Machine Learning For Risk Calculations

Machine Learning For Risk Calculations Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Machine Learning For Risk Calculations book. This book definitely worth reading, it is an incredibly well-written.

Machine Learning for Risk Calculations

Ignacio Ruiz,Mariano Zeron
Machine Learning for Risk Calculations Book PDF

Author : Ignacio Ruiz,Mariano Zeron
Publisher : John Wiley & Sons
Page : 471 pages
File Size : 47,6 Mb
Release : 2021-12-20
Category : Business & Economics
ISBN : 9781119791409

Get Book

Machine Learning for Risk Calculations by Ignacio Ruiz,Mariano Zeron Pdf

State-of-the-art algorithmic deep learning and tensoring techniques for financial institutions The computational demand of risk calculations in financial institutions has ballooned and shows no sign of stopping. It is no longer viable to simply add more computing power to deal with this increased demand. The solution? Algorithmic solutions based on deep learning and Chebyshev tensors represent a practical way to reduce costs while simultaneously increasing risk calculation capabilities. Machine Learning for Risk Calculations: A Practitioner’s View provides an in-depth review of a number of algorithmic solutions and demonstrates how they can be used to overcome the massive computational burden of risk calculations in financial institutions. This book will get you started by reviewing fundamental techniques, including deep learning and Chebyshev tensors. You’ll then discover algorithmic tools that, in combination with the fundamentals, deliver actual solutions to the real problems financial institutions encounter on a regular basis. Numerical tests and examples demonstrate how these solutions can be applied to practical problems, including XVA and Counterparty Credit Risk, IMM capital, PFE, VaR, FRTB, Dynamic Initial Margin, pricing function calibration, volatility surface parametrisation, portfolio optimisation and others. Finally, you’ll uncover the benefits these techniques provide, the practicalities of implementing them, and the software which can be used. Review the fundamentals of deep learning and Chebyshev tensors Discover pioneering algorithmic techniques that can create new opportunities in complex risk calculation Learn how to apply the solutions to a wide range of real-life risk calculations. Download sample code used in the book, so you can follow along and experiment with your own calculations Realize improved risk management whilst overcoming the burden of limited computational power Quants, IT professionals, and financial risk managers will benefit from this practitioner-oriented approach to state-of-the-art risk calculation.

Machine Learning for Financial Risk Management with Python

Abdullah Karasan
Machine Learning for Financial Risk Management with Python Book PDF

Author : Abdullah Karasan
Publisher : "O'Reilly Media, Inc."
Page : 334 pages
File Size : 48,9 Mb
Release : 2021-12-07
Category : Computers
ISBN : 9781492085201

Get Book

Machine Learning for Financial Risk Management with Python by Abdullah Karasan Pdf

Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, risk analysts, and quantitative and algorithmic analysts will examine Python-based machine learning and deep learning models for assessing financial risk. Building hands-on AI-based financial modeling skills, you'll learn how to replace traditional financial risk models with ML models. Author Abdullah Karasan helps you explore the theory behind financial risk modeling before diving into practical ways of employing ML models in modeling financial risk using Python. With this book, you will: Review classical time series applications and compare them with deep learning models Explore volatility modeling to measure degrees of risk, using support vector regression, neural networks, and deep learning Improve market risk models (VaR and ES) using ML techniques and including liquidity dimension Develop a credit risk analysis using clustering and Bayesian approaches Capture different aspects of liquidity risk with a Gaussian mixture model and Copula model Use machine learning models for fraud detection Predict stock price crash and identify its determinants using machine learning models

Mathematics for Machine Learning

Marc Peter Deisenroth,A. Aldo Faisal,Cheng Soon Ong
Mathematics for Machine Learning Book PDF

Author : Marc Peter Deisenroth,A. Aldo Faisal,Cheng Soon Ong
Publisher : Cambridge University Press
Page : 391 pages
File Size : 40,9 Mb
Release : 2020-04-23
Category : Computers
ISBN : 9781108470049

Get Book

Mathematics for Machine Learning by Marc Peter Deisenroth,A. Aldo Faisal,Cheng Soon Ong Pdf

Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.

