Every financial term used in Dawo, explained in plain English. 136 entries across 7 domains.
Estimates a stock's intrinsic value by projecting future free cash flows and discounting them back to today. Highly sensitive to growth and discount-rate assumptions.
Best for stable businesses; less reliable for hyper-growth or cyclical names where assumptions dominate.
Annualized dividend per share divided by current stock price. The cash income an investor would collect per dollar invested, before any price appreciation.
EV (Enterprise Value) is what it would cost to buy the entire company outright: market cap + total debt − cash on the balance sheet. Unlike market cap, EV reflects the price an acquirer would actually pay (you take on the debt, you get to use the cash).
EV is the right numerator for valuation ratios that compare to operating profit (EV/EBITDA, EV/Revenue) because those denominators are before financing costs.
Enterprise Value divided by EBITDA. EV = market cap + total debt − cash (what it would cost to buy the whole company outright). EBITDA = Earnings Before Interest, Taxes, Depreciation, and Amortization — a proxy for operating cash profit. The ratio compares profitability across companies with different debt loads.
10-15× is typical; above 20× signals premium or rich pricing.
Enterprise Value (market cap + debt − cash) divided by trailing revenue. Used when the company isn't profitable yet (negative EBITDA) or when comparing growth-stage businesses by scale.
The valuation multiple (e.g. P/E or EV/EBITDA) applied to the final forecast year to estimate what the business is worth at the end of the projection.
Market average is roughly 15–18× earnings. A high exit multiple assumes the market will always pay a premium for this stock.
Forward Price-to-Earnings ratio — the stock price divided by the next 12 months of expected earnings per share. Lower = you're paying less for each dollar of future profit.
Below 15× = relatively cheap; 15-25× = typical for stable growth; above 25× = priced for above-average growth.
Free Cash Flow (FCF) divided by market cap, as a percentage. FCF is the cash a business actually generates after running and reinvesting — the 'real' yield an owner could pocket if all of it were distributed, vs the dividend yield which only counts what's actually paid out.
Above 5% = rich; 2-5% = healthy; below 2% = thin; negative = burning cash.
The Compound Annual Growth Rate the Lead Analyst assumes for the company's revenue under this scenario. It's the steady annual % growth that, compounded year over year, takes today's revenue to the model's forecast revenue at year 5. Higher growth → more optimistic.
Compare across scenarios: a bull case might assume 15% CAGR, base 8%, bear 2%. The gap shows how much the upside relies on growth holding up.
Extra per-share value the Lead Analyst attributes to high-impact opportunities NOT yet in the base model — new product launches, M&A optionality, latent platform value, etc. Surfaced as a dollar-per-share adder on top of the DCF. The accompanying mechanism + magnitude_source show what assumption it's based on.
Optionality should be modest (≤20% of price). Large values signal the thesis depends heavily on unproved upside — handle with care.
Values the stock by applying the median valuation multiple (typically EV/EBITDA or P/E) from a basket of comparable companies to this company's forecast earnings. The cleanest sanity-check on whether the DCF is anchored to reality — if peers trade at 18× EV/EBITDA and this stock's DCF implies 30×, something deserves scrutiny.
Best when peer set is genuinely comparable. Loses signal when the company is sui generis (NVDA had no real peers for years) or when the whole sector is mispriced.
Price/Earnings-to-Growth ratio — forward P/E divided by the expected earnings growth rate. Adjusts P/E for growth: a 30× P/E growing 30% has PEG 1.0 (fair); 30× growing 10% has PEG 3.0 (expensive).
Below 1.0 = growth at a value price; 1.0-1.5 = fair; above 1.5 = paying up for growth.
Price-to-Free-Cash-Flow ratio — market cap divided by free cash flow (cash from operations minus capital spending). FCF is harder to manipulate than earnings, so this ratio is often the cleanest valuation check.
Price-to-Sales ratio — the stock's market cap divided by revenue. Like EV/Revenue but ignores debt. A quick sanity check on whether a stock is priced reasonably relative to its top line.
