Basic Prerequisites: Core Financial Ratios (the ratio catalog and its lookup table), Fundamental Analysis & Screening (the discretionary screen these factors mechanize), and Accounting Quality & Red Flags (the defense layer every factor inherits). (these are the folder-level prerequisites for pages 02–06; page 01 states its own, smaller, entry requirements)
1. Intuition & Practical Objective
A screen filters; a factor predicts - systematically, across the whole market. This folder is the seat of the difference: turning the accounting data of the previous folders (book value, earnings, gross profit, asset growth, accruals) into priced characteristics - fundamental signals on which, on average and over long horizons, cheap-and-good firms have out-earned expensive-and-bad ones. This is quantitative fundamental investing: take a real number off the statements, sort the entire universe on it, hold the cheap side, and let the cross-section do the work.
The governing fact, from Fama–French 1992: two easily measured variables - size and book-to-market - capture the cross-sectional variation in average returns that the CAPM’s beta alone could not. That single paper ended the “one risk factor (beta)” era and opened the factor catalog this folder maps: value (book-to-market, earnings yield), profitability (ROE, gross profitability), investment (asset growth), and quality (the Piotroski F-score, accruals). Fama–French 2015 then put two of those - profitability (RMW) and investment (CMA) - inside the asset-pricing model, which is the formal statement that accounting fundamentals are priced risk, not curiosities.
The one-sentence essence. “A fundamental factor is an accounting characteristic that, when you sort the market on it and hold one side, has historically earned a persistent premium - value pays for cheapness, profitability pays for quality, investment punishes over-investment, and the F-score separates the cheap-and-strong from the cheap-and-doomed.”
Audience arc: beginner learns what a factor is and why accounting data predicts returns; intermediate learns how each factor is constructed and sorted into portfolios; expert reads the factor-model evidence, how factors are combined, and where the premium decays (crowding, data mining). The sub-pages below are ordered for exactly that climb.
2. Mathematical Ground Truth & Factor Lookup Table
Notation: book-to-market book common equity / market equity; earnings yield net income / market equity; net income / book equity; gross profitability (sales COGS) / total assets; asset growth total assets / prior total assets; the Piotroski F-score (0–9); cash flow from operations.
| Factor | Construction | Sort & long side | Sign of premium | Evidence anchor |
|---|---|---|---|---|
| Size | Market equity price shares | small vs. big | small earns more | Fama–French 1992, 1993 (SMB) |
| Value | (or ) | high (cheap) | positive | Fama–French 1992, 1993 (HML) |
| Earnings yield | high | positive | Basu 1983; Fama–French 1992 | |
| Profitability (gross) | high | positive | Novy-Marx 2013; Fama–French 2015 (RMW) | |
| Profitability (ROE) | high | positive | Hou, Xue & Zhang 2015 (q-factor) | |
| Investment | low (conservative) | negative | Fama–French 2015 (CMA) | |
| Quality (composite) | Piotroski | high | positive | Piotroski 2000 |
| Quality (accruals) | Accruals NI CFO | low accruals | negative | Sloan 1996; Chan et al. 2006 |
(All lookup values computed and reproduced exactly in §3 and re-verified on each sub-page; the sample universe “AlphaChem, BetaSteel, …” is fully specified on each sub-page.)
Critical scaling caveat - factors are cross-sectional orderings, not absolute thresholds. Every factor premium is a relative statement: the top tercile of the universe on out-earned the bottom tercile on average, over many years. A single firm’s is nearly meaningless; its rank against the market’s current cross-section is the factor signal. This is why every factor is built by sorting, and it is the discipline that 02 · Fundamental Factors operationalizes.
3. Computational Implementation - the factor engine
Runs on the standard library only. It takes one cross-section of twelve firms’ statements, computes every fundamental factor on the lookup table (value, earnings yield, gross profitability, ROE, asset growth), sorts the universe into value-weighted portfolio terciles on each factor, and reports each factor’s realized return spread - the machine that every screen and every Fama–French construction in this folder reduces to.
Read the spreads carefully - they are the whole folder in one table. Value (B/M +4.0%, E/P +3.2%) and investment (−4.0%, i.e. low investment wins) show their premiums immediately. Gross profitability and ROE, however, show no raw premium in this naive sort - the profitable firms here are expensive growth firms (high GP/A, low B/M). That is exactly the Novy-Marx point that opens 03 · Value & Profitability: profitability pays, but only once value is conditioned on - you must control the expensive/profitable confound.
4. Failure Modes & First-Principles Breakdowns
Hub signposts - the folder’s failure-mode analysis lives in 05 · Failure Modes & Practice. In one line each:
- Backtests manufacture premiums. Test enough random characteristics and one will look spectacular by chance - Green, Hand & Zhang’s 200-odd return-predictive signals are mostly redundancy around a handful of real factors. Data-mining is the factor’s occupational disease.
- Factors crowd and decay. A published premium is an arbitrage with a half-life; once it is crowded, the return compresses. The value premium of the 1990s is thinner today - crowding is a first-principles ceiling, not a bug.
- Accounting games hit the factor at its source. A factor is only as honest as the line items it sorts on - restatements, accrual manipulation, and look-ahead leakage silently corrupt the cross-section (see Accounting Quality & Red Flags).
5. References
- Fama, Eugene & French, Kenneth: “The Cross-Section of Expected Stock Returns” (JF, 1992)
- Fama & French: “Common Risk Factors in the Returns on Stocks and Bonds” (JFE, 1993)
- Fama & French: “A Five-Factor Asset Pricing Model” (JFE, 2015)
- Novy-Marx, Robert: “The Other Side of Value: The Gross Profitability Premium” (JFE, 2013)
- Piotroski, Joseph D.: “Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers” (JAR, 2000)
- Hou, Kewei; Xue, Chen & Zhang, Lu: “Digesting Anomalies: An Investment Approach” (RFS, 2015)
- Green, Jeremiah; Hand, John R. M. & Zhang, X. Frank: “The Characteristics That Provide Independent Information About Average U.S. Monthly Stock Returns” (RFS, 2017)
- Sloan, Richard: “Do Stock Prices Fully Reflect Information in Accruals and Cash Flows About Future Earnings?” (TAR, 1996)
6. Connected Graph Bridges
- Foundational base: Core Financial Ratios · Fundamental Analysis & Screening (the discretionary screen this folder mechanizes) · Accounting Quality & Red Flags (the defense layer every factor inherits)
- Sibling topics: Equity Valuation (why low and high are “cheap”) · Data Sources & Corporate Data (point-in-time Compustat/EDGAR - where factor inputs come from)
- Quantitative bridges: Fundamental Multi-Factor Models (the Barra/FF factor-model construction layer) · Backtesting Hygiene & Deflated Sharpe (why a backtested factor premium must be discounted) · Portfolio Optimization (how factor tilts become portfolios)
- Sub-pages (in-folder): 01 From Zero · 02 Fundamental Factors · 03 Value & Profitability · 04 Quality & F-scores · 05 Failure Modes & Practice · 06 Advanced Extensions