Probabilistic Machine Learning for Finance and Investing Deepak K. Kanungo

Probabilistic Machine Learning for Finance and Investing Deepak K. Kanungo - okladka książki

Autor:: Deepak K. Kanungo
Wydawnictwo:: O'Reilly Media (Z chęcią przeczytam książkę w języku polskim)
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Stron:: 266
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There are several reasons why probabilistic machine learning represents the next-generation ML framework and technology for finance and investing. This generative ensemble learns continually from small and noisy financial datasets while seamlessly enabling probabilistic inference, retrodiction, prediction, and counterfactual reasoning. Probabilistic ML also lets you systematically encode personal, empirical, and institutional knowledge into ML models.

Whether they're based on academic theories or ML strategies, all financial models are subject to modeling errors that can be mitigated but not eliminated. Probabilistic ML systems treat uncertainties and errors of financial and investing systems as features, not bugs. And they quantify uncertainty generated from inexact inputs and outputs as probability distributions, not point estimates. This makes for realistic financial inferences and predictions that are useful for decision-making and risk management.

Unlike conventional AI, these systems are capable of warning us when their inferences and predictions are no longer useful in the current market environment. By moving away from flawed statistical methodologies and a restrictive conventional view of probability as a limiting frequency, you’ll move toward an intuitive view of probability as logic within an axiomatic statistical framework that comprehensively and successfully quantifies uncertainty. This book shows you how.

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Szczegóły książki

ISBN Ebooka:: 978-14-920-9763-1, 9781492097631
Data wydania ebooka :: 2023-08-14 Data wydania ebooka często jest dniem wprowadzenia tytułu do sprzedaży i może nie być równoznaczna z datą wydania książki papierowej. Dodatkowe informacje możesz znaleźć w darmowym fragmencie. Jeśli masz wątpliwości skontaktuj się z nami sklep@ebookpoint.pl.
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Informatyka » Biznes IT » Big data » Uczenie maszynowe

Spis treści książki

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