A simulation of thousands of possible future prices for this stock, plotted as a curve. Where the curve is tall, that price is more likely; where it's flat, that price is unlikely. The vertical 'Now' line is the current price. Markers show: 'Low end' (worst 1-in-10 outcomes are below this), 'Most likely' (the median — half of outcomes are above, half below), and 'High end' (best 1-in-10 outcomes are above this).
Two peaks (a bimodal curve) means outcomes cluster around two distinct cases — typically base + bull. The dip between is real low-probability space, not missing data.
Our price targets follow the sell-side standard convention: 12-month horizon. The blended target mixes methods with different implicit horizons — DCF reflects present-value 'fair value' (no fixed horizon), while Forward P/E, peer comps, and sell-side consensus methods are explicitly 12-month forward. Read the headline as: 'where the stock should trade once the thesis assumptions are accepted by the market — typically within 12 months.'
A specific timing question — 'when will this play out?' — is better answered by the thesis-test horizon (next earnings / FDA / contract event) shown as the ⚡ chip on the ribbon.
Works the DCF backward: given today's price, what growth rate must the market be assuming? Useful to test whether the current price requires unrealistic future performance.
Values each business segment at an appropriate peer multiple (e.g. AWS at a software multiple, retail at a retailer multiple) then sums them. Reveals when consolidated multiples understate a high-quality division.
The average 12-month price target from professional Wall Street analysts covering this stock (Goldman, Morgan Stanley, etc.). Dawo uses this as one input in the blended valuation — it's the consensus view of human professionals who track the company full-time. The price is the simple average; the LA also weighs analyst dispersion (a tight cluster is more informative than a wide one).
Useful as a sanity check. Wide gap between Dawo and sell-side = either we're seeing something they aren't, or vice versa — read the variant-perception text for the explanation.
Values each business segment separately at appropriate multiples, then adds them up. Reveals hidden value when one division (e.g. AWS inside Amazon) deserves a much higher multiple than the consolidated company gets.
The perpetual annual growth rate assumed BEYOND the model's explicit forecast period (typically year 5). Used in the DCF's terminal-value calculation. Should never exceed long-run GDP growth (~2-3%) — anything higher implies the company eventually grows larger than the global economy.
Small changes here move DCF value a lot. 2% terminal growth → reasonable; 4% → aggressive; 5%+ → unsupportable.
The rate a company's cash flows are assumed to grow forever, beyond the explicit forecast. A DCF input.
Should stay at or below long-run economic growth (~2–3%). Anything higher implies the company outgrows the entire economy forever — usually unrealistic.
Price-to-Earnings ratio — the stock price divided by the last 12 months of actual earnings per share. Backward-looking; useful for stable businesses but misleading for fast-growing or cyclical names.
An adjustment in basis points (1bp = 0.01%) that the Lead Analyst adds to the base WACC (Weighted Average Cost of Capital) for this scenario. Positive premium = higher discount rate = lower DCF value (more risk assumed). Negative = lower discount rate (less risk, higher value).
Bear cases typically add 100-300bps premium for execution / regulatory / cyclical risk. Bull cases occasionally subtract premium when the LA sees structurally lower risk.
WACC (Weighted Average Cost of Capital) is the blended rate of return a company must earn to satisfy ALL its investors — equity holders and debt holders combined, weighted by how much capital each provides. Used as the discount rate in DCF valuations: a higher WACC means future cash flows are worth less today.
Typical range: 7-10% for stable large-caps; 10-13% for growth/cyclicals; 13%+ for distressed or highly speculative names.
The operating margin (operating income ÷ revenue) the Lead Analyst assumes the company will earn in year 5 of the model. Captures whether the scenario assumes margin expansion (good), maintenance, or compression (bad).
Margin assumptions matter as much as growth. A 'growth at any cost' bull case with declining margins may not actually flow to free cash flow.
EBITDA = Earnings Before Interest, Taxes, Depreciation, and Amortization. A cash-profit proxy that strips out three sources of accounting noise: how the business is financed (interest), what tax bracket it's in (taxes), and non-cash accounting charges for ageing assets (D&A). Lets you compare core operating profitability across companies with different debt loads and asset structures.
