This book, unique in its composition, reviews the academic empirical literature on how CDSs actually work in practice, including during distressed times of market crises. It also discusses the mechanics of single-name and index CDSs, the theoretical costs and benefits of CDSs, as well as comprehensively summarizes the empirical evidence on important aspects of these instruments of risk transfer. Full-time academics, researchers at financial institutions, and students will benefit from the dispassionate and comprehensive summary of the academic literature; they can read this book instead of identifying, collecting, and reading the hundreds of academic articles on the important subject of credit risk transfer using derivatives and benefit from the synthesis of the literature provided.

This book introduces a state-of-art approach in evaluating portfolio management and risk based on artificial intelligence and alternative data. The book covers a textual analysis of news and social media, information extraction from GPS and IoTs data, and risk predictions based on small transaction data, etc. The book summarizes and introduces the advancement in each area and highlights the machine learning and deep learning techniques utilized to achieve the goals. As a complement, it also illustrates examples on how to leverage the python package to visualize and analyze the alternative datasets, and will be of interest to academics, researchers, and students of risk evaluation, risk management, data, AI, and financial innovation.

This book introduces a state-of-art approach in evaluating portfolio management and risk based on artificial intelligence and alternative data. The book covers a textual analysis of news and social media, information extraction from GPS and IoTs data, and risk predictions based on small transaction data, etc. The book summarizes and introduces the advancement in each area and highlights the machine learning and deep learning techniques utilized to achieve the goals. As a complement, it also illustrates examples on how to leverage the python package to visualize and analyze the alternative datasets, and will be of interest to academics, researchers, and students of risk evaluation, risk management, data, AI, and financial innovation.

This book, unique in its composition, reviews the academic empirical literature on how CDSs actually work in practice, including during distressed times of market crises. It also discusses the mechanics of single-name and index CDSs, the theoretical costs and benefits of CDSs, as well as comprehensively summarizes the empirical evidence on important aspects of these instruments of risk transfer. Full-time academics, researchers at financial institutions, and students will benefit from the dispassionate and comprehensive summary of the academic literature; they can read this book instead of identifying, collecting, and reading the hundreds of academic articles on the important subject of credit risk transfer using derivatives and benefit from the synthesis of the literature provided.

This book introduces a state-of-art approach in evaluating portfolio management and risk based on artificial intelligence and alternative data. The book covers a textual analysis of news and social media, information extraction from GPS and IoTs data, and risk predictions based on small transaction data, etc. The book summarizes and introduces the advancement in each area and highlights the machine learning and deep learning techniques utilized to achieve the goals. As a complement, it also illustrates examples on how to leverage the python package to visualize and analyze the alternative datasets, and will be of interest to academics, researchers, and students of risk evaluation, risk management, data, AI, and financial innovation.

This book introduces a state-of-art approach in evaluating portfolio management and risk based on artificial intelligence and alternative data. The book covers a textual analysis of news and social media, information extraction from GPS and IoTs data, and risk predictions based on small transaction data, etc. The book summarizes and introduces the advancement in each area and highlights the machine learning and deep learning techniques utilized to achieve the goals. As a complement, it also illustrates examples on how to leverage the python package to visualize and analyze the alternative datasets, and will be of interest to academics, researchers, and students of risk evaluation, risk management, data, AI, and financial innovation.