF, EPUB and Kindle. Book excerpt: The fifth European edition of Corporate Finance takes an applied approach to cover all the latest research and topic areas important to students taking Finance courses. The new edition provides an international perspective on all areas of corporate finance and has been updated to include discussion on current trends such as the integrated nature of global supply chains, financial risk management, and key regulatory changes impacting the sector. It addresses the impact that FinTech, the climate and geopolitics are having on the development of corporate finance, considers the questions brought about by the global corona virus pandemic, and looks to the future of the industry. Understanding and Application •Clear, user-friendly style •Example boxes in every chapter provide hypothetical examples to illustrate theoretical concepts such as cash flow timing, dividend smoothing and differential growth. •Real World Insight boxes use companies like Apple, Volkswagen and Adidas to show how they have applied corporate finance theories and concepts to their business decisions. •Chapter links throughout provide quick cross-referencing to show the connections between topics. Practice and Proficiency •Mini and Practical cases present scenarios and questions to practice application and learning. •Questions and Problems in each chapter, categorised by topic and level of difficulty, allow for rigorous testing of the chapter content. •Numbered maths equations and key notation boxes listing the variables and acronyms that will be encountered in each chapter, designed to encourage mastery of Maths. •Exam Questions designed to take 45 minutes and test you on material learned in a more formal exam style. •Connect® resources include algorithmic questions designed to ensure equations and calculations are not learned by rote but by thorough understanding and practice. New to This Edition •Sustainability in Action boxes draw on issues relating to the environment, society, the economy and climate change to show how corporate finance is so important to the resolution of sustainability challenges. •Updated discussions and new sections on sustainable value added, green bonds, dividend policy and share repurchases, Islamic Financing, intangible valuation, and the differential value method. Available on McGraw Hill’s Connect®, the well-established online learning platform, which features our award-winning adaptive reading experience as well as resources to help faculty and institutions improve student outcomes and course delivery efficiency. To learn more, visit mheducation.co.uk/connect David Hillier is Associate Principal and Executive Dean of the University of Strathclyde Business School. A Professor of Finance, David was recognized as being in the top 3 per cent of the most prolific finance researchers in the world over the past 50 years (Heck and Cooley, 2009) and appears regularly in the media as a business commentator. His YouTube channel of finance lectures (professordavidhillier) has attracted nearly half a million views worldwide. This European edition is originally based on the Corporate Finance text by Stephen A. Ross, Randolph W. Westerfield, Jeffrey F. Jaffe, and Bradford D. Jordan.
Book Synopsis The Age of Big Data by : Jude Hawthorne
Download or read book The Age of Big Data written by Jude Hawthorne and published by RWG Publishing. This book was released on 2024-07-24 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: In "The Age of Big Data: Harnessing Information for Insight and Impact," Jude Hawthorne delves into the transformative power of massive datasets in our increasingly digital world. Drawing from his extensive experience since the late 1990s, Hawthorne explores the evolution of "big data" from a nascent concept to a critical element in various fields such as medicine, finance, and climate science. He examines the challenges of managing and interpreting vast amounts of data from diverse sources and showcases innovative approaches taken by creative minds across different disciplines. This insightful book illuminates the essential role of data in driving new insights, solving persistent problems, and integrating disparate modes of inquiry, ultimately demonstrating how harnessing big data is vital for achieving progress in today's complex, data-driven society.
Book Synopsis Data Privacy and Competition Law in the Age of Big Data by : Samson Y. Esayas
Download or read book Data Privacy and Competition Law in the Age of Big Data written by Samson Y. Esayas and published by Oxford University Press. This book was released on 2024-07-09 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: The monetization of personal data has become an increasingly common business practice, igniting global debate on the interface between data privacy law and competition law. Data Privacy and Competition Law in the Age of Big Data provides a comprehensive, novel, and interdisciplinary analysis of this nexus. Drawing insights from emergent properties and complexity science, the book exposes the commonalities and conflicts between how data privacy law and competition law address challenges resulting from the commercialization of personal data. Samson Y. Esayas begins by identifying key shifts in big data: the growing trend of processing personal data for diverse purposes, the aggregation of data across various operations, and the shift from offering stand-alone products and services to ecosystems of several, with personal data central in connecting the different markets. These shifts engender a complex economic landscape, marked by multiple actors, a web of interactions, and non-linear, emergent outcomes. Despite this complexity, the prevailing approach to data privacy law and competition law emphasises isolated units of analysis-whether a relevant market or a distinct processing operation. This approach overlooks system-wide (emergent) risks borne of cumulative processing operations and cross-market practices. Additionally, a mindset focused on either data privacy law or competition law overlooks the increasing intersection between the two regimes, missing opportunities for synergy. In light of these challenges, Esayas's volume calls for recalibrating data privacy law and competition law for a complex economy, emphasizing a holistic, systems-level perspective that addresses emergent harms and a polycentric strategy that leverages the strengths of each legal regime.
