Wall Street and the Rise of Adolf Hitler

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 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 Preface I. Risk Management Foundation 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 Further Resources 2. Introduction to Time Series Modeling Time Series Component Trend Seasonality Cyclicality Residual Time Series Models Moving Average Model Autoregressive Model Autoregressive Integrated Moving Average Model Conclusion Further Resources 3. Deep Learning for Time Series Modeling Recurrent Neural Network Long-Short Term Memory Conclusion Further Resources 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 Network Bayesian Approach Bayes’ Theorem Conclusion Further Resources 5. Market Risk Value-at-Risk Variance-Covariance Method Historical-Simulation Method Monte Carlo-Simulation VaR Denoising Expected Shortfall Liquidity Augmented Expected Shortfall Effective Cost Conclusion Further Resources