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The Volatility Machine

The Volatility Machine
Author: Michael Pettis
Publisher: Oxford University Press, USA
Total Pages: 266
Release: 2001
Genre: Capital
ISBN: 0195143302

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This book presents a radically different argument for what has caused, and likely will continue to cause, the collapse of emerging market economies. Pettis combines the insights of economic history, economic theory, and finance theory into a comprehensive model for understanding sovereign liability management and the causes of financial crises. He examines recent financial crises in emerging market countries along with the history of international lending since the 1820s to argue that the process of international lending is driven primarily by external events and not by local politics and/or economic policies. He draws out the corporate finance implications of this approach to argue that most of the current analyses of the recent financial crises suffered by Latin America, Asia, and Russia have largely missed the point. He then develops a sovereign finance model, analogous to corporate finance, to understand the capital structure needs of emerging market countries. Using this model, he finally puts into perspective the recent crises, a new sovereign liability management theory, the implications of the model for sovereign debt restructurings, and the new financial architecture. Bridging the gap between finance specialists and traders, on the one hand, and economists and policy-makers on the other, The Volatility Machine is critical reading for anyone interested in where the international economy is going over the next several years.


The Volatility Machine : Emerging Economics and the Threat of Financial Collapse

The Volatility Machine : Emerging Economics and the Threat of Financial Collapse
Author: Michael Pettis Adjunct Professor Columbia University
Publisher: Oxford University Press, USA
Total Pages: 272
Release: 2001-04-23
Genre: Business & Economics
ISBN: 0195349482

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This book presents a radically different argument for what has caused, and likely will continue to cause, the collapse of emerging market economies. Pettis combines the insights of economic history, economic theory, and finance theory into a comprehensive model for understanding sovereign liability management and the causes of financial crises. He examines recent financial crises in emerging market countries along with the history of international lending since the 1820s to argue that the process of international lending is driven primarily by external events and not by local politics and/or economic policies. He draws out the corporate finance implications of this approach to argue that most of the current analyses of the recent financial crises suffered by Latin America, Asia, and Russia have largely missed the point. He then develops a sovereign finance model, analogous to corporate finance, to understand the capital structure needs of emerging market countries. Using this model, he finally puts into perspective the recent crises, a new sovereign liability management theory, the implications of the model for sovereign debt restructurings, and the new financial architecture. Bridging the gap between finance specialists and traders, on the one hand, and economists and policy-makers on the other, The Volatility Machine is critical reading for anyone interested in where the international economy is going over the next several years.


The Volatility Machine

The Volatility Machine
Author: Michael Pettis
Publisher: Oxford University Press
Total Pages: 266
Release: 2001-05-17
Genre: Business & Economics
ISBN: 0195349482

Download The Volatility Machine Book in PDF, ePub and Kindle

This book presents a radically different argument for what has caused, and likely will continue to cause, the collapse of emerging market economies. Pettis combines the insights of economic history, economic theory, and finance theory into a comprehensive model for understanding sovereign liability management and the causes of financial crises. He examines recent financial crises in emerging market countries along with the history of international lending since the 1820s to argue that the process of international lending is driven primarily by external events and not by local politics and/or economic policies. He draws out the corporate finance implications of this approach to argue that most of the current analyses of the recent financial crises suffered by Latin America, Asia, and Russia have largely missed the point. He then develops a sovereign finance model, analogous to corporate finance, to understand the capital structure needs of emerging market countries. Using this model, he finally puts into perspective the recent crises, a new sovereign liability management theory, the implications of the model for sovereign debt restructurings, and the new financial architecture. Bridging the gap between finance specialists and traders, on the one hand, and economists and policy-makers on the other, The Volatility Machine is critical reading for anyone interested in where the international economy is going over the next several years.


The Great Rebalancing

The Great Rebalancing
Author: Michael Pettis
Publisher: Princeton University Press
Total Pages: 256
Release: 2014-10-26
Genre: Business & Economics
ISBN: 0691163626

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How trade imbalances spurred on the global financial crisis and why we aren't out of trouble yet China's economic growth is sputtering, the Euro is under threat, and the United States is combating serious trade disadvantages. Another Great Depression? Not quite. Noted economist and China expert Michael Pettis argues instead that we are undergoing a critical rebalancing of the world economies. Debunking popular misconceptions, Pettis shows that severe trade imbalances spurred on the recent financial crisis and were the result of unfortunate policies that distorted the savings and consumption patterns of certain nations. Pettis examines the reasons behind these destabilizing policies, and he predicts severe economic dislocations that will have long-lasting effects. Demonstrating how economic policies can carry negative repercussions the world over, The Great Rebalancing sheds urgent light on our globally linked economic future.


