Alexander Denev

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 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.

Portfolio Management Under Stress offers a novel way to apply the well-established Bayesian-net methodology to the important problem of asset allocation under conditions of market distress or, more generally, when an investor believes that a particular scenario (such as the break-up of the Euro) may occur. Employing a coherent and thorough approach, it provides practical guidance on how best to choose an optimal and stable asset allocation in the presence of user specified scenarios or 'stress conditions'. The authors place causal explanations, rather than association-based measures such as correlations, at the core of their argument, and insights from the theory of choice under ambiguity aversion are invoked to obtain stable allocations results. Step-by-step design guidelines are included to allow readers to grasp the full implementation of the approach, and case studies provide clarification. This insightful book is a key resource for practitioners and research academics in the post-financial crisis world.