Implementing Machine Learning for Finance

Tshepo Chris Nokeri
Implementing Machine Learning for Finance Book PDF

Author : Tshepo Chris Nokeri
Publisher : Apress
Page : 182 pages
File Size : 46,9 Mb
Release : 2021-05-27
Category : Computers
ISBN : 1484271092

Get Book

Implementing Machine Learning for Finance by Tshepo Chris Nokeri Pdf

Bring together machine learning (ML) and deep learning (DL) in financial trading, with an emphasis on investment management. This book explains systematic approaches to investment portfolio management, risk analysis, and performance analysis, including predictive analytics using data science procedures. The book introduces pattern recognition and future price forecasting that exerts effects on time series analysis models, such as the Autoregressive Integrated Moving Average (ARIMA) model, Seasonal ARIMA (SARIMA) model, and Additive model, and it covers the Least Squares model and the Long Short-Term Memory (LSTM) model. It presents hidden pattern recognition and market regime prediction applying the Gaussian Hidden Markov Model. The book covers the practical application of the K-Means model in stock clustering. It establishes the practical application of the Variance-Covariance method and Simulation method (using Monte Carlo Simulation) for value at risk estimation. It also includes market direction classification using both the Logistic classifier and the Multilayer Perceptron classifier. Finally, the book presents performance and risk analysis for investment portfolios. By the end of this book, you should be able to explain how algorithmic trading works and its practical application in the real world, and know how to apply supervised and unsupervised ML and DL models to bolster investment decision making and implement and optimize investment strategies and systems. What You Will Learn Understand the fundamentals of the financial market and algorithmic trading, as well as supervised and unsupervised learning models that are appropriate for systematic investment portfolio management Know the concepts of feature engineering, data visualization, and hyperparameter optimization Design, build, and test supervised and unsupervised ML and DL models Discover seasonality, trends, and market regimes, simulating a change in the market and investment strategy problems and predicting market direction and prices Structure and optimize an investment portfolio with preeminent asset classes and measure the underlying risk Who This Book Is For Beginning and intermediate data scientists, machine learning engineers, business executives, and finance professionals (such as investment analysts and traders)

Machine Learning for Financial Risk Management with Python

Abdullah Karasan
Machine Learning for Financial Risk Management with Python Book PDF

Author : Abdullah Karasan
Publisher : "O'Reilly Media, Inc."
Page : 334 pages
File Size : 55,6 Mb
Release : 2021-12-07
Category : Business & Economics
ISBN : 9781492085225

Get Book

Machine Learning for Financial Risk Management with Python by Abdullah Karasan Pdf

Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, and risk analysts will explore Python-based machine learning and deep learning models for assessing financial risk. You'll learn how to compare results from ML models with results obtained by traditional financial risk models. Author Abdullah Karasan helps you explore the theory behind financial risk assessment before diving into the differences between traditional and ML models. Review classical time series applications and compare them with deep learning models Explore volatility modeling to measure degrees of risk, using support vector regression, neural networks, and deep learning Revisit and improve market risk models (VaR and expected shortfall) using machine learning techniques Develop a credit risk based on a clustering technique for risk bucketing, then apply Bayesian estimation, Markov chain, and other ML models Capture different aspects of liquidity with a Gaussian mixture model Use machine learning models for fraud detection Identify corporate risk using the stock price crash metric Explore a synthetic data generation process to employ in financial risk.

Disrupting Finance

Theo Lynn,John G. Mooney,Pierangelo Rosati,Mark Cummins
Disrupting Finance Book PDF

Author : Theo Lynn,John G. Mooney,Pierangelo Rosati,Mark Cummins
Publisher : Springer
Page : 194 pages
File Size : 42,9 Mb
Release : 2018-12-06
Category : Business & Economics
ISBN : 9783030023300

Get Book

Disrupting Finance by Theo Lynn,John G. Mooney,Pierangelo Rosati,Mark Cummins Pdf

This open access Pivot demonstrates how a variety of technologies act as innovation catalysts within the banking and financial services sector. Traditional banks and financial services are under increasing competition from global IT companies such as Google, Apple, Amazon and PayPal whilst facing pressure from investors to reduce costs, increase agility and improve customer retention. Technologies such as blockchain, cloud computing, mobile technologies, big data analytics and social media therefore have perhaps more potential in this industry and area of business than any other. This book defines a fintech ecosystem for the 21st century, providing a state-of-the art review of current literature, suggesting avenues for new research and offering perspectives from business, technology and industry.

