Advances in Financial Economics Vol. 16 contains a set of empirical papers by a set of global scholars who examine corporate governance and market regulation from a variety of perspectives.

This self-contained volume brings together a collection of chapters by some of the most distinguished researchers and practitioners in the field of mathematical finance and financial engineering. Presenting state-of-the-art developments in theory and practice, the book has real-world applications to fixed income models, credit risk models, CDO pricing, tax rebates, tax arbitrage, and tax equilibrium. It is a valuable resource for graduate students, researchers, and practitioners in mathematical finance and financial engineering.

Financial Economics and Econometrics provides an overview of the core topics in theoretical and empirical finance, with an emphasis on applications and interpreting results. Structured in five parts, the book covers financial data and univariate models; asset returns; interest rates, yields and spreads; volatility and correlation; and corporate finance and policy. Each chapter begins with a theory in financial economics, followed by econometric methodologies which have been used to explore the theory. Next, the chapter presents empirical evidence and discusses seminal papers on the topic. Boxes offer insights on how an idea can be applied to other disciplines such as management, marketing and medicine, showing the relevance of the material beyond finance. Readers are supported with plenty of worked examples and intuitive explanations throughout the book, while key takeaways, ‘test your knowledge’ and ‘test your intuition’ features at the end of each chapter also aid student learning. Digital supplements including PowerPoint slides, computer codes supplements, an Instructor’s Manual and Solutions Manual are available for instructors. This textbook is suitable for upper-level undergraduate and graduate courses on financial economics, financial econometrics, empirical finance and related quantitative areas.

An authoritative guide to the new economics of our crisis-filled century. Published in collaboration with the Institute for New Economic Thinking. The 2008 financial crisis was a seismic event that laid bare how financial institutions’ instabilities can have devastating effects on societies and economies. COVID-19 brought similar financial devastation at the beginning of 2020 and once more massive interventions by central banks were needed to heed off the collapse of the financial system. All of which begs the question: why is our financial system so fragile and vulnerable that it needs government support so often? For a generation of economists who have risen to prominence since 2008, these events have defined not only how they view financial instability, but financial markets more broadly. Leveraged brings together these voices to take stock of what we have learned about the costs and causes of financial fragility and to offer a new canonical framework for understanding it. Their message: the origins of financial instability in modern economies run deeper than the technical debates around banking regulation, countercyclical capital buffers, or living wills for financial institutions. Leveraged offers a fundamentally new picture of how financial institutions and societies coexist, for better or worse. The essays here mark a new starting point for research in financial economics. As we muddle through the effects of a second financial crisis in this young century, Leveraged provides a road map and a research agenda for the future.

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.

Modern financial markets offer the real world's best approximation to the idealized price auction market envisioned in economic theory. Nevertheless, as the increasingly exquisite and detailed financial data demonstrate, financial markets often fail to behave as they should if trading were truly dominated by the fully rational investors that populate financial theories. These markets anomalies have spawned a new approach to finance, one which as editor Richard Thaler puts it, "entertains the possibility that some agents in the economy behave less than fully rationally some of the time." Advances in Behavioral Finance collects together twenty-one recent articles that illustrate the power of this approach. These papers demonstrate how specific departures from fully rational decision making by individual market agents can provide explanations of otherwise puzzling market phenomena. To take several examples, Werner De Bondt and Thaler find an explanation for superior price performance of firms with poor recent earnings histories in the tendencies of investors to overreact to recent information. Richard Roll traces the negative effects of corporate takeovers on the stock prices of the acquiring firms to the overconfidence of managers, who fail to recognize the contributions of chance to their past successes. Andrei Shleifer and Robert Vishny show how the difficulty of establishing a reliable reputation for correctly assessing the value of long term capital projects can lead investment analysis, and hence corporate managers, to focus myopically on short term returns. As a testing ground for assessing the empirical accuracy of behavioral theories, the successful studies in this landmark collection reach beyond the world of finance to suggest, very powerfully, the importance of pursuing behavioral approaches to other areas of economic life. Advances in Behavioral Finance is a solid beachhead for behavioral work in the financial arena and a clear promise of wider application for behavioral economics in the future.

An innovative textbook for use in advanced undergraduate and graduate courses; accessible to students in financial mathematics, financial engineering and economics. Introduction to the Economics and Mathematics of Financial Markets fills the longstanding need for an accessible yet serious textbook treatment of financial economics. The book provides a rigorous overview of the subject, while its flexible presentation makes it suitable for use with different levels of undergraduate and graduate students. Each chapter presents mathematical models of financial problems at three different degrees of sophistication: single-period, multi-period, and continuous-time. The single-period and multi-period models require only basic calculus and an introductory probability/statistics course, while an advanced undergraduate course in probability is helpful in understanding the continuous-time models. In this way, the material is given complete coverage at different levels; the less advanced student can stop before the more sophisticated mathematics and still be able to grasp the general principles of financial economics. The book is divided into three parts. The first part provides an introduction to basic securities and financial market organization, the concept of interest rates, the main mathematical models, and quantitative ways to measure risks and rewards. The second part treats option pricing and hedging; here and throughout the book, the authors emphasize the Martingale or probabilistic approach. Finally, the third part examines equilibrium models—a subject often neglected by other texts in financial mathematics, but included here because of the qualitative insight it offers into the behavior of market participants and pricing.