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Machine Learning in Finance
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 t...
Adventures In Financial Data Science: The Empirical Properties Of Financial And Economic Data (Second Edition)
This book provides insights into the true nature of financial and economic data, and is a practical guide on how to analyze a variety of data sources. The focus of the book is on finance and economics, but it also illustrates the use of quantitative analysis and data science in many different areas. Lastly, the book includes practical information on how to store and process data and provides a framework for data driven reasoning about the world.The book begins with entertaining tales from Graham Giller's career in finance, starting with speculating in UK government bonds at the Oxford Post Office, accidentally creating a global instant messaging system that went 'viral' before anybody knew w...
Creators of Intelligence
Get your hands on the secret recipe for a rewarding career in data science from 18 AI leaders Purchase of the print or Kindle book includes a free PDF eBook Key Features Gain access to insights and expertise from data science leaders shared in one-on-one interviews Get pragmatic advice on how to become a successful data scientist and data science leader Receive guidance to overcome common pitfalls and challenges and ensure your projects’ success Book DescriptionA Gartner prediction in 2018 led to numerous articles stating that "85% of AI and machine learning projects fail to deliver.” Although it's unclear whether a mass extinction event occurred for AI implementations at the end of 2022...
Quantitative Finance with Python
Quantitative Finance with Python: A Practical Guide to Investment Management, Trading and Financial Engineering bridges the gap between the theory of mathematical finance and the practical applications of these concepts for derivative pricing and portfolio management. The book provides students with a very hands-on, rigorous introduction to foundational topics in quant finance, such as options pricing, portfolio optimization and machine learning. Simultaneously, the reader benefits from a strong emphasis on the practical applications of these concepts for institutional investors. Features Useful as both a teaching resource and as a practical tool for professional investors. Ideal textbook for first year graduate students in quantitative finance programs, such as those in master’s programs in Mathematical Finance, Quant Finance or Financial Engineering. Includes a perspective on the future of quant finance techniques, and in particular covers some introductory concepts of Machine Learning. Free-to-access repository with Python codes available at www.routledge.com/ 9781032014432 and on https://github.com/lingyixu/Quant-Finance-With-Python-Code.
Python for Finance
DESCRIPTION Python's intuitive syntax and beginner-friendly nature makes it an ideal programming language for financial professionals. It acts as a bridge between the world of finance and data analysis. This book will introduce essential concepts in financial analysis methods and models, covering time-series analysis, graphical analysis, technical and fundamental analysis, asset pricing and portfolio theory, investment and trade strategies, risk assessment and prediction, and financial ML practices. The Python programming language and its ecosystem libraries, such as Pandas, NumPy, SciPy, Statsmodels, Matplotlib, Seaborn, Scikit-learn, Prophet, and other data science tools will demonstrate t...
Machine Learning and Data Sciences for Financial Markets
Leveraging the research efforts of more than sixty experts in the area, this book reviews cutting-edge practices in machine learning for financial markets. Instead of seeing machine learning as a new field, the authors explore the connection between knowledge developed by quantitative finance over the past forty years and techniques generated by the current revolution driven by data sciences and artificial intelligence. The text is structured around three main areas: 'Interactions with investors and asset owners,' which covers robo-advisors and price formation; 'Risk intermediation,' which discusses derivative hedging, portfolio construction, and machine learning for dynamic optimization; and 'Connections with the real economy,' which explores nowcasting, alternative data, and ethics of algorithms. Accessible to a wide audience, this invaluable resource will allow practitioners to include machine learning driven techniques in their day-to-day quantitative practices, while students will build intuition and come to appreciate the technical tools and motivation for the theory.
Credit Risk Frontiers
A timely guide to understanding and implementing credit derivatives Credit derivatives are here to stay and will continue to play a role in finance in the future. But what will that role be? What issues and challenges should be addressed? And what lessons can be learned from the credit mess? Credit Risk Frontiers offers answers to these and other questions by presenting the latest research in this field and addressing important issues exposed by the financial crisis. It covers this subject from a real world perspective, tackling issues such as liquidity, poor data, and credit spreads, as well as the latest innovations in portfolio products and hedging and risk management techniques. Provides a coherent presentation of recent advances in the theory and practice of credit derivatives Takes into account the new products and risk requirements of a post financial crisis world Contains information regarding various aspects of the credit derivative market as well as cutting edge research regarding those aspects If you want to gain a better understanding of how credit derivatives can help your trading or investing endeavors, then Credit Risk Frontiers is a book you need to read.
Time-changed Birth Processes, Random Thinning, and Correlated Default Risk
Credit risk pervades all nancial transactions. The credit crisis has indicated the need for quantitative models for valuation, hedging, rating, risk management and regulatory monitoring of credit risk. A credit investor such as a bank granting loans to rms or an asset manager buying corporate bonds is exposed to correlated default risk. A portfolio credit derivative is a nancial security that allows the investor to transfer this risk to the credit market. In the rst part of this thesis, we study the valuation and risk analysis of portfolio derivatives. To capture the complex economic phenomena that drive the pricing of these securities, we introduce a time-changed birth process as a probabil...
B Physics And Cp Violation: Bcp4 - Proceedings Of The International Workshop
This book offers the first strong evidence of the existence of CP violation in neutral B decays extracted from sophisticated B factories in the US and Japan. It also holds out the expectation of rare B decays and D, K physics in the near future. In addition, new physics beyond the Standard Model is described. Both experimental and theoretical points of view are given.
BCP4
This book offers the first strong evidence of the existence of CP violation in neutral B decays extracted from sophisticated B factories in the US and Japan. It also holds out the expectation of rare B decays and D, K physics in the near future. In addition, new physics beyond the Standard Model is described. Both experimental and theoretical points of view are given.
