Machine Learning for Financial Risk Management with Python

In Hong Kong, the banking system is the primary source of financial stability risk. Post-2008 regulatory reforms have focused on financial stability policies and tools while neglecting the design of supervisory models. This book provides a comparative analysis of how supervisory models affect the management of financial stability regulations in Hong Kong's banking system. Regulatory issues discussed span prudential regulations, systemically important banks, unconventional liquidity tools, deposit insurance, lender of last resort, resolution regimes, central clearing counterparties and derivatives, Renminbi infrastructure, stock and bond connect schemes, distributed ledger technology, digital yuan, US dollar sanctions, cryptocurrencies, RegTech, and FinTech. A Regulatory Design for Financial Stability in Hong Kong elucidates the flaws and synergies in Hong Kong's banking regulatory framework and proposes conventional and innovative regulatory reforms. This book will be of great interest

Augment your knowledge of Python with this entertaining learning guide, which features 100 exercises and programming puzzles and solutions. Python Challenges will help prepare you for your next exam or a job interview, and covers numerous practical topics such as strings, data structures, recursion, arrays, and more.Each topic is addressed in its own separate chapter, starting with an introduction to the basics and followed by 10 to 15 exercises of various degrees of difficulty, helping you to improve your programming skills effectively. Detailed sample solutions, including the algorithms used for all tasks, are included to maximize your understanding of each area. Author Michael Inden also describes alternative solutions and analyzes possible pitfalls and typical errors.Three appendices round out the book: the first covers the Python command line interpreter, which is often helpful for trying out the code snippets and examples in the book, followed by an overview of Pytest for unit te

Safety oil and gas engineers lack a single source to utilize for practical understanding and application and do not receive proper training until already placed on a job site. Risk Management in the Oil and Gas Industry: Offshore and Onshore Concepts and Case Studies delivers the concepts, strategies and good practices of offshore and onshore safety engineering applicable to petroleum engineering and immediately surrounding industries. Guided by the strategic risk management line, this reference organizes steps in order of importance and priority that should be given to the themes in the practical exercise of risk management activities, from the conceptual and design phase to operational and crisis management situations. Each chapter is packed with practical case studies, lessons learned, exercises, and review questions. The reference also touches on the newest techniques including liquefied natural gas (cryogenics) operations and computer simulations that contemplate the influence of

Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil and gas use cases. Petroleum engineers will learn when to use machine learning, how it is already used in oil and gas operations, and how to manage the data stream moving forward. Practical in its approach, the book explains all aspects of a data science or machine learning project, including the managerial parts of it that are so often the cause for failure.Several real-life case studies round out the book with topics such as predictive maintenance, soft sensing, and forecasting. Viewed as a guide book, this manual will lead a practitioner through the journey of a data science project in the oil and gas industry circumventing the pitfalls and articulating the business value.

The world of machine learning is changing all the time. It is so amazing the idea that we are able to take a computer and let it learn as it goes. Without having to write out all of the codes that we need for every situation out there or every input that the user may pick, we are able to write out codes in machine learning, even with Python, in order to let the computer or device learn and make decisions on its own.This guidebook is going to take a closer look at how Python machine learning is able to work, as well as how you can use some of the tools and techniques that come with this process for your own needs. When you are interested in learning more about what machine learning is all about, as well as how you can use a part of the coding from Python inside of this process, then this guidebook is the tool for you Some of the topics that we will explore when we go through this guidebook will include: Understanding some of the basics of machine learning;Some of the different parts tha

A project-based approach to learning Python programming for beginners. Intriguing projects teach you how to tackle challenging problems with code.You've mastered the basics. Now you're ready to explor

AI framework intended to solve a problem of bias-variance tradeoff for supervised learning methods in real-life applications. It comprises of bootstrapping to create multiple training and testing

Deep learning is revolutionizing how machine translation systems are built today. This book introduces the challenge of machine translation and evaluation - including historical, linguistic, and applied context -- then develops the core deep learning methods used for natural language applications. Code examples in Python give readers a hands-on blueprint for understanding and implementing their own machine translation systems. The book also provides extensive coverage of machine learning tricks, issues involved in handling various forms of data, model enhancements, and current challenges and methods for analysis and visualization. Summaries of the current research in the field make this a state-of-the-art textbook for undergraduate and graduate classes, as well as an essential reference for researchers and developers interested in other applications of neural methods in the broader field of human language processing.

