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.

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.

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.

Advances in Pacific Basin Business, Economics, and Finance is an annual publication designed to focus on interdisciplinary research in finance, economics, and management. It particularly emphasizes the economic, financial, and management relationships among Pacific Rim countries.

​Discussing a wide range of topics of contemporary relevance from the domain of finance and economics, this book presents a collection of twenty-four research papers, which were selected on the basis of their topicality, the novelty of their methods, and the importance of their subject matter. All papers pursue an empirical approach to address key research issues, and are categorized into three major parts. Part one includes papers related to development economics and environmental economics. The second part focuses on monetary economics, public economics, and behavioral economics, while the third tackles issues concerning corporate finance and financial risk management. Bringing together works of scholars from around the world, the book presents a truly global perspective, and not only serves as an essential guide on the topic for researchers, but also has a distinctive role to play in policymaking.

Some recent developments in the mathematics of optimization, including the concepts of invexity and quasimax, have not yet been applied to models of economic growth, and to finance and investment. Their applications to these areas are shown in this book.

This volume, in the series Advances in Financial Economics, discusses a variety of topics in the field.

Advances in Entrepreneurial Finance brings together contributions from researchers from the fields of entrepreneurship, behavioral finance, psychology, and neuroscience to shed new light on the dynamics of decision making and risk taking by entrepreneurs and venture capitalists (VCs). Every new venture requires access to capital at competitive interest rates, and much has been written on general entrepreneurship by management scholars and financial contracting by financial economists using traditional finance theory with all its highly restrictive assumptions regarding decision makers’ cognitive capabilities and behavior. But recent developments in behavioral finance can now be applied to understand how entrepreneurs and VCs perceive risk and uncertainty and how they decide and act accordingly. Showcasing the latest research, this volume demonstrates that findings from the behavioral and neuroscience arenas can and do explain decision making by entrepreneurs and venture investors in the real world. Consequently, such findings have practical implications not only for entrepreneurs, venture capitalists, and their advisors, but also all government agencies and NGOs that want to support product and technological innovation, capital formation, job creation, and economic development.

The past twenty years have seen an extraordinary growth in the use of quantitative methods in financial markets. Finance professionals now routinely use sophisticated statistical techniques in portfolio management, proprietary trading, risk management, financial consulting, and securities regulation. This graduate-level textbook is intended for PhD students, advanced MBA students, and industry professionals interested in the econometrics of financial modeling. The book covers the entire spectrum of empirical finance, including: the predictability of asset returns, tests of the Random Walk Hypothesis, the microstructure of securities markets, event analysis, the Capital Asset Pricing Model and the Arbitrage Pricing Theory, the term structure of interest rates, dynamic models of economic equilibrium, and nonlinear financial models such as ARCH, neural networks, statistical fractals, and chaos theory. Each chapter develops statistical techniques within the context of a particular financial application. This exciting new text contains a unique and accessible combination of theory and practice, bringing state-of-the-art statistical techniques to the forefront of financial applications. Each chapter also includes a discussion of recent empirical evidence, for example, the rejection of the Random Walk Hypothesis, as well as problems designed to help readers incorporate what they have read into their own applications.