Advances in Financial Machine Learning

Advances in Financial Machine Learning
Author: Marcos Lopez de Prado
Publisher: John Wiley & Sons
Total Pages: 400
Release: 2018-01-23
Genre: Business & Economics
ISBN: 1119482119

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Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.


Avoiding the Fall

Avoiding the Fall
Author: Michael Pettis
Publisher: Brookings Institution Press
Total Pages: 172
Release: 2013-09-24
Genre: Political Science
ISBN: 0870034081

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The days of rapid economic growth in China are over. Mounting debt and rising internal distortions mean that rebalancing is inevitable. Beijing has no choice but to take significant steps to restructure its economy. The only question is how to proceed. Michael Pettis debunks the lingering bullish expectations for China's economic rise and details Beijing's options. The urgent task of shifting toward greater domestic consumption will come with political costs, but Beijing must increase household income and reduce its reliance on investment to avoid a fall.


Dark Pools

Dark Pools
Author: Scott Patterson
Publisher: Crown Currency
Total Pages: 386
Release: 2012-06-12
Genre: Business & Economics
ISBN: 0307887197

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A news-breaking account of the global stock market's subterranean battles, Dark Pools portrays the rise of the "bots"--artificially intelligent systems that execute trades in milliseconds and use the cover of darkness to out-maneuver the humans who've created them. In the beginning was Josh Levine, an idealistic programming genius who dreamed of wresting control of the market from the big exchanges that, again and again, gave the giant institutions an advantage over the little guy. Levine created a computerized trading hub named Island where small traders swapped stocks, and over time his invention morphed into a global electronic stock market that sent trillions in capital through a vast jungle of fiber-optic cables. By then, the market that Levine had sought to fix had turned upside down, birthing secretive exchanges called dark pools and a new species of trading machines that could think, and that seemed, ominously, to be slipping the control of their human masters. Dark Pools is the fascinating story of how global markets have been hijacked by trading robots--many so self-directed that humans can't predict what they'll do next.


Rule Based Investing

Rule Based Investing
Author: Chiente Hsu
Publisher: Pearson Education
Total Pages: 188
Release: 2014
Genre: Business & Economics
ISBN: 0133354342

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Use rule-based investment strategies to maintain trading and investment discipline, and protect yourself from fear, greed, pride, and other costly emotions! Since the mid-1990s, assets under management in rule-based or non-discretionary hedge funds have outgrown those in discretionary or qualitative funds. Recent research shows that rule-based funds have outperformed discretionary funds on a risk-adjusted basis over the past 30 years, and have especially outperformed during recent financial crises. This is the first comprehensive guide to designing and applying these sophisticated strategies. Combining academic rigor and practical applications, it explains what rule-based investment strategies are, how to construct them, and how to distinguish bad ones from good ones. Unlike any other guide, it systematically covers every facet of the topic, including Forex, rates, emerging markets, equity, volatility, and other key topics. Credit Suisse head of global strategy and modeling, Chiente Hsu, covers carry, momentum, seasonality, and value-based strategies; as well as the construction of portfolios of rule-based strategies that support diversification. Replete with realistic examples, this book will be a valuable resource for everyone concerned with effective investing, from traders to specialists in applied corporate finance.


Machine Learning in Finance

Machine Learning in Finance
Author: Matthew F. Dixon
Publisher: Springer Nature
Total Pages: 565
Release: 2020-07-01
Genre: Business & Economics
ISBN: 3030410684

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This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.


Machine Learning in Insurance

Machine Learning in Insurance
Author: Jens Perch Nielsen
Publisher: MDPI
Total Pages: 260
Release: 2020-12-02
Genre: Business & Economics
ISBN: 3039364472

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Machine learning is a relatively new field, without a unanimous definition. In many ways, actuaries have been machine learners. In both pricing and reserving, but also more recently in capital modelling, actuaries have combined statistical methodology with a deep understanding of the problem at hand and how any solution may affect the company and its customers. One aspect that has, perhaps, not been so well developed among actuaries is validation. Discussions among actuaries’ “preferred methods” were often without solid scientific arguments, including validation of the case at hand. Through this collection, we aim to promote a good practice of machine learning in insurance, considering the following three key issues: a) who is the client, or sponsor, or otherwise interested real-life target of the study? b) The reason for working with a particular data set and a clarification of the available extra knowledge, that we also call prior knowledge, besides the data set alone. c) A mathematical statistical argument for the validation procedure.