Transfer learning deals with how systems can quickly adapt themselves to new situations, tasks and environments. It gives machine learning systems the ability to leverage auxiliary data and models to help solve target problems when there is only a small amount of data available. This makes such systems more reliable and robust, keeping the machine learning model faced with unforeseeable changes from deviating too much from expected performance. At an enterprise level, transfer learning allows knowledge to be reused so experience gained once can be repeatedly applied to the real world. For example, a pre-trained model that takes account of user privacy can be downloaded and adapted at the edge of a computer network. This self-contained, comprehensive reference text describes the standard algorithms and demonstrates how these are used in different transfer learning paradigms. It offers a solid grounding for newcomers as well as new insights for seasoned researchers and developers.

This textbook explains Deep Learning Architecture, with applications to various NLP Tasks, including Document Classification, Machine Translation, Language Modeling, and Speech Recognition. With the w

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Modern NLP techniques based on machine learning radically improve the ability of software to recognize patterns, use context to infer meaning, and accurately discern intent from poorly-structured text

Analysis for Financial Management, 12e presents standard techniques and modern developments in a practical and intuitive manner with an emphasis on the managerial applications of financial analysis.

In a world cluttered with messages competing for people’s attention all of the time, marketers must surface relevant information if they want to capture the attention of their consumers or busin

In the years since the subprime financial crisis of 2007–2011, we have learned a number of important lessons about the crisis, and have subsequently applied appropriate legislation, such as increased capital ratios and systematic stress testing, in order to combat it. However, it would be naive to suggest that such measures have put an end to the possibility of future crises. In this book, senior figures in economics, risk Management, and the banking sector use active research and policy debates to offer a wide perspective on what the next financial crisis may look like and what can be done about it from a regulatory point of view. By first exploring issues of macroeconomic policy, and then studying cutting-edge methodologies, challenging important aspects of testing financial practice, this book will be an essential read for all those studying and researching financial crises, financial regulation and macroprudential policy-making.

Real-world data sets are messy and complicated. Written for students in social science and public management, this authoritative but approachable guide describes all the tools needed to collect data and prepare it for analysis. Offering detailed, step-by-step instructions, it covers collection of many different types of data including web files, APIs, and maps; data cleaning; data formatting; the integration of different sources into a comprehensive data set; and storage using third-party tools to facilitate access and shareability, from Google Docs to GitHub. Assuming no prior knowledge of R and Python, the author introduces programming concepts gradually, using real data sets that provide the reader with practical, functional experience.

Real-world data sets are messy and complicated. Written for students in social science and public management, this authoritative but approachable guide describes all the tools needed to collect data and prepare it for analysis. Offering detailed, step-by-step instructions, it covers collection of many different types of data including web files, APIs, and maps; data cleaning; data formatting; the integration of different sources into a comprehensive data set; and storage using third-party tools to facilitate access and shareability, from Google Docs to GitHub. Assuming no prior knowledge of R and Python, the author introduces programming concepts gradually, using real data sets that provide the reader with practical, functional experience.

This book explores the accounting and regulatory framework associated with the acquisition and disposal of financial instruments; how to determine their value; how to manage the risk connected with th

With the sixteenth edition of Financial and Managerial Accounting: The Basis for Business Decisions, the Williams author team continues to be a solid foundation for students who are learning basic acc

Today’s healthcare supervisors are continually faced with smaller budgets, fewer workers, and greater responsibilities. The seventh edition of Umiker’s Management Skills for the New Health Care Superv