Risk Modeling

Terisa Roberts,Stephen J. Tonna
Risk Modeling Book PDF

Author : Terisa Roberts,Stephen J. Tonna
Publisher : John Wiley & Sons
Page : 214 pages
File Size : 47,5 Mb
Release : 2022-09-20
Category : Business & Economics
ISBN : 9781119824947

Get Book

Risk Modeling by Terisa Roberts,Stephen J. Tonna Pdf

A wide-ranging overview of the use of machine learning and AI techniques in financial risk management, including practical advice for implementation Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning introduces readers to the use of innovative AI technologies for forecasting and evaluating financial risks. Providing up-to-date coverage of the practical application of current modelling techniques in risk management, this real-world guide also explores new opportunities and challenges associated with implementing machine learning and artificial intelligence (AI) into the risk management process. Authors Terisa Roberts and Stephen Tonna provide readers with a clear understanding about the strengths and weaknesses of machine learning and AI while explaining how they can be applied to both everyday risk management problems and to evaluate the financial impact of extreme events such as global pandemics and changes in climate. Throughout the text, the authors clarify misconceptions about the use of machine learning and AI techniques using clear explanations while offering step-by-step advice for implementing the technologies into an organization's risk management model governance framework. This authoritative volume: Highlights the use of machine learning and AI in identifying procedures for avoiding or minimizing financial risk Discusses practical tools for assessing bias and interpretability of resultant models developed with machine learning algorithms and techniques Covers the basic principles and nuances of feature engineering and common machine learning algorithms Illustrates how risk modeling is incorporating machine learning and AI techniques to rapidly consume complex data and address current gaps in the end-to-end modelling lifecycle Explains how proprietary software and open-source languages can be combined to deliver the best of both worlds: for risk models and risk practitioners Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning is an invaluable guide for CEOs, CROs, CFOs, risk managers, business managers, and other professionals working in risk management.

Interpretable Machine Learning

Christoph Molnar
Interpretable Machine Learning Book PDF

Author : Christoph Molnar
Publisher : Lulu.com
Page : 320 pages
File Size : 52,8 Mb
Release : 2020
Category : Artificial intelligence
ISBN : 9780244768522

Get Book

Interpretable Machine Learning by Christoph Molnar Pdf

This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance

El Bachir Boukherouaa,Mr. Ghiath Shabsigh,Khaled AlAjmi,Jose Deodoro,Aquiles Farias,Ebru S Iskender,Mr. Alin T Mirestean,Rangachary Ravikumar
Powering the Digital Economy Opportunities and Risks of Artificial Intelligence in Finance Book PDF

Author : El Bachir Boukherouaa,Mr. Ghiath Shabsigh,Khaled AlAjmi,Jose Deodoro,Aquiles Farias,Ebru S Iskender,Mr. Alin T Mirestean,Rangachary Ravikumar
Publisher : International Monetary Fund
Page : 35 pages
File Size : 44,5 Mb
Release : 2021-10-22
Category : Business & Economics
ISBN : 9781589063952

Get Book

Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance by El Bachir Boukherouaa,Mr. Ghiath Shabsigh,Khaled AlAjmi,Jose Deodoro,Aquiles Farias,Ebru S Iskender,Mr. Alin T Mirestean,Rangachary Ravikumar Pdf

This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.

Understanding Machine Learning

Shai Shalev-Shwartz,Shai Ben-David
Understanding Machine Learning Book PDF

Author : Shai Shalev-Shwartz,Shai Ben-David
Publisher : Cambridge University Press
Page : 415 pages
File Size : 40,5 Mb
Release : 2014-05-19
Category : Computers
ISBN : 9781107057135

Get Book

Understanding Machine Learning by Shai Shalev-Shwartz,Shai Ben-David Pdf

Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

Artificial Intelligence in Asset Management

Söhnke M. Bartram,Jürgen Branke,Mehrshad Motahari
Artificial Intelligence in Asset Management Book PDF

Author : Söhnke M. Bartram,Jürgen Branke,Mehrshad Motahari
Publisher : CFA Institute Research Foundation
Page : 95 pages
File Size : 46,8 Mb
Release : 2020-08-28
Category : Business & Economics
ISBN : 9781952927034

Get Book

Artificial Intelligence in Asset Management by Söhnke M. Bartram,Jürgen Branke,Mehrshad Motahari Pdf

Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from new data sources. Finally, robo-advisors owe a large part of their success to AI techniques. Yet the use of AI can also create new risks and challenges, such as those resulting from model opacity, complexity, and reliance on data integrity.

