Probabilistic Machine Learning for Finance and Investing

Spis treści ebooka

• Preface
  • Who Should Read This Book?
  • Why I Wrote This Book
  • Navigating This Book
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. The Need for Probabilistic Machine Learning
  • Finance Is Not Physics
  • All Financial Models Are Wrong, Most Are Useless
  • The Trifecta of Modeling Errors
    • Errors in Model Specification
    • Errors in Model Parameter Estimates
    • Errors from the Failure of a Model to Adapt to Structural Changes
  • Probabilistic Financial Models
  • Financial AI and ML
  • Probabilistic ML
    • Probability Distributions
    • Knowledge Integration
    • Parameter Inference
    • Generative Ensembles
    • Uncertainty Awareness
  • Summary
  • References
  • Further Reading
• 2. Analyzing and Quantifying Uncertainty
  • The Monty Hall Problem
  • Axioms of Probability
  • Inverting Probabilities
  • Simulating the Solution
  • Meaning of Probability
    • Frequentist Probability
    • Epistemic Probability
    • Relative Probability
  • Risk Versus Uncertainty: A Useless Distinction
  • The Trinity of Uncertainty
    • Aleatory Uncertainty
    • Epistemic Uncertainty
    • Ontological Uncertainty
  • The No Free Lunch Theorems
  • Investing and the Problem of Induction
  • The Problem of Induction, NFL Theorems, and Probabilistic Machine Learning
  • Summary
  • References
• 3. Quantifying Output Uncertainty with Monte Carlo Simulation
  • Monte Carlo Simulation: Proof of Concept
  • Key Statistical Concepts
    • Mean and Variance
    • Expected Value: Probability-Weighted Arithmetic Mean
    • Why Volatility Is a Nonsensical Measure of Risk
    • Skewness and Kurtosis
    • The Gaussian or Normal Distribution
    • Why Volatility Underestimates Financial Risk
    • The Law of Large Numbers
    • The Central Limit Theorem
  • Theoretical Underpinnings of MCS
  • Valuing a Software Project
  • Building a Sound MCS
  • Summary
  • References
• 4. The Dangers of Conventional Statistical Methodologies
  • The Inverse Fallacy
  • NHST Is Guilty of the Prosecutors Fallacy
  • The Confidence Game
    • Single-Factor Market Model for Equities
    • Simple Linear Regression with Statsmodels
    • Confidence Intervals for Alpha and Beta
  • Unveiling the Confidence Game
    • Errors in Making Probabilistic Claims About Population Parameters
    • Errors in Making Probabilistic Claims About a Specific Confidence Interval
    • Errors in Making Probabilistic Claims About Sampling Distributions
  • Summary
  • References
  • Further Reading
• 5. The Probabilistic Machine Learning Framework
  • Investigating the Inverse Probability Rule
  • Estimating the Probability of Debt Default
  • Generating Data with Predictive Probability Distributions
  • Summary
  • Further Reading
• 6. The Dangers of Conventional AI Systems
  • AI Systems: A Dangerous Lack of Common Sense
  • Why MLE Models Fail in Finance
    • An MLE Model for Earnings Expectations
    • A Probabilistic Model for Earnings Expectations
  • Markov Chain Monte Carlo Simulations
    • Markov Chains
    • Metropolis Sampling
  • Summary
  • References
• 7. Probabilistic Machine Learning with Generative Ensembles
  • MLE Regression Models
    • Market Model
    • Model Assumptions
    • Learning Parameters Using MLE
    • Quantifying Parameter Uncertainty with Confidence Intervals
    • Predicting and Simulating Model Outputs
  • Probabilistic Linear Ensembles
    • Prior Probability Distributions P(a, b, e)
    • Likelihood Function P(Y| a, b, e, X)
    • Marginal Likelihood Function P(Y|X)
    • Posterior Probability Distributions P(a, b, e| X, Y)
  • Assembling PLEs with PyMC and ArviZ
    • Define Ensemble Performance Metrics
      • Financial activities
      • Objective function
      • Performance metrics
    • Analyze Data and Engineer Features
      • Data exploration
        • Feature engineering
      • Data analysis
    • Develop and Retrodict Prior Ensemble
      • Specify distributions and their parameters
      • Sample distributions and simulate data
      • Evaluate and revise untrained model
    • Train and Retrodict Posterior Model
      • Train and sample posterior
      • Retrodict and simulate training data
      • Evaluate and revise trained model
    • Test and Evaluate Ensemble Predictions
      • Swap data and resample posterior predictive distribution
      • Predict and simulate test data
      • Evaluate, revise, or deploy ensemble
  • Summary
  • References
  • Further Reading
• 8. Making Probabilistic Decisions with Generative Ensembles
  • Probabilistic Inference and Prediction Framework
  • Probabilistic Decision-Making Framework
    • Integrating Subjectivity
    • Estimating Losses
    • Minimizing Losses
  • Risk Management
    • Capital Preservation
    • Ergodicity
    • Generative Value at Risk
    • Generative Expected Shortfall
    • Generative Tail Risk
  • Capital Allocation
    • Gamblers Ruin
    • Expected Valuers Ruin
    • Modern Portfolio Theory
    • Markowitz Investors Ruin
    • Kelly Criterion
    • Kelly Investors Ruin
  • Summary
  • References
  • Further Reading
• Index

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