Series 86 Cheat Sheet — Research Analyst Modeling, Valuation & Forecasting
April 9, 2026
Comprehensive FINRA Series 86 reference: macro/industry data collection, fundamental company analysis, accounting comparability, forecasting frameworks, valuation methods (multiples, DCF, DDM, economic profit, SOTP), cost of capital, and catalyst-driven re-rating logic.
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Series 86 is “analyst work under time pressure”: gather the right facts, translate them into a coherent model, pick the right valuation lens, and defend a recommendation with the cleanest logic.
This cheat sheet is a study aid (not legal advice). Always follow your firm’s written supervisory procedures (WSPs) and current FINRA/SEC requirements.
Study reality: most misses come from (a) wrong definition (EV vs equity, FCF vs earnings), (b) wrong method selection (P/E vs EV multiple vs DCF), or (c) missing the “driver chain” from macro → industry → company → model.
How Series 86 questions are written (exam mindset)
Many items are “which is most likely / best supports / most appropriate” decisions.
The best answer is usually the one that is most defensible: correct definition, consistent assumptions, and the right driver.
If the stem contains a key constraint (cyclical industry, negative earnings, high leverage, capital intensity, dividend payer), that constraint often determines the correct valuation approach.
Series 86 “best answer” checklist
What is the driver chain? macro → industry → company
What is the data quality? trend + comparability + accounting method + one-time items
What is the model driver? volume/price/mix, margin, capex, working capital, leverage
What valuation lens fits? P/E vs EV-based vs DCF vs DDM vs SOTP
What’s the catalyst and risk? what changes the market’s mind; what breaks the thesis
Analyst-modeling reflexes table (high-yield)
Stem cue
Think first about
Strongest next move
cyclical company or peak-margin period
normalization
move to mid-cycle earnings or cash flow before valuing
negative or distorted earnings
valuation-method fit
shift away from P/E toward EV/sales, EV/EBITDA, or DCF as appropriate
high leverage or refinancing risk
capital structure sensitivity
emphasize EV, downside risk, and whether equity value is convex or impaired
dividend-paying stable business
payout support
consider DDM only if dividends are durable and policy is a real value driver
conglomerate or clearly distinct segments
segment economics
use SOTP logic rather than forcing one blended multiple
strong earnings growth but weak cash conversion
earnings quality
test working capital, capex, and cash-flow support before trusting the story
Bookmark table: fastest Series 86 decision sort
If the question is really about…
Ask first…
Usually strongest answer direction
method selection
what business constraint or data problem dominates?
choose the valuation lens that fits the company’s economics and data quality
earnings quality
are the reported numbers normalized and cash-backed?
adjust for one-time items and weak conversion before valuing
leverage
is equity value too distorted to compare directly?
move toward EV-based logic and downside sensitivity
segment diversity
are multiple businesses being forced into one multiple?
use segment logic or SOTP
catalyst relevance
does this event actually change cash flows or risk?
prioritize drivers that reprice the thesis, not just headlines
Fast eliminations (often wrong on Series 86):
Mixing EV valuation with equity multiples/metrics (or vice versa).
Treating cycle-peak earnings or “one-time” items as sustainable.
Using P/E when earnings are negative or heavily distorted without switching methods.
Ignoring working capital and capex while focusing only on earnings.
Using the wrong discount rate for the cash flow (FCFF vs FCFE mismatch).
Definition hygiene (the points you can’t afford to miss)
Item
Correct framing (exam level)
Common mistake
EV vs equity value
EV values the operations; equity is what’s left for common shareholders
valuing EBITDA using equity value
FCFF vs FCFE
FCFF is “to all capital providers” (discount with WACC); FCFE is “to equity” (discount with cost of equity)
discounting FCFF with cost of equity
Multiple matching
EV multiples pair with EBITDA/EBIT/sales; equity multiples pair with net income/FCF (concept)
pairing EV/EBITDA with EPS
Trailing vs forward
forward multiples reflect expected fundamentals; trailing can be stale
mixing time periods (TTM earnings with forward price target logic)
Nominal vs real
discount rate and growth must be in the same “units”
using nominal WACC with “real” growth (or vice versa)
Working capital sign
ΔNWC is usually a use of cash when it rises
assuming higher AR/inventory increases cash
Valuation-method quick cues
P/E: better for profitable companies where equity earnings are meaningful and capital structures are not the main comparison problem.
