# It's Just Beta — An Equity Factor Model Primer

> A primer on equity factor models: exposures, factor returns, factor portfolios, risk model assembly, risk and performance attribution, portfolio construction, hedging, model evaluation, and extensions. Written for quantitatively literate readers with no prior factor-model experience. One ten-stock worked example ("MiniModel") runs through every chapter, so every quoted number is reproducible.

The complete primer as a single markdown file: [llms-full.txt](https://itsjustbeta.com/llms-full.txt). Each chapter below links to its raw markdown; the HTML version drops the .md suffix.

## Foundations

- [Why Factor Models Exist](https://itsjustbeta.com/chapters/01-introduction.md): Suppose you manage a portfolio of 50 stocks. One morning it is down 1.8%. Why?
- [The Factor Model Equation](https://itsjustbeta.com/chapters/02-the-factor-model-equation.md): For a universe of N stocks and K factors, over one period (a day, a week, a month), the factor model states that each stock's return decomposes as:
- [Factors and Exposures](https://itsjustbeta.com/chapters/03-factors-and-exposures.md): A factor is a common driver of returns shared across many stocks. An exposure is one stock's sensitivity to a given factor. In the model equation a column of X is a factor and a row is a single stock's exposures.
- [Types of Factor Model](https://itsjustbeta.com/chapters/04-model-types.md): Every factor model must produce the same four ingredients: exposures X, factor returns f, a factor covariance F, and specific risk \Delta. F and \Delta are estimated from data in all three families, so what separates the families is which of X and f 

## Construction

- [Estimation Universe and Coverage Universe](https://itsjustbeta.com/chapters/05-universes.md): Before any regression is run, a model builder must answer two questions that are easy to conflate:
- [Estimating Factor Returns: The Cross-Sectional Regression](https://itsjustbeta.com/chapters/06-estimating-factor-returns.md): This chapter is the mathematical core of the series: one regression that turns measured exposures into factor returns. The fundamental model's exposures are measured (Chapter 3) on a curated universe (Chapter 5). What remains unknown each period is t
- [Factor Portfolios](https://itsjustbeta.com/chapters/07-factor-portfolios.md): Chapter 6 ended on a structural fact: the estimated factor returns are a linear map of stock returns, \hat f = P r. Each row of P is a portfolio, and the factor return is that portfolio's return. Understanding these portfolios turns the regression fr
- [Risk Model Assembly: Factor Covariance, Specific Risk, and the Full Forecast](https://itsjustbeta.com/chapters/08-risk-model-assembly.md): Chapters 3–7 produce for every period a vector of factor returns \hat ft and a vector of residuals \hat\epsilont. A risk model is what you get by turning those two histories into forecasts: a factor covariance matrix F, a specific-risk matrix \Delta,

## Applications

- [Risk Attribution: Where Does My Risk Come From?](https://itsjustbeta.com/chapters/09-risk-attribution.md): A risk number by itself is almost useless. The questions that drive decisions are: how much risk, from which sources, and from which positions? Risk attribution is the machinery that answers them, and it is pure algebra on the assembled model of Chap
- [Performance Attribution: Where Did My Returns Come From?](https://itsjustbeta.com/chapters/10-performance-attribution.md): Risk attribution (Chapter 9) is ex ante: model-implied, forward-looking, in volatility units. Performance attribution is its ex post mirror: realized returns, decomposed through the same exposures and factor returns, in return units. The two reports 
- [Portfolio Construction: How Do I Build the Portfolio I Want?](https://itsjustbeta.com/chapters/11-portfolio-construction.md) _(coming soon)_
- [Hedging: How Do I Remove the Risk I Don't Want?](https://itsjustbeta.com/chapters/12-hedging.md) _(coming soon)_
- [Alpha Research: What Can't the Model Explain?](https://itsjustbeta.com/chapters/13-alpha-research.md) _(coming soon)_

## In Practice

- [Evaluating a Factor Model: Is It Fit for Purpose?](https://itsjustbeta.com/chapters/14-model-evaluation.md) _(coming soon)_
- [Modifying a Factor Model: Adding, Removing, and Changing Factors](https://itsjustbeta.com/chapters/15-modifying-the-model.md) _(coming soon)_

## Appendix

- [Practical Considerations: Data, Implementation, and Pitfalls](https://itsjustbeta.com/chapters/16-practical-considerations.md): The mathematics of Chapters 2–15 is perhaps a fifth of a real factor-model effort. The rest is data engineering, numerical care, and the discipline to avoid a handful of known traps.
- [Appendix: Reference Material](https://itsjustbeta.com/chapters/17-appendix.md): | Symbol                           | Dimensions                  | Meaning                                                                                                                        | First used                                            
- [Mini Example Source Code](https://itsjustbeta.com/chapters/18-mini-example-source-code.md): This computes every number quoted across the chapters.