The first and only book to systematically address methodologies and processes of leveraging non-traditional information sources in the context of investing and risk management
Harnessing non-traditional data sources to generate alpha, analyze markets, and forecast risk is a subject of intense interest for financial professionals. A growing number of regularly-held conferences on alternative data are being established, complemented by an upsurge in new papers on the subject. Alternative data is starting to be steadily incorporated by conventional institutional investors and risk managers throughout the financial world. Methodologies to analyze and extract value from alternative data, guidance on how to source data and integrate data flows within existing systems is currently not treated in literature. Filling this significant gap in knowledge, The Book of Alternative Data is the first and only book to offer a coherent, systematic treatment of the subject.
This groundbreaking volume provides readers with a roadmap for navigating the complexities of an array of alternative data sources, and delivers the appropriate techniques to analyze them. The authors―leading experts in financial modeling, machine learning, and quantitative research and analytics―employ a step-by-step approach to guide readers through the dense jungle of generated data. A first-of-its kind treatment of alternative data types, sources, and methodologies, this innovative book:
The Book of Alternative Data is an indispensable resource for anyone wishing to analyze or monetize different non-traditional datasets, including Chief Investment Officers, Chief Risk Officers, risk professionals, investment professionals, traders, economists, and machine learning developers and users.
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ALEXANDER DENEV is Head of AI, Financial Services - Risk Advisory at Deloitte LLP. Prior to that he led Quantitative Research & Advanced Analytics at IHS Markit. Previously, he held roles at the Royal Bank of Scotland, Societe Generale, and European Investment Bank. Denev is a visiting lecturer at the University of Oxford where he graduated with a degree in Mathematical Finance. He is author of numerous papers and books on novel methods of financial modeling with applications ranging from stress testing to asset allocation.
SAEED AMEN is the founder of Cuemacro, where he consults on systematic trading. For 15 years, he has developed systematic trading strategies and quantitative indices including at major investment banks, Lehman Brothers and Nomura. He is also a visiting lecturer at Queen Mary University of London and a co-founder of the Thalesians, a quant think tank.
Praise for The Book of Alternative Data
"Alternative data is one of the hottest topics in the investment management industry today. Whether it is used to forecast global economic growth in real-time, parse the entrails of a company with more granularity than that offered by a quarterly report, or to better understand stock market behavior, alternative data is something that everyone in asset management needs to get to grips with. Alexander Denev and Saeed Amen are able guides to a convoluted subject with many pitfalls, both technical and theoretical, even for those that still think Python is a snake best avoided."
Robin Wigglesworth, Global Finance Correspondent, Financial Times
"Congratulations to the authors for producing such a timely, comprehensive, and accessible discussion of alternative data. As we move further into the 21st Century, this book will rapidly become the go-to work on the subject."
David Hand, Senior Research Investigator and Emeritus Professor of Mathematics, Imperial College London
"Over the last decade, Alternative Data has become central to the quest for temporary monopoly of information. Yet, despite its frequent use, little has been written about the end-to-end pipeline necessary to extract value. This book fills the omission, providing not just practical overviews of machine learning methods and data sources, but placing as much importance on data ingestion, preparation, and pre-processing as on the models that map to outcomes. The authors do not consider methodology alone, but also provide insightful case studies, practical examples and highlight the importance of cost-benefit analysis throughout. For value extraction from Alternative Data, they provide informed insights and deep conceptual understanding crucial if we are to successfully embed such technology at the heart of trading."
Stephen Roberts, Royal Academy of Engineering and Man Group Professor of Machine Learning, University of Oxford, UK; director, Oxford-Man Institute of Quantitative Finance
"True investment outperformance comes from the triad of data + machine learning + supercomputing. Alexander Denev and Saeed Amen have written the first comprehensive exposition of alternative data, revealing sources of alpha that are not tapped by structured datasets. Asset managers unfamiliar with the contents of this book are not earning the fees they charge to investors."
Dr. Marcos López de Prado, Professor of Practice, Cornell University; CIO, True Positive Technologies LP
"Alexander and Saeed have written an important book about an important topic. I am involved with alternative data every day, but I still enjoyed the perspectives in the book and learned a lot. I highly recommend it to everybody looking to harness the power of alt data (and avoid the pitfalls!)."
Jens Nordvig, Founder and CEO, Exante Data
As investors search for innovative methods to generate superior returns, many are turning to alternative data. Alternative datasets already exist, for those who know where to look and who have the resources to acquire them. The difficulty lies in knowing how to analyze this data appropriately, generate signal from the noise, and act on alternative data insights to make investment decisions. The Book of Alternative Data: A Guide for Investors, Traders, and Risk Managers is the first book to comprehensively instruct readers in the process of making money by investing with alternative data.
Automotive supply chain data, satellite imagery, survey data, mobile phone location data, social media, and credit card transaction data are a few of the alternative data sources that investors and risk managers can use to generate a competitive edge. This book provides detailed case studies demonstrating how to locate and assess such datasets. More importantly, the authors present an end-to-end process for alternative data analysis and investing, so readers will be able to extract value from any source of alternative data, now and in the future.
Because alternative datasets are more expensive, more difficult to use, and generally newer than traditional data, the risks involved can be considerable. The Book of Alternative Data clarifies the murky waters of alternative data, helping readers understand and manage the unique risks. Alternative data can be easy to misinterpret, opening the possibility for costly errors. Legality and compliance are also major concerns. This book thoroughly addresses these and other considerations, leaving institutional investors and risk managers with a basis of knowledge that will enable them to extract the maximum value from alternative data.
For further resources related to the book, please see https://www.cuemacro.com/altdata
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Hardcover. Condition: new. Hardcover. The first and only book to systematically address methodologies and processes of leveraging non-traditional information sources in the context of investing and risk management Harnessing non-traditional data sources to generate alpha, analyze markets, and forecast risk is a subject of intense interest for financial professionals. A growing number of regularly-held conferences on alternative data are being established, complemented by an upsurge in new papers on the subject. Alternative data is starting to be steadily incorporated by conventional institutional investors and risk managers throughout the financial world. Methodologies to analyze and extract value from alternative data, guidance on how to source data and integrate data flows within existing systems is currently not treated in literature. Filling this significant gap in knowledge, The Book of Alternative Data is the first and only book to offer a coherent, systematic treatment of the subject. This groundbreaking volume provides readers with a roadmap for navigating the complexities of an array of alternative data sources, and delivers the appropriate techniques to analyze them. The authorsleading experts in financial modeling, machine learning, and quantitative research and analyticsemploy a step-by-step approach to guide readers through the dense jungle of generated data. A first-of-its kind treatment of alternative data types, sources, and methodologies, this innovative book: Provides an integrated modeling approach to extract value from multiple types of datasetsTreats the processes needed to make alternative data signals operationalHelps investors and risk managers rethink how they engage with alternative datasetsFeatures practical use case studies in many different financial markets and real-world techniquesDescribes how to avoid potential pitfalls and missteps in starting the alternative data journeyExplains how to integrate information from different datasets to maximize informational value The Book of Alternative Data is an indispensable resource for anyone wishing to analyze or monetize different non-traditional datasets, including Chief Investment Officers, Chief Risk Officers, risk professionals, investment professionals, traders, economists, and machine learning developers and users. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9781119601791
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