The financial services industry is undergoing significant change. This has added challenges for institutions assessing their operations and internal controls for regulatory considerations. Updated for 2019, this industry standard resource offers comprehensive, reliable accounting implementation guidance for preparers. It offers clear and practical guidance of audit and accounting issues, and in-depth coverage of audit considerations, including controls, fraud, risk assessment, and planning and execution of the audit. Topics covered include: Transfers and servicing; Troubled debt restructurings; Financing receivables and the allowance for loan losses; and, Fair value accounting This guide also provides direction for institutions assessing their operations and internal controls for regulatory considerations as well as discussions on existing regulatory reporting matters. The financial services industry is undergoing significant change. This has added challenges for institutions assessing their operations and internal controls for regulatory considerations. Updated for 2019, this industry standard resource offers comprehensive, reliable accounting implementation guidance for preparers. It offers clear and practical guidance of audit and accounting issues, and in-depth coverage of audit considerations, including controls, fraud, risk assessment, and planning and execution of the audit. Topics covered include: Transfers and servicing; Troubled debt restructurings; Financing receivables and the allowance for loan losses; and, Fair value accounting This guide also provides direction for institutions assessing their operations and internal controls for regulatory considerations as well as discussions on existing regulatory reporting matters.

"Capital is the defining feature of modern economies, yet most people have no idea where it actually comes from. What is it, exactly, that transforms mere wealth into an asset that automatically creates more wealth? The Code of Capital explains how capital is created behind closed doors in the offices of private attorneys, and why this little-known fact is one of the biggest reasons for the widening wealth gap between the holders of capital and everybody else. In this revealing book, Katharina Pistor argues that the law selectively "codes" certain assets, endowing them with the capacity to protect and produce private wealth. With the right legal coding, any object, claim, or idea can be turned into capital - and lawyers are the keepers of the code. Pistor describes how they pick and choose among different legal systems and legal devices for the ones that best serve their clients' needs, and how techniques that were first perfected centuries ago to code landholdings as capital are being used today to code stocks, bonds, ideas, and even expectations--assets that exist only in law. A powerful new way of thinking about one of the most pernicious problems of our time, The Code of Capital explores the different ways that debt, complex financial products, and other assets are coded to give financial advantage to their holders. This provocative book paints a troubling portrait of the pervasive global nature of the code, the people who shape it, and the governments that enforce it."--Provided by publisher.

A behind-the-scenes account of the derivatives business at a major investment bank The financial industry's invention of complex products such as credit default swaps and other derivatives has been widely blamed for triggering the global financial crisis of 2008. In Codes of Finance, Vincent Antonin Lépinay, a former employee of one of the world’s leading investment banks, takes readers behind the scenes of the equity derivatives business at the bank before the crisis, providing a detailed firsthand account of the creation, marketing, selling, accounting, and management of these financial instruments—and of how they ultimately created havoc inside and outside the bank.

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.

The financial industry has recently adopted Python at a tremendous rate, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. Updated for Python 3, the second edition of this hands-on book helps you get started with the language, guiding developers and quantitative analysts through Python libraries and tools for building financial applications and interactive financial analytics. Using practical examples throughout the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks.