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Foundations of Probability

  • What is Probability?
  • Theoretical vs Empirical Probability
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  • Variance and Standard Deviation
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  • Key Inequalities

Set Theory & Combinatorics

  • Set Operations in Probability
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Conditional & Bayesian Probability

  • Conditional Probability
  • Bayes' Theorem
  • Law of Total Probability

Random Variables & Distributions

  • What is a Random Variable?
  • Discrete vs Continuous
  • PDFs and CDFs
  • Expectation, Variance, and Moments

Discrete Distributions

  • Bernoulli and Binomial
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Continuous Distributions

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Limit Theorems

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Frequentist Inference

  • Confidence Intervals
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Advanced Probability Tools

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Bayesian Inference

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Regression Analysis

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Multivariate Statistics

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Stochastic Processes

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Simulation & Approximation

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Time Series

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Information Theory

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Linear Algebra

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Calculus & Optimization

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Machine Learning Fundamentals

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Deep Learning

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Options Pricing

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  • Binomial Trees
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Portfolio Theory

  • Mean–Variance Optimization
  • CAPM and Factor Models
  • Sharpe, Sortino, and Information Ratio
  • Black–Litterman
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Trading & Risk Applications

  • Value-at-Risk
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Study Guide/Portfolio Theory
Section 22 · Lesson 22.105

CAPM and Factor Models

Decomposing returns into systematic exposures.

The Capital Asset Pricing Model (CAPM) is the simplest factor model: each asset's expected excess return is proportional to its beta to the market:

E[Ri−Rf]=βi E[Rm−Rf]E[R_i - R_f] = \beta_i\, E[R_m - R_f]E[Ri​−Rf​]=βi​E[Rm​−Rf​]

The intuition: investors only get paid for systematic risk that can't be diversified away.

Multi-factor extensions have largely supplanted single-factor CAPM. Fama-French three-factor adds size and value:

E[Ri−Rf]=βiMKTMKT+βiSMBSMB+βiHMLHMLE[R_i - R_f] = \beta_i^{MKT} \text{MKT} + \beta_i^{SMB} \text{SMB} + \beta_i^{HML} \text{HML}E[Ri​−Rf​]=βiMKT​MKT+βiSMB​SMB+βiHML​HML

Five-factor adds profitability and investment. Carhart's momentum factor and Asness-Frazzini quality factor are also widely used. Modern factor models often have 202020+ factors covering style, sector, country, and macro exposures.

Factor models serve two purposes: explaining returns (decomposing past performance) and forecasting risk (covariance matrices implied by factor exposures are far more stable than raw sample covariances).

Under CAPM, an asset with β=0.5\beta = 0.5β=0.5 and risk-free rate 4%4\%4% earns an expected return of (assuming market premium 6%6\%6%):

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