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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.
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.
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.
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.
This book looks at Wall Street wonders Warren Buffet, Benjamin Graham, and other legends and shares how you can utilize their secrets to unimaginable success! It’s time to put your money to work the smart way and stop chasing quick payoffs that never turn out. That seductive stock tip you just overheard? That’s your ticket to flushing your savings down the toilet. The story you saw on a promising new product? Only those who invested before the story came out have any chance of a solid payout. If you want to succeed in the market, you need to learn how to invest based on value, selecting stocks that will continue to enrich you for years to come. By learning the keys to value investing, Money Machine will teach you how to: Judge a stock by the cash it generates Determine the stock’s intrinsic value Use key investment benchmarks such as price-earnings ratio and dividend-price ratio Recognize stock market bubbles and profit from panics Avoid psychological traps that can trip you up Investing in the market doesn’t have to be reckless speculation. Invest in value, not ventures, and find the financial success all those gamblers are still looking for!
"This is a very important book."--Martin Wolf, Financial TimesA provocative look at how today's trade conflicts are caused by governments promoting the interests of elites at the expense of workers Longlisted for the 2020 Financial Times & McKinsey Business Book of the Year Award "Worth reading for [the authors'] insights into the history of trade and finance."--George Melloan, Wall Street Journal Trade disputes are usually understood as conflicts between countries with competing national interests, but as Matthew C. Klein and Michael Pettis show, they are often the unexpected result of domestic political choices to serve the interests of the rich at the expense of workers and ordinary retirees. Klein and Pettis trace the origins of today's trade wars to decisions made by politicians and business leaders in China, Europe, and the United States over the past thirty years. Across the world, the rich have prospered while workers can no longer afford to buy what they produce, have lost their jobs, or have been forced into higher levels of debt. In this thought-provoking challenge to mainstream views, the authors provide a cohesive narrative that shows how the class wars of rising inequality are a threat to the global economy and international peace--and what we can do about it.
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.
Learn to understand and implement the latest machine learning innovations to improve your investment performance Machine learning (ML) is changing virtually every aspect of our lives. Today, ML algorithms accomplish tasks that – until recently – only expert humans could perform. And finance is ripe for disruptive innovations that will transform how the following generations understand money and invest. In the book, readers will learn how to: Structure big data in a way that is amenable to ML algorithms Conduct research with ML algorithms on big data Use supercomputing methods and back test their discoveries while avoiding false positives Advances in Financial Machine Learning addresses real life problems faced by practitioners every day, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their individual 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.
In An Engine, Not a Camera, Donald MacKenzie argues that the emergence of modern economic theories of finance affected financial markets in fundamental ways. These new, Nobel Prize-winning theories, based on elegant mathematical models of markets, were not simply external analyses but intrinsic parts of economic processes. Paraphrasing Milton Friedman, MacKenzie says that economic models are an engine of inquiry rather than a camera to reproduce empirical facts. More than that, the emergence of an authoritative theory of financial markets altered those markets fundamentally. For example, in 1970, there was almost no trading in financial derivatives such as "futures." By June of 2004, derivatives contracts totaling $273 trillion were outstanding worldwide. MacKenzie suggests that this growth could never have happened without the development of theories that gave derivatives legitimacy and explained their complexities. MacKenzie examines the role played by finance theory in the two most serious crises to hit the world's financial markets in recent years: the stock market crash of 1987 and the market turmoil that engulfed the hedge fund Long-Term Capital Management in 1998. He also looks at finance theory that is somewhat beyond the mainstream—chaos theorist Benoit Mandelbrot's model of "wild" randomness. MacKenzie's pioneering work in the social studies of finance will interest anyone who wants to understand how America's financial markets have grown into their current form.
This book shows how current and recent market prices convey information about the probability distributions that govern future prices. Moving beyond purely theoretical models, Stephen Taylor applies methods supported by empirical research of equity and foreign exchange markets to show how daily and more frequent asset prices, and the prices of option contracts, can be used to construct and assess predictions about future prices, their volatility, and their probability distributions. Stephen Taylor provides a comprehensive introduction to the dynamic behavior of asset prices, relying on finance theory and statistical evidence. He uses stochastic processes to define mathematical models for price dynamics, but with less mathematics than in alternative texts. The key topics covered include random walk tests, trading rules, ARCH models, stochastic volatility models, high-frequency datasets, and the information that option prices imply about volatility and distributions. Asset Price Dynamics, Volatility, and Prediction is ideal for students of economics, finance, and mathematics who are studying financial econometrics, and will enable researchers to identify and apply appropriate models and methods. It will likewise be a valuable resource for quantitative analysts, fund managers, risk managers, and investors who seek realistic expectations about future asset prices and the risks to which they are exposed.