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

Table of Contents

  1. Preface
    1. Conventions Used in This Book
    2. Using Code Examples
    3. O’Reilly Online Learning
    4. How to Contact Us
  2. 1. Fundamentals of Risk Management
    1. Risk
    2. Return
    3. Risk Management
    4. Main Financial Risks
    5. Big Financial Collapse
    6. Information Asymmetry in Financial Risk Management
    7. Adverse Selection
    8. Moral Hazard
    9. Conclusion
    10. Further Resources
  3. 2. Introduction to Time Series Modeling
    1. Time Series Component
    2. Trend
    3. Seasonality
    4. Cyclicality
    5. Residual
    6. Time Series Models
    7. Moving Average Model
    8. Autoregressive Model
    9. Autoregressive Integrated Moving Average Model
    10. Conclusion
    11. Further Resources
  4. 3. Deep Learning for Time Series Modeling
    1. Recurrent Neural Network
    2. Long-Short Term Memory
    3. Conclusion
    4. Further Resources
  5. 4. Machine Learning-Based Volatility Prediction
    1. ARCH Model
    2. GARCH Model
    3. GJR-GARCH
    4. EGARCH
    5. Support Vector Regression-GARCH
    6. Neural Network
    7. Bayesian Approach
    8. Bayes’ Theorem
    9. Conclusion
    10. Further Resources