# Machine Learning for Financial Risk Management with Python: Algorithms for Modeling Risk

- Length: 350 pages
- Edition: 1
- Language: English
- Publisher: O'Reilly Media
- Publication Date: 2022-01-18
- ISBN-10: 1492085251
- ISBN-13: 9781492085256
- Sales Rank: #3448216 (See Top 100 Books)

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

Preface Conventions Used in This Book Using Code Examples O’Reilly Online Learning How to Contact Us Acknowledgements I. Risk Management Foundations 1. Fundamentals of Risk Management Risk Return Risk Management Main Financial Risks Big Financial Collapse Information Asymmetry in Financial Risk Management Adverse Selection Moral Hazard Conclusion References 2. Introduction to Time Series Modeling Time Series Components Trend Seasonality Cyclicality Residual Time Series Models White Noise Moving Average Model Autoregressive Model Autoregressive Integrated Moving Average Model Conclusion References 3. Deep Learning for Time Series Modeling Recurrent Neural Networks Long-Short Term Memory Conclusion References II. Machine Learning for Market, Credit, Liquidity, and Operational Risks 4. Machine Learning-Based Volatility Prediction ARCH Model GARCH Model GJR-GARCH EGARCH Support Vector Regression: GARCH Neural Networks The Bayesian Approach Markov Chain Monte Carlo Metropolis–Hastings Conclusion References 5. Modeling Market Risk Value at Risk (VaR) Variance-Covariance Method The Historical Simulation Method The Monte Carlo Simulation VaR Denoising Expected Shortfall Liquidity-Augmented Expected Shortfall Effective Cost Conclusion References 6. Credit Risk Estimation Estimating the Credit Risk Risk Bucketing Probability of Default Estimation with Logistic Regression Probability of Default Estimation with the Bayesian Model Probability of Default Estimation with Support Vector Machines Probability of Default Estimation with Random Forest Probability of Default Estimation with Neural Network Probability of Default Estimation with Deep Learning Conclusion References 7. Liquidity Modeling Liquidity Measures Volume-Based Liquidity Measures Transaction Cost–Based Liquidity Measures Price Impact–Based Liquidity Measures Market Impact-Based Liquidity Measures Gaussian Mixture Model Gaussian Mixture Copula Model Conclusion References 8. Modeling Operational Risk Getting Familiar with Fraud Data Supervised Learning Modeling for Fraud Examination Cost-Based Fraud Examination Saving Score Cost-Sensitive Modeling Bayesian Minimum Risk Unsupervised Learning Modeling for Fraud Examination Self-Organizing Map Autoencoders Conclusion References III. Modeling Other Financial Risk Sources 9. A Corporate Governance Risk Measure: Stock Price Crash Stock Price Crash Measures Minimum Covariance Determinant Application of Minimum Covariance Determinant Logistic Panel Application Conclusion References 10. Synthetic Data Generation and The Hidden Markov Model in Finance Synthetic Data Generation Evaluation of the Synthetic Data Generating Synthetic Data A Brief Introduction to the Hidden Markov Model Fama-French Three-Factor Model Versus HMM Conclusion References Afterword Index About the Author

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