Critics call it 'earnings before the bad stuff' — D&A reflects real capital being spent, so always cross-check with free cash flow.
EBITDA (Earnings Before Interest, Taxes, Depreciation, and Amortization) as a percentage of revenue. Approximates operating profit per dollar of sales, stripping out financing, tax, and non-cash accounting choices so businesses are comparable.
Revenue minus cost of goods sold, divided by revenue. Captures pricing power and unit economics before operating overhead. Software typically 70-90%; manufacturing 25-45%.
Net income divided by revenue. The bottom line — what's left for shareholders after every cost. Comparable across industries with the same caveat as ROE about leverage.
Operating income divided by revenue. The slice of every revenue dollar that survives core operating costs (excludes interest and taxes).
Dawo composite score combining ROE (Return on Equity), EBITDA margin, and cash conversion into a single 0-100 quality read. Sourced from SEC EDGAR fundamentals filings.
70+ = strong; 40-70 = mixed; below 40 = weak fundamentals.
Dawo's 0–100 composite of a company's financial strength — profitability, margins, balance-sheet health, and cash-flow consistency. Higher means a sturdier, better-run business.
Above 70 = strong; 40–70 = average; below 40 = weak. A high score doesn't mean the stock is cheap — it means the business is solid.
Return on Equity — net income divided by shareholders' equity. How much profit each dollar of owner-invested capital generates. Sensitive to leverage: a heavily-borrowed business can post a high ROE without being fundamentally efficient.
Above 15% = strong; 10-15% = decent; below 10% = weak.
Return on Invested Capital — after-tax operating profit divided by all capital used in the business (equity + debt). The best single measure of how efficiently a business turns money into more money, independent of how it's financed.
Above 15% = great compounder; 10-15% = average; below 10% = destroys value vs cost of capital.
Return on Tangible Common Equity — ROE but with goodwill and other intangibles stripped from the denominator. Banks and acquisition-heavy companies report this to show returns on the hard capital actually at risk.
For banks: 15%+ is excellent; 10-15% solid; below 10% weak.
Accruals = net income minus operating cash flow (i.e. the non-cash portion of reported profit). Divided by average assets to scale across company sizes. Measures how much earnings sit on paper vs come in as actual cash — high accruals can signal aggressive revenue recognition.
Above 10% = quality concern; below 5% = clean.
Bankruptcy-prediction score from NYU professor Edward Altman (1968) — combines 5 financial ratios (working capital, retained earnings, EBIT, equity value, and revenue, each scaled by total assets) into a single Z value. Designed for industrial manufacturers; less reliable for asset-light or financial businesses.
Above 3.0 = safe; 1.8-3.0 = grey zone; below 1.8 = distress risk within 2 years.
Statistical score from finance professor Messod Beneish (1999) that flags the probability of earnings manipulation. Combines 8 ratios — receivables growth, gross margin trends, accruals quality, leverage shifts, etc. — into a single 'M' number. Famously caught Enron a year before it collapsed.
Above −1.78 = elevated risk of accounting restatement. Below −2.22 = clean.
Free Cash Flow (FCF) divided by net income. Measures how efficiently the profit a company reports on its income statement actually shows up as cash in the bank. Persistently low conversion can mean earnings exist on paper (via accruals, revenue recognition timing, etc.) but aren't translating into real cash.
Above 1.0 = excellent; 0.7-1.0 = healthy; below 0.5 = cash-quality red flag.
Automated screening that flags holdings with accounting anomalies — high accruals (gap between paper profit and cash), weak cash conversion, deteriorating Beneish M-Score, etc. These are heuristic warning signals for further research, NOT legal determinations or accusations of fraud.
9-point fundamental-strength scorecard from Stanford professor Joseph Piotroski. One point each for 9 criteria across profitability (positive net income, growing ROA, etc.), leverage (less debt, more current assets), and operating efficiency (margin and turnover improvements). The 'F' stands for 'fundamentals'.
7-9 = strong; 4-6 = average; 0-3 = financially weak.
Across thousands of simulated future paths, this is the size of the biggest peak-to-trough drop in the worst 5% of outcomes. Ignore the 95% that don't get this bad — this number is the tail risk to plan around.
Max DD (5th) of −22% means: if you land in the unlucky 5%, expect to drop ~22% from your peak before recovering.
Sensitivity to the broad market. Beta 1.0 = moves in line with S&P 500; beta 0.5 = half as volatile; beta 1.5 = 50% more volatile. Doesn't say WHY — just the historical correlation.
CVaR (Conditional Value at Risk), also called Expected Shortfall, is the AVERAGE loss on the worst 5% of days — i.e. the days when you do break through your VaR threshold. Where VaR tells you where the bad-day line is, CVaR tells you how bad it gets once you cross that line.
Measures how closely two assets move together. Range −1 to +1. Positive correlation = they rise and fall together; near zero = independent; negative = they move opposite each other (rare in equities).
Diversification benefit only exists when correlations are below ~0.7. In a crisis, equity correlations spike toward 1.0.
In real crashes, even low-beta defensive stocks sell off with the market because cross-asset correlations spike toward 1.0. We pull stress-time betas toward 1.0 to reflect this.
A stock with normal beta 0.5 gets an effective beta of 0.85 in market_crash (with a 0.7 blend).
Compares the weighted-average volatility of your individual holdings to the actual volatility of your portfolio. Higher = your holdings' price movements offset each other (true diversification).
Above 1.3 (some texts: 1.2) = strong diversification benefit; 1.0-1.3 = modest; ≤1.0 = holdings move in lockstep.
HHI computed after looking THROUGH each ETF into its underlying constituents — so an S&P 500 ETF doesn't count as a single concentrated position, it counts as 500 small ones. Reflects your real economic exposure.
More accurate than position-level HHI for ETF-heavy portfolios.
Splits a stress loss across what's driving it: broad market move, interest-rate sensitivity, and credit-spread sensitivity. Tells you what's actually moving your portfolio, not just that it moved.
A loss that's '85% market, 10% rates, 5% credit' means the broad sell-off did most of the damage; rates and credit added a smaller bite.
How much your portfolio leans toward well-known style factors (value, growth, quality, size, momentum) relative to the S&P 500 baseline. A negative Value tilt means your holdings carry higher valuation multiples than the market average — common for quality/growth strategies, not necessarily a problem.
For absolute valuation health (P/E, P/B levels), use the per-stock metrics. Factor tilts measure RELATIVE positioning vs the benchmark.
Sensitivity of an international ETF's USD value to USD strength. When USD rallies, USD-priced foreign holdings lose ground on the conversion alone.
EEM has β_FX ≈ 0.95: a 5% USD rally drags EEM down ~4.75% on the FX dimension before any local equity move.
A model that lets daily volatility cluster: calm periods stay calm, turbulent periods stay turbulent. We use it instead of assuming volatility is constant.
If your portfolio just had a big move, the next few days are projected with elevated volatility — matching real market behavior.
HHI (Herfindahl-Hirschman Index) is a concentration score originally used by antitrust regulators to measure market dominance. We borrow it for portfolios: square each holding's weight, then add them up. Higher = more concentrated in a few names; lower = more spread out. A 10-stock equal-weighted portfolio has HHI = 0.10.
Below 0.10 = well-diversified; 0.10-0.25 = moderate; above 0.25 = concentrated; above 0.40 = single-name dominant.
Re-runs your CURRENT portfolio through the actual daily returns of a past crisis (2008, COVID, dot-com, etc.). Shows what you would have experienced day by day if you'd held this same portfolio back then.
HHI (Herfindahl-Hirschman Index) is a concentration score — originally used by antitrust regulators to measure market dominance, now borrowed for portfolios: square each holding's weight, then add them up. Higher = more concentrated in a few names. 'Look-through' means we compute it AFTER expanding each ETF into its underlying stocks, so an S&P 500 ETF counts as 500 small positions rather than one big one. Reflects your true economic concentration, not just ticker-level concentration.
Benchmark: the S&P 500 itself has HHI ~50 (extremely diversified). Below 1,000 = well-diversified portfolio. Above 2,500 = very concentrated.
An extra component that adds rare, large daily moves (gaps from earnings, macro shocks) on top of the normal simulator. Captures tail risk that a smooth distribution misses.
Roughly 1-2% of simulated days now include a jump-style move, sized from your holdings' own historical tail days.
Thousands of simulated future paths for your portfolio, each randomly drawn but anchored in your holdings' actual price patterns. Shows the distribution of outcomes, not a single forecast.
Fraction of simulated paths that finish below where you started. Not 'will you lose money' — it's how often the simulation does over thousands of runs.
Prob loss of 22% = roughly 1 in 5 simulated paths ends below today's value.
Count of distinct GICS sectors (the 11-way classification used by S&P and MSCI — Tech, Financials, Healthcare, etc.) you have meaningful exposure to. Concentrating in only 2-3 sectors amplifies sector-specific risk (e.g. all-tech portfolios get hammered in rate-spike regimes).
8-11 sectors = full GICS coverage; 4-7 = moderate concentration; ≤3 = sector-bet portfolio.
Applies a defined market shock (e.g. -30% market, +200bp rates) to your specific holdings. Each position takes a tailored hit based on its sensitivities. Shows what would happen IF the scenario occurred — not how likely it is.
A bell-curve-like distribution with heavier tails than a normal distribution. Better matches real equity returns, which have more extreme outcomes than 'normal' would predict.
A way of sampling joint extremes — when one asset crashes, others crash too. More realistic than independent or Gaussian-correlated draws for stress periods.
VaR (Value at Risk) at 95% confidence is the loss you'd expect to exceed only 5% of the time. Read it as: 'on a typical bad day (the worst 1-in-20), I shouldn't lose more than X.' A daily VaR of 2% means: 19 days out of 20, your portfolio shouldn't drop more than 2%.
Doesn't tell you HOW BAD the worst 5% gets — that's CVaR's job.
Return in excess of what a benchmark with the same market exposure (beta) would have delivered. Positive alpha = manager / portfolio added value beyond riding the market.
Hard to sustain; most alpha disappears after fees and over long periods.
What percentage of the benchmark's LOSSES your portfolio absorbs during down-markets. Below 100% means you held up better than the benchmark when markets fell; above 100% means you fell harder.
The combination 'high up-capture + low down-capture' is the holy grail — gain most of the upside while absorbing less of the downside.
Max DD (Maximum Drawdown) is the largest peak-to-trough decline along a path. If your portfolio reached $100k, fell to $78k, and later recovered, the max drawdown was 22%.
Max DD (Maximum Drawdown) is the largest peak-to-trough decline over the measured period. If a portfolio hit $100k, dropped to $72k, then recovered, max drawdown = 28%.
What you actually lived through, not a smoothed annualized statistic.
MWR (Money-Weighted Return), also called IRR (Internal Rate of Return), accounts for the timing AND size of every deposit and withdrawal. It answers 'what return did my actual dollars earn?' — including credit/blame for whether you added cash before a rally or after a drop.
Use MWR to judge your investing DECISIONS (including timing); use TWR to judge your INVESTMENTS' performance.
Excess return over the risk-free rate divided by volatility. Risk-adjusted performance — how much extra return you got per unit of volatility taken on.
Above 1.0 = good; above 1.5 = excellent; below 0.5 = volatility wasn't rewarded.
A close cousin of the Sharpe ratio (named after Frank Sortino) that only counts downside volatility — big down moves — rather than total volatility. Better for asymmetric strategies (options, momentum) where huge up-days shouldn't be 'punished' as risk.
Trading days from the worst point back to the pre-event peak. ~21 trading days = 1 month, ~252 = 1 year. Null/none means the window ended before recovery.
TWR (Time-Weighted Return) measures portfolio performance independently of when you added or withdrew cash. It strips out the effect of contribution timing, so it answers 'how well did my investments perform?' rather than 'how well did I do, including the timing of when I put money in?'
TWR is the apples-to-apples way to compare your performance vs benchmarks like the S&P 500.
Annualized standard deviation of the difference between your portfolio's returns and a benchmark's returns. Measures how much your day-to-day path deviates from the benchmark.
Below 2% = closet indexer; 2-6% = moderately active; above 6% = highly active or concentrated.
What percentage of the benchmark's GAINS your portfolio picks up during up-markets. 100% means you matched the benchmark on up days; above 100% means you outperformed; below 100% means you lagged.
Annualized standard deviation of daily returns. Measures how much the portfolio's value swings around its average, regardless of direction. Often used as a proxy for 'risk' in textbooks.
Below 15% = low; 15-25% = moderate; above 25% = high (typical for individual stocks or growth-heavy portfolios).
YTD (Year-to-Date) total return — price change plus dividends — from January 1 of the current year through today. Includes both realized and unrealized gains.
Net dollar value of share buybacks (minus dilution from new issuance) divided by market cap, annualized. Companion to dividend yield: total cash return to shareholders = div yield + buyback yield.
Increase in shares outstanding (from stock-based compensation, M&A scrip, or new issuance). Shrinks per-share metrics even when total earnings are flat. Often hidden in 'adjusted' earnings figures.
Above 3% annual dilution is a quality concern; many tech companies dilute 5-10% via stock comp.
Yield on the 10-year US Treasury note. The benchmark long-term US 'risk-free rate' that anchors every DCF discount rate globally.
Rising yields compress growth-stock multiples; falling yields lift them. Historical norm: 3-5%.
Business-activity surveys (ISM Manufacturing & Services PMI). Above 50 = expansion, below 50 = contraction.
Dawo classifies each Fed/ECB/BoE/BoJ/PBoC speech with Haiku and assigns a hawkish-dovish score on [-1, +1]. Positive = hawkish (rate hikes, restrictive); negative = dovish (rate cuts, accommodation); near zero = neutral / data-dependent. The displayed value is a recency-weighted average over the last 30 days.
Leading signal: central-bank language typically shifts 1-3 weeks before rate decisions price in fully. Dovish drift at the ECB in late 2024 preceded the 2025 EU rally; hawkish drift at the Fed in early 2024 led the bond sell-off.
Consumer demand pulse: retail sales, sentiment, real disposable income. ~70% of US GDP is consumer spending.
Year-over-year change in a basket of consumer prices. The headline inflation measure central banks target.
Corporate-bond risk premia — how much extra yield investors demand to lend to companies vs governments. Widening spreads = market expects more defaults.
HY OAS above 6% historically flags recession stress; below 3% signals complacency.
Markets outside equities that signal global risk appetite: oil, the dollar, gold, copper, FX rates, volatility.
Forward-looking indicators that flag where in the business cycle we are: recession probability, yield curve shape, leading economic index.
Aggregate leverage indicators (government, corporate, household). High levels constrain future policy space.
Trade-weighted US dollar value vs a basket of foreign currencies. A stronger dollar hurts US exporters and dollar-denominated commodities.
Extra return investors demand for holding equities vs risk-free bonds. Dawo proxies this from HY credit spreads — higher spreads = higher ERP = higher discount rates.
The target overnight rate set by the US Federal Reserve. Sets the floor for all other US short-term rates.
Wars, sanctions, election shocks, sovereign crises. Drives risk-off rotations (energy + defense up; cyclicals down) and elevates the cross-asset GPR Index.
Daily geopolitical-risk index based on news-text analysis (Caldara & Iacoviello). Spikes during wars, terror events, and major diplomatic crises.
Quantitative measures of policy uncertainty and geopolitical risk based on news-text analysis.
Realized economic output growth: GDP, industrial production, retail sales. Backward-looking but anchors earnings expectations.
Option-Adjusted Spread for the US High-Yield (junk-bond) index — the extra yield investors demand vs Treasuries to compensate for default risk.
Below 3% = complacent; 3-5% = normal; above 6% = stress; above 8% = recession-style.
How long the classifier estimates the event will materially affect earnings. Dawo uses this to time-decay the event's weight: a 30-day-horizon event has zero weight by day 31. Prevents stale shocks from dragging today's verdict.
Year-over-year change in consumer or producer prices. Drives central-bank policy and erodes nominal returns.
Most central banks target ~2% CPI; sustained readings above 3% trigger tightening cycles.
Purchasing Managers' Index — monthly survey of supply-chain managers' new orders, production, employment, and inventories.
Above 50 = expansion; below 50 = contraction; below 45 = recession-style stress.
Employment and wage indicators: unemployment, jobless claims, payrolls. Tight labor markets fuel inflation; loose ones flag recession.
Composite of 10 forward-looking US indicators (manufacturing orders, building permits, claims, etc.) compiled by the Conference Board.
Sustained year-over-year decline historically precedes recession by 6-12 months.
A discrete shift in central-bank stance, fiscal policy, or major macro indicator (e.g. ECB cut announcement, US debt-ceiling deal, surprise NFP print). Often the proximate cause of regime-level repricing.
Dawo's automated classification of where the economy sits in the business cycle, based on curve, credit, growth, and labor signals. Four states: expansion, late_cycle, recession, recovery.
Drives sector elasticity in scenarios — defensives outperform in recession, cyclicals in recovery.
Dawo's classifier rates each material news event as low / medium / high / severe based on the magnitude and persistence of the implied earnings impact. Only high+ events feed the Lead Analyst's scenario weights, and shifts are capped at ±15pp so a single headline can't whipsaw a thesis.
Severe = recession-sized shock (tariffs, war, major guidance cut). High = quarter-mover. Below high = surfaced for awareness only.
Government bond yields and central-bank policy rates. The 'risk-free rate' anchor for every stock's discount rate — when rates rise, future cash flows are worth less today, so growth stocks compress.
10Y Treasury is the global benchmark; central-bank rates (Fed, ECB, BoE, BoJ) set the front end.
Smoothed 12-month-ahead US recession probability from the New York Fed, derived from the yield curve.
Above 30% historically marks elevated risk; readings above 40% have preceded every modern US recession.
Each non-US region's ETF total return minus SPY's over the same window — measured in percentage points. Positive = region outperforming US. Dawo proxies regional flow via EZU (Eurozone), EWU (UK), EWJ (Japan), FXI (China) — free-tier substitute for the paid EPFR feed.
Coincident signal: capital has already moved. Useful for confirming a regional thesis or flagging where the market has rotated; not a leading indicator of where it will go next.
Tariffs, fines, antitrust decisions, export controls, drug approvals/denials. Surface area is huge — these are the most-frequent severe events in modern markets.
How sensitive a sector's earnings are to the macro cycle. Cyclicals (Industrials, Financials, Materials) = 1.0×; Defensives (Staples, Utilities, Healthcare) ≈ 0.3×.
Used to compute portfolio-level macro exposure vs the S&P 500 benchmark.
Forward-looking 30-day implied volatility of the S&P 500, derived from option prices. Often called the 'fear gauge'.
Below 15 = complacent; 15-25 = normal; above 30 = panic. Spikes typically mark short-term lows.
The blended discount rate for a company's future cash flows, weighting equity and debt costs. Dawo computes a live WACC per symbol using the local risk-free rate + ERP from credit spreads.
WACC is the most sensitive DCF input — a 100 bps change can move fair value by 15-25%.
Spread between 10-year and 2-year Treasury yields. When negative ('inverted'), short-term rates exceed long-term rates — a classic recession-warning signal.
Every US recession since 1970 was preceded by a 10Y-2Y inversion 6-18 months prior.
The starting probability before any company-specific judgment — how often stocks in situations like this one have historically gone up, held, or fallen.
Anchors the odds in history rather than a hunch, so a single optimistic or pessimistic read can't run away with the probabilities.
The company's own filings, guidance, and disclosures (10-K/10-Q, earnings calls, investor decks).
Useful but self-interested — management chooses how to frame it, so we test these figures rather than take them at face value.
The steady annual rate that would turn a starting value into an ending value over multiple years. Smooths out year-to-year volatility into one comparable number.
5-10% = mature; 10-20% = growth; >20% = hypergrowth (rarely sustainable past 5 years).
Dawo's confidence in the assessment, on a 0–100 scale. Reflects the quality and convergence of evidence: high conviction = multiple independent signals agree; low = signals are mixed or sparse.
Above 70 = high; 50-70 = moderate; below 50 = mixed signals.
How sensitive a corporate bond is to credit-spread widening (the extra yield investors demand for taking corporate risk over Treasuries).
HYG has credit duration ~4. If credit spreads widen 250bp in a recession, HYG loses ~10% just from the credit move.
How much the high-yield-vs-investment-grade spread widens. In a recession, this typically widens 200bp+ as risk premiums rise.
How sensitive a bond's price is to interest-rate changes, in years. A bond with duration 6 loses ~6% if rates rise 1% (100bp).
AGG (D=6) loses ~12% in a 200bp rate spike. TLT (D=17) loses ~34% in the same scenario.
ETF (Exchange-Traded Fund) is a basket of stocks (or bonds, commodities, etc.) that trades on an exchange like a single stock. Most ETFs track an index — e.g. SPY tracks the S&P 500, so buying one share gives you fractional ownership of all 500 companies. Cheap, tax-efficient, and an easy way to diversify.
How much of your portfolio's return pattern is explained by well-known style factors (size, value, momentum, quality, low-volatility). Tells you what tilts you have — even if you didn't intend them.
The share of the evidence behind a scenario (or the whole analysis) that is independent or market-derived, rather than company-reported or model-derived.
Higher = the view leans on outside checks instead of the company's own narrative. Low independence is a flag to dig deeper, not necessarily wrong.
Information from third parties with no stake in the company's story — Wall Street sell-side research, news reporting, regulators, and peer cross-reads.
The strongest kind of support: nobody here profits from the company looking good, so it's a genuine outside check.
Figures the market itself sets — the live share price, traded valuation multiples, and the consensus of professional forecasts.
Reflects what real buyers and sellers believe today, not a claim made by the company.
Numbers produced by Dawo's own models — the DCF valuation, peer regressions, and stress tests.
Internally consistent and transparent, but it's our own work, not outside corroboration.
NAV (Net Asset Value) is the per-share value of a fund's underlying holdings: total assets − total liabilities, divided by shares outstanding. For ETFs, the market price usually tracks NAV closely (within a few basis points); a persistent gap is called a premium or discount.
Forward-looking window — the next 12 months of estimated earnings or revenue. 'NTM EPS' is sell-side analysts' consensus forecast for the upcoming year.
The plain arithmetic difference between two percentages. Moving the odds from 30% to 35% is a change of 5 percentage points (5pp) — not a 5% change.
Keeps probability shifts unambiguous: '5pp' means the same thing whether the starting point was 10% or 80%.
Rank within all simulated outcomes. The 5th percentile = worse than 95% of paths. The 95th percentile = better than 95% of paths.
How far the analyst moved the odds away from the historical base rate, based on this specific company's evidence. The move is capped so it can't override the historical anchor.
Large tilts should be backed by independent evidence — otherwise the odds are just an opinion dressed up as a forecast.
A basis point = 0.01%. 100bp = 1%. Stress scenarios specify how much the 10y Treasury yield is assumed to move (e.g. +200bp = +2% rates).
How much upside the bull case offers per dollar of downside the bear case implies. R/R of 2.0 = $2 of potential gain for every $1 of potential loss.
Above 1.5 = favorable; 1.0-1.5 = balanced; below 1.0 = unfavorable risk/reward.
Days until the next concrete event (earnings, FDA decision, contract renewal, etc.) that could resolve the thesis. Short = thesis tested soon; long = patient capital required.
Backward-looking window — the most recent 12 months of actual reported financials. Removes seasonality without waiting for the next fiscal year-end.
Implied USD index move during the scenario. Positive = USD rallies (typical in safe-haven scenarios). Hits international equity holdings on the conversion dimension.
The specific way Dawo's view differs from the sell-side consensus. Not just bull/bear sentiment — a structural claim about why the market may be wrong (e.g. 'consensus underweights segment X' or 'historical multiple X compresses in a recession').