A "sector playbook" is the body of industry-specific knowledge needed to read a company correctly: the right valuation metric, the demand drivers, the cost structure, the regulatory frame, and the part of the economic cycle the business lives in. The premise behind this whole branch is that a generic financial template misreads most companies — net interest margin is the heartbeat of a bank but meaningless for a miner; FFO replaces net income for a REIT; ARR and net revenue retention matter more than GAAP earnings for early SaaS; a refiner's fortunes turn on crack spreads, not oil price per se. Each industry has its own "language," and analyzing a stock without it produces confidently wrong conclusions. The core tension of the domain is specificity vs. comparability: the metrics that make an industry legible (cap rates, combined ratios, AISC, breakevens) are precisely the ones that make cross-sector comparison invalid, so the discipline is knowing which lens to pick up — and never forcing one industry's yardstick onto another.

The organizing framework: GICS and cyclicality

The corpus is organized along the Global Industry Classification Standard (GICS) — the taxonomy maintained jointly by MSCI and S&P Dow Jones Indices. GICS is a four-tier hierarchy: 11 sectors → 25 industry groups → 74 industries → 163 sub-industries, with each company assigned by principal business activity (revenue being the key determinant). The 11 sectors are Communication Services, Consumer Discretionary, Consumer Staples, Energy, Financials, Health Care, Industrials, Information Technology, Materials, Real Estate, and Utilities. The taxonomy is not static: Real Estate was carved out as a standalone sector in 2016, Telecommunication Services was rebuilt into Communication Services in 2018, and the March 2023 revision re-granularized REITs and reclassified retailers by what they sell rather than how (online vs. in-store). The folder layout mirrors this — sectors 002–012 — with a leading cyclicality framework (001) that sorts the same sectors a second way, by behavior across the business cycle rather than by line of business.

That cyclicality cut is the cross-sector spine. Sectors fall into recognizable archetypes — cyclical (industrials, discretionary, materials, financials), defensive (staples, utilities, healthcare), secular-growth (technology, parts of communication services), and interest-rate-sensitive (utilities, REITs, banks, homebuilders) — and these archetypes drive the sector-rotation idea: that leadership migrates predictably from early-cycle cyclicals through late-cycle energy/materials into defensives during contraction. (See 001-cyclicality-framework. Note: those nodes are scaffolded but their docs are not yet written.)

When sector knowledge matters most — and when less

Industry context is most load-bearing when (a) the right valuation metric is non-standard (REITs, banks, insurers, miners, E&P, early SaaS) — using P/E or net income here is simply an error; (b) the business is commodity-price-taking (energy, materials, shipping), so the stock tracks an exogenous price more than company execution; (c) earnings are binary or lumpy (biotech clinical catalysts, defense contract awards, semiconductor inventory cycles); or (d) regulation sets the economics (utilities' allowed returns, pharma pricing, bank capital rules). It matters less for broad index exposure, for pure technical/price-action setups that are agnostic to fundamentals, and for very short holding horizons where flow and momentum dominate company-specific fundamentals.

Map of the sub-topics

Each sector folder drills into the metrics and dynamics that actually move that group:

• Financials (002) — banks (net interest margin, loan growth & credit quality, rate sensitivity, capital ratios), insurance (combined ratio, float, P&C vs. life vs. reinsurance), asset managers/exchanges, fintech & payments.
• Real Estate / REITs (003) — REIT types, FFO/AFFO (the income metric that replaces EPS), cap rates & occupancy, rate sensitivity & leverage.
• Energy (004) — upstream E&P (price drivers, breakevens & reserves, decline rates), midstream/pipelines, downstream refiners (crack spreads), oilfield services.
• Materials & Mining (005) — the commodity cycle, miners (AISC, reserves & grade), chemicals, steel & aluminum.
• Industrials (006) — capex cycles & backlog, PMI sensitivity, aerospace & defense, machinery, and transports (rails, airlines, trucking, shipping).
• Consumer Discretionary (007) — the spending cycle, retail/e-commerce, autos, restaurants & leisure, homebuilders, travel & hospitality.
• Consumer Staples (008) — defensive characteristics, pricing power & brands, food/beverage/household.
• Healthcare (009) — pharma (patent cliffs, pipeline/R&D, pricing & regulation), biotech (clinical catalysts, trial phases, cash runway), devices/tools, managed care.
• Technology (010) — software/SaaS (ARR & net revenue retention, Rule of 40, CAC/LTV/churn), semiconductors (cyclicality & inventory, fabless vs. foundry vs. IDM, capex), internet platforms, hardware.
• Communication Services (011) — telecom, media & entertainment, streaming & advertising.
• Utilities (012) — regulated returns & rate base, rate sensitivity & dividends, the renewables transition.

Adoption, debate & evidence

The descriptive layer — that industries need their own metrics, and that GICS is the standard map — is near-universal and uncontroversial; sector ETFs, analyst coverage, and index construction are all built on it. The prescriptive layer — that you can time sector rotation against the business cycle for excess return — is genuinely contested and should not be presented as established fact. Sam Stovall's S&P sector-rotation framework (1995) popularized the cyclical map, and some studies report momentum-based sector rotation adding modest excess return (commonly cited around 1–3% per year before costs) and, more reliably, downside protection late-cycle. But the skeptical literature is substantial: Molchanov & Stangl (The Myth of Business Cycle Sector Rotation, International Journal of Finance & Economics, 2024, 29(4): 4419–4442) find no evidence of the systematic sector outperformance the conventional wisdom predicts — and, notably, even assuming an investor can perfectly time business-cycle turning points, any edge is at best modest and diminishes sharply after transaction costs and real-time cycle-timing error. The honest summary: sector rotation is more defensible as a risk-management/context tool than as a standalone alpha engine, and rotation requires knowing where you are in the cycle — which is far harder ex ante than the tidy diagrams imply.

Strengths & limitations

The framework's strength is error-prevention: it stops the analyst from applying the wrong yardstick and surfaces the one or two variables that dominate a given business. Its biggest limitation is that GICS buckets are coarse and increasingly leaky — Amazon (discretionary) is partly a cloud company, conglomerates straddle sectors, and "tech-like" economics now appear across communication services and discretionary. The single most common misuse is treating a sector label as a forecast ("staples are defensive, so this name will hold up") rather than as conditional context; defensive describes relative behavior in risk-off regimes, not a directional edge, and rate-sensitive sectors can break their own playbook when real rates move.

Sources

• S&P Dow Jones Indices & MSCI — GICS methodology (11 sectors / 25 industry groups / 74 industries / 163 sub-industries; assignment by principal business activity).
• MSCI / S&P Dow Jones Indices — 2023 GICS revision (REIT re-granularization, retailer reclassification; effective March 2023); history of 2016 Real Estate carve-out and 2018 Communication Services rebuild.
• Wikipedia — Global Industry Classification Standard (structure and revision history, cross-check).
• Sam Stovall — Standard & Poor's Guide to Sector Investing (1995), origin of the business-cycle sector-rotation map.
• Molchanov, A. & Stangl, J. — The Myth of Business Cycle Sector Rotation, International Journal of Finance & Economics (2024), 29(4): 4419–4442 — skeptical evidence; no systematic outperformance, and edge erodes after costs even with perfect cycle timing.
• Sector-rotation practitioner literature (e.g. cited momentum-rotation studies reporting ~1–3%/yr pre-cost excess return and late-cycle downside protection) — note: contested; treat as risk-management context, not established alpha.
• Note on dispute: the descriptive GICS/metric framework is uncontested; the predictive sector-timing claim is genuinely disputed and should be read accordingly.