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:
- Provides an integrated modeling approach to extract value from multiple types of datasets
- Treats the processes needed to make alternative data signals operational
- Helps investors and risk managers rethink how they engage with alternative datasets
- Features practical use case studies in many different financial markets and real-world techniques
- Describes how to avoid potential pitfalls and missteps in starting the alternative data journey
- Explains 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.
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.