Machine Learning in Finance

Matthew F. Dixon,Igor Halperin,Paul Bilokon
Machine Learning in Finance Book PDF

Author : Matthew F. Dixon,Igor Halperin,Paul Bilokon
Publisher : Springer Nature
Page : 565 pages
File Size : 53,8 Mb
Release : 2020-07-01
Category : Business & Economics
ISBN : 9783030410681

Get Book

Machine Learning in Finance by Matthew F. Dixon,Igor Halperin,Paul Bilokon Pdf

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.

Machine Learning for Financial Risk Management with Python

Abdullah Karasan
Machine Learning for Financial Risk Management with Python Book PDF

Author : Abdullah Karasan
Publisher : O'Reilly Media
Page : 350 pages
File Size : 42,9 Mb
Release : 2022-01-18
Category : Electronic
ISBN : 1492085251

Get Book

Machine Learning for Financial Risk Management with Python by Abdullah Karasan Pdf

Financial risk management is quickly evolving with the help of artificial intelligence. With this practical book, developers, programmers, engineers, financial analysts, and risk analysts will explore Python-based machine learning and deep learning models for assessing financial risk. You'll learn how to compare results from ML models with results obtained by traditional financial risk models. Author Abdullah Karasan helps you explore the theory behind financial risk assessment before diving into the differences between traditional and ML models. Review classical time series applications and compare them with deep learning models Explore volatility modeling to measure degrees of risk, using support vector regression, neural networks, and deep learning Revisit and improve market risk models (VaR and expected shortfall) using machine learning techniques Develop a credit risk based on a clustering technique for risk bucketing, then apply Bayesian estimation, Markov chain, and other ML models Capture different aspects of liquidity with a Gaussian mixture model Use machine learning models for fraud detection Identify corporate risk using the stock price crash metric Explore a synthetic data generation process to employ in financial risk

Causal Analytics for Applied Risk Analysis

Louis Anthony Cox Jr.,Douglas A. Popken,Richard X. Sun
Causal Analytics for Applied Risk Analysis Book PDF

Author : Louis Anthony Cox Jr.,Douglas A. Popken,Richard X. Sun
Publisher : Springer
Page : 588 pages
File Size : 49,7 Mb
Release : 2018-06-19
Category : Business & Economics
ISBN : 9783319782423

Get Book

Causal Analytics for Applied Risk Analysis by Louis Anthony Cox Jr.,Douglas A. Popken,Richard X. Sun Pdf

Causal analytics methods can revolutionize the use of data to make effective decisions by revealing how different choices affect probabilities of various outcomes. This book presents and illustrates models, algorithms, principles, and software for deriving causal models from data and for using them to optimize decisions with uncertain outcomes. It discusses how to describe and summarize situations; detect changes; evaluate effects of policies or interventions; learn what works best under different conditions; predict values of as-yet unobserved quantities from available data; and identify the most likely explanations for observed outcomes, including surprises and anomalies. The book resents practical techniques for causal modeling and analytics that practitioners can apply to improve understanding of how choices affect probabilities of consequences and, based on this understanding, to recommend choices that are more likely to accomplish their intended objectives.The book begins with a survey of modern analytics methods, focusing mainly on techniques useful for decision, risk, and policy analysis. Chapter 2 introduces free in-browser software, including the Causal Analytics Toolkit (CAT) software, to enable readers to perform the analyses described and to apply modern analytics methods easily to their own data sets. Chapters 3 through 11 show how to apply causal analytics and risk analytics to practical risk analysis challenges, mainly related to public and occupational health risks from pathogens in food or from pollutants in air. Chapters 12 through 15 turn to broader questions of how to improve risk management decision-making by individuals, groups, organizations, institutions, and multi-generation societies with different cultures and norms for cooperation. These chapters examine organizational learning, community resilience, societal risk management, and intergenerational collaboration and justice in managing risks.

Fundamentals of Machine Learning for Predictive Data Analytics, second edition

John D. Kelleher,Brian Mac Namee,Aoife D'Arcy
Fundamentals of Machine Learning for Predictive Data Analytics second edition Book PDF

Author : John D. Kelleher,Brian Mac Namee,Aoife D'Arcy
Publisher : MIT Press
Page : 853 pages
File Size : 40,8 Mb
Release : 2020-10-20
Category : Computers
ISBN : 9780262361101

Get Book

Fundamentals of Machine Learning for Predictive Data Analytics, second edition by John D. Kelleher,Brian Mac Namee,Aoife D'Arcy Pdf

The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.