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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.
4e de couv.: 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. Codes of Finance takes readers behind the scenes of the equity derivatives business at one of the world's leading investment banks 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. Vincent Antonin Lépinay, a former employee of the bank, investigates the journey of a derivative through the bank's front, middle, and back offices. In the process, he provides a rare look at the strange world of quants, traders, salespeople, accountants, and others involved in a self-annihilating form of life in which securities designed by the bank eventually threaten its infrastructure. Throughout, he tries to understand the baffling languages of engineered financial products and the often-conflicting bodies of expertise that are mobilized to create them. Codes of Finance highlights the massive costs of investment banking's hubristic dream of manufacturing global financial services that derive their value from multiple economies across the world. Yet the book challenges simplistic condemnations of financial engineering by showing that derivation is the central operator of economic life--stretching far beyond the phenomenon of financial derivatives themselves. Essential reading for economic sociologists and financial economists, as well as for readers curious to decipher modern finance, this is the first serious study of the intellectual and organizational puzzles raised by the controversial products of contemporary financial engineering.
"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.
If you are an undergraduate or graduate student, a beginner to algorithmic development and research, or a software developer in the financial industry who is interested in using Python for quantitative methods in finance, this is the book for you. It would be helpful to have a bit of familiarity with basic Python usage, but no prior experience is required.
" ... A modern tale of one person's journey to uncover the five secrets to living his one best financial life"--Page 4 of cover.
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.
This book introduces the reader to the C++ programming language and how to use it to write applications in quantitative finance (QF) and related areas. No previous knowledge of C or C++ is required -- experience with VBA, Matlab or other programming language is sufficient. The book adopts an incremental approach; starting from basic principles then moving on to advanced complex techniques and then to real-life applications in financial engineering. There are five major parts in the book: C++ fundamentals and object-oriented thinking in QF Advanced object-oriented features such as inheritance and polymorphism Template programming and the Standard Template Library (STL) An introduction to GOF design patterns and their applications in QF Applications The kinds of applications include binomial and trinomial methods, Monte Carlo simulation, advanced trees, partial differential equations and finite difference methods. This book includes a companion website with all source code and many useful C++ classes that you can use in your own applications. Examples, test cases and applications are directly relevant to QF. This book is the perfect companion to Daniel J. Duffy’s book Financial Instrument Pricing using C++ (Wiley 2004, 0470855096 / 9780470021620)
Before you can control your finances, save money, and get out of debt, you must first understand your spending habits. Sounds simple, right? But for most people it is not, because budgets are based on complicated monthly spending habits, which can be overwhelming, causing people to give up. Now, there's a different way that will revolutionize the way you look at personal finance. In an incredibly easy, comprehensible way, The Dollar Code shows you how to break down spending in order to pay off debt and achieve financial freedom--no matter how many other methods have failed you in the past. Jason R. Hastie's method is based on the principle of living within your means, but what makes it different is that it gives you just one number to remember—your own personal "Daily Digit"—the amount of money you can freely spend each day without going into debt. This one number is the key to financial freedom because it makes spending easy to understand, and when you understand spending, you can control it. Who will benefit from reading and applying The Dollar Code? Everyone who has ever identified with one or more of these statements: I can't understand why I don't have enough money at the end of the week. I've tried budgeting and failed because it was too complicated. I feel out of control of my finances. I get depressed about not having enough money. I have too much debt and feel like the world is caving in. I feel constantly bombarded by unexpected fees and expenses. Not only does Hastie's Dollar Code put those statements to rest, it also addresses a broad range of issues and scenarios that sometimes catch us off guard and, if we're not careful, throw us into a tailspin. Hastie also tackles head-on issues such as the benefits and pitfalls of credit cards, emergency funds, saving for the future, and "the fun bucket." His handy "twenty tips" and worksheets at the back of the book make applying the dollar code even easier and, once you unlock your code, you'll understand why it is the one and only number you need to achieve financial freedom. Really—it's that simple . . . and fun!
This book discusses the interplay of stochastics (applied probability theory) and numerical analysis in the field of quantitative finance. The stochastic models, numerical valuation techniques, computational aspects, financial products, and risk management applications presented will enable readers to progress in the challenging field of computational finance.When the behavior of financial market participants changes, the corresponding stochastic mathematical models describing the prices may also change. Financial regulation may play a role in such changes too. The book thus presents several models for stock prices, interest rates as well as foreign-exchange rates, with increasing complexity across the chapters. As is said in the industry, 'do not fall in love with your favorite model.' The book covers equity models before moving to short-rate and other interest rate models. We cast these models for interest rate into the Heath-Jarrow-Morton framework, show relations between the different models, and explain a few interest rate products and their pricing.The chapters are accompanied by exercises. Students can access solutions to selected exercises, while complete solutions are made available to instructors. The MATLAB and Python computer codes used for most tables and figures in the book are made available for both print and e-book users. This book will be useful for people working in the financial industry, for those aiming to work there one day, and for anyone interested in quantitative finance. The topics that are discussed are relevant for MSc and PhD students, academic researchers, and for quants in the financial industry.