Book Synopsis Machine Learning in Asset Pricing by : Stefan Nagel
Download or read book Machine Learning in Asset Pricing written by Stefan Nagel and published by Princeton University Press. This book was released on 2021-05-11 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: A groundbreaking, authoritative introduction to how machine learning can be applied to asset pricing Investors in financial markets are faced with an abundance of potentially value-relevant information from a wide variety of different sources. In such data-rich, high-dimensional environments, techniques from the rapidly advancing field of machine learning (ML) are well-suited for solving prediction problems. Accordingly, ML methods are quickly becoming part of the toolkit in asset pricing research and quantitative investing. In this book, Stefan Nagel examines the promises and challenges of ML applications in asset pricing. Asset pricing problems are substantially different from the settings for which ML tools were developed originally. To realize the potential of ML methods, they must be adapted for the specific conditions in asset pricing applications. Economic considerations, such as portfolio optimization, absence of near arbitrage, and investor learning can guide the selection and modification of ML tools. Beginning with a brief survey of basic supervised ML methods, Nagel then discusses the application of these techniques in empirical research in asset pricing and shows how they promise to advance the theoretical modeling of financial markets. Machine Learning in Asset Pricing presents the exciting possibilities of using cutting-edge methods in research on financial asset valuation.
Book Synopsis Analytics in a Big Data World by : Bart Baesens
Download or read book Analytics in a Big Data World written by Bart Baesens and published by John Wiley & Sons. This book was released on 2014-04-15 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: The guide to targeting and leveraging business opportunities using big data & analytics By leveraging big data & analytics, businesses create the potential to better understand, manage, and strategically exploiting the complex dynamics of customer behavior. Analytics in a Big Data World reveals how to tap into the powerful tool of data analytics to create a strategic advantage and identify new business opportunities. Designed to be an accessible resource, this essential book does not include exhaustive coverage of all analytical techniques, instead focusing on analytics techniques that really provide added value in business environments. The book draws on author Bart Baesens' expertise on the topics of big data, analytics and its applications in e.g. credit risk, marketing, and fraud to provide a clear roadmap for organizations that want to use data analytics to their advantage, but need a good starting point. Baesens has conducted extensive research on big data, analytics, customer relationship management, web analytics, fraud detection, and credit risk management, and uses this experience to bring clarity to a complex topic. Includes numerous case studies on risk management, fraud detection, customer relationship management, and web analytics Offers the results of research and the author's personal experience in banking, retail, and government Contains an overview of the visionary ideas and current developments on the strategic use of analytics for business Covers the topic of data analytics in easy-to-understand terms without an undo emphasis on mathematics and the minutiae of statistical analysis For organizations looking to enhance their capabilities via data analytics, this resource is the go-to reference for leveraging data to enhance business capabilities.
Download or read book Big Data written by and published by . This book was released on 2011 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Big Data in Computational Social Science and Humanities by : Shu-Heng Chen
Download or read book Big Data in Computational Social Science and Humanities written by Shu-Heng Chen and published by Springer. This book was released on 2018-11-21 with total page 391 pages. Available in PDF, EPUB and Kindle. Book excerpt: This edited volume focuses on big data implications for computational social science and humanities from management to usage. The first part of the book covers geographic data, text corpus data, and social media data, and exemplifies their concrete applications in a wide range of fields including anthropology, economics, finance, geography, history, linguistics, political science, psychology, public health, and mass communications. The second part of the book provides a panoramic view of the development of big data in the fields of computational social sciences and humanities. The following questions are addressed: why is there a need for novel data governance for this new type of data?, why is big data important for social scientists?, and how will it revolutionize the way social scientists conduct research? With the advent of the information age and technologies such as Web 2.0, ubiquitous computing, wearable devices, and the Internet of Things, digital society has fundamentally changed what we now know as "data", the very use of this data, and what we now call "knowledge". Big data has become the standard in social sciences, and has made these sciences more computational. Big Data in Computational Social Science and Humanities will appeal to graduate students and researchers working in the many subfields of the social sciences and humanities.
Book Synopsis Big Data Processing With Hadoop by : Revathi, T.
Download or read book Big Data Processing With Hadoop written by Revathi, T. and published by IGI Global. This book was released on 2018-11-16 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to the increasing availability of affordable internet services, the number of users, and the need for a wider range of multimedia-based applications, internet usage is on the rise. With so many users and such a large amount of data, the requirements of analyzing large data sets leads to the need for further advancements to information processing. Big Data Processing With Hadoop is an essential reference source that discusses possible solutions for millions of users working with a variety of data applications, who expect fast turnaround responses, but encounter issues with processing data at the rate it comes in. Featuring research on topics such as market basket analytics, scheduler load simulator, and writing YARN applications, this book is ideally designed for IoT professionals, students, and engineers seeking coverage on many of the real-world challenges regarding big data.
Book Synopsis Machine Learning for Factor Investing: R Version by : Guillaume Coqueret
Download or read book Machine Learning for Factor Investing: R Version written by Guillaume Coqueret and published by CRC Press. This book was released on 2020-08-31 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning (ML) is progressively reshaping the fields of quantitative finance and algorithmic trading. ML tools are increasingly adopted by hedge funds and asset managers, notably for alpha signal generation and stocks selection. The technicality of the subject can make it hard for non-specialists to join the bandwagon, as the jargon and coding requirements may seem out of reach. Machine Learning for Factor Investing: R Version bridges this gap. It provides a comprehensive tour of modern ML-based investment strategies that rely on firm characteristics. The book covers a wide array of subjects which range from economic rationales to rigorous portfolio back-testing and encompass both data processing and model interpretability. Common supervised learning algorithms such as tree models and neural networks are explained in the context of style investing and the reader can also dig into more complex techniques like autoencoder asset returns, Bayesian additive trees, and causal models. All topics are illustrated with self-contained R code samples and snippets that are applied to a large public dataset that contains over 90 predictors. The material, along with the content of the book, is available online so that readers can reproduce and enhance the examples at their convenience. If you have even a basic knowledge of quantitative finance, this combination of theoretical concepts and practical illustrations will help you learn quickly and deepen your financial and technical expertise.