EV/EBITDA or EV/EBIT: better when you need a capital-structure-neutral operating lens.
EV/Sales: useful when margins or EBITDA are weak, volatile, or temporarily negative.
DCF: best when the thesis depends on explicit cash-flow timing, investment intensity, or a changing margin profile.
DDM: only works well when dividends are central to the equity story and reasonably predictable.
High-yield trap: choosing the “fancier” method when the stem is really testing whether you can match the method to the business and the data quality.
Valuation-method flow
flowchart TD
A["Start with the company constraint"] --> B{"Profitable and comparable earnings?"}
B -->|"Yes"| C["Consider P/E or other equity multiple if capital structure is not the issue"]
B -->|"No"| D{"Operating metric still meaningful?"}
D -->|"Yes"| E["Shift to EV/EBITDA, EV/EBIT, or DCF"]
D -->|"No"| F["Use EV/Sales, SOTP, or another structure-aware method"]
C --> G["Stress-test assumptions and catalyst support"]
E --> G
F --> G
Research workflow (how real analysis becomes an answer)
Macro view: what’s happening to growth, inflation, rates, credit, and risk appetite?
Industry view: what determines demand, pricing power, cost pressure, and regulation?
Company view: what is the business model, competitive position, and earnings quality?
Model: turn the thesis into drivers; tie the statements; sanity-check.
Valuation: pick the correct metric/model; compare to history/peers; build a range.
Recommendation: identify catalysts, risks, and what would change your mind.
F1 — Macro and industry data collection (high yield)
growth-sensitive sectors outperform in expansions; defensives in slowdowns
Inflation
price pressure
higher inflation can compress margins, raise discount rates, and shift winners/losers
Interest rates
cost of capital
higher rates reduce PV of cash flows; rate-sensitive sectors reprice
Unemployment / wages
labor conditions
affects consumer demand and cost pressure
Consumer confidence
demand sentiment
can lead consumption shifts (esp. discretionary)
Credit spreads
risk appetite
widening spreads signal higher risk premia and tighter financing
Series 86 pattern: if a question asks “what macro data matters most?”, choose the metric that directly touches the company’s demand or discount rate.
Fiscal vs monetary policy (concept)
Fiscal policy: government spending and taxation that affects aggregate demand (high level).
Monetary policy: central bank actions affecting short-term rates, liquidity, and credit conditions (high level).
Exam pattern: if the question is about discount rates, look to monetary policy / rates / credit spreads. If it’s about demand, look to growth, disposable income, employment, confidence.
Correlation and regression (Series 86 level)
FINRA expects the “what does this prove?” mindset:
Correlation: variables move together; does not prove causation.
Regression (concept): estimates relationship between a dependent variable and one or more drivers.
High-level interpretation cues:
a higher R² means the model explains more of the variation (but doesn’t prove causality).
Rising CCC can signal cash strain even when reported earnings look strong.
Context matters: CCC can rise during growth spurts; the question is whether it’s controlled and reversible.
Growth and profit drivers (what questions look like)
The outline lists common “what drives earnings?” inputs; Series 86 often tests these as root causes:
management quality and execution
contract structures
capex and capacity for growth
strength of business model
product assessment and innovation
customer concentration
subscriber acquisition costs (where relevant)
If the stem highlights a single driver (e.g., customer concentration), the best answer is usually the one that explains risk to revenue stability and pricing power.
Accounting comparability (high yield)
Series 86 expects you to recognize that accounting choices affect comparability and sometimes valuation.
Common high-level areas:
Inventory accounting (LIFO vs FIFO): affects COGS and earnings when input costs change (high level).
Liquidity: current/quick ratio concepts and near-term maturities (high level).
Exam pattern: if a company looks “cheap” but has high leverage, the best answer often emphasizes risk premium, refinancing risk, and equity downside convexity.
Corporate actions and comparability
The outline calls out corporate actions as analysis inputs: