Factor Investing Academy

Factor investing is the practice of explaining and building portfolios around the characteristics that have historically driven differences in stock returns, rather than around individual company picks. The framework began with the Capital Asset Pricing Model, which held that a stock's expected return depended on a single quantity: its sensitivity to the overall market. That model was elegant, and it was incomplete. By the early 1990s a large body of evidence showed that stocks sharing certain traits, notably small companies and companies trading cheaply relative to their book value, had earned returns the single-factor model could not account for.

Eugene Fama and Kenneth French formalised that evidence into the three-factor model in 1993, adding size and value to the market factor. In 2015 they extended it to five factors by adding profitability and investment. Mark Carhart's 1997 four-factor model added momentum, which Fama and French have never included in their own models but which remains one of the most persistently documented patterns in the literature. Together these models form the vocabulary that most of quantitative finance now uses to describe why one portfolio behaved differently from another.

The articles below work through that vocabulary in order, from the intuition behind factors to the practical problems that appear when you try to implement them with real money. They are written to be readable without a finance degree, but they do not skip the parts that matter. Where a claim is contested, and several of the most important ones are, the disagreement is described rather than resolved.

How this section is organised

Core Concepts establishes what a factor is, what each of the five Fama-French factors measures, and why the size effect became the most heavily debated of the original three. Start here if the terminology is new.

Deep Dives examines the value premium and the competing risk-based and behavioural explanations for it, covers momentum and the quality factors, and walks through what a century of factor data actually shows, including the long stretches where the premiums went missing.

Practical Guide is the part most likely to change what you do. It covers portfolio construction, why factor timing is far harder than it appears, the specific ways backtests mislead, how to measure the factor exposures in a portfolio you already own, and why a real fund never quite matches the theoretical index it tracks.

A note on what this data can and cannot tell you

Every figure on this site comes from the Kenneth R. French Data Library, which publishes the return series underlying the published research. That makes the numbers here the same numbers used in thousands of academic papers, and it means they carry the same limitations. These are historical realised returns for theoretical long-short portfolios rebalanced on a fixed schedule. They are gross of trading costs, taxes, and the practical frictions that make the difference between a paper premium and a realised one.

The most common mistake made with this data is treating a low current reading as a signal to buy. A factor sitting in the bottom decile of its historical distribution tells you that the strategy has recently done badly. Whether that predicts a reversal is precisely the question the factor timing literature has struggled with for thirty years, and the honest answer is that the evidence is weak. The live readings shown throughout these articles are context, not recommendations. Nothing on this site is investment advice.

Core Concepts

Deep Dives

Practical Guide