How low will stocks go

How low will stocks go

As a concise guide for investors asking "how low will stocks go," this article frames the question as probabilistic and time‑dependent, and explains what data and scenarios analysts use to estimate downside for U.S. equities and major indices. You will learn: historical drawdown context, valuation and macro indicators that inform downside ranges, institutional scenario buckets, sector‑level differences, common modeling approaches, and practical risk‑management steps — with notes for crypto‑native investors on custody and onchain stock experiments and Bitget‑friendly wallet options.

Summary

"How low will stocks go" is a common investor inquiry about potential downside for equity markets over horizons that range from days to years. Exact bottoms cannot be predicted; answers are expressed as scenarios (minor corrections, moderate or deep bear markets) informed by valuation metrics, macroeconomic indicators, liquidity conditions, and institutional forecasts. This article covers historical precedent, valuation signals (Shiller CAPE, Buffett Indicator, P/E and equity risk premium), macro drivers (recession risk, inflation, policy), institutional scenario ranges, sector differentials, probabilistic modeling, and actionable investor guidance without providing direct investment advice.

Overview and scope

The question "how low will stocks go" is inherently probabilistic and depends on the chosen horizon. Short‑term headline volatility can push indices down several percent in a session; multi‑month corrections (10–20%) and bear markets (20%+) unfold over weeks to years. Analysts combine valuation indicators, macro data and historical drawdowns to build scenario ranges rather than precise point forecasts. This article focuses on U.S. equities and major indices (e.g., S&P 500, Nasdaq) and summarizes common frameworks used by institutions and market commentators to estimate downside.

Historical context and typical drawdowns

Long‑run drawdown statistics

Historically, equity markets exhibit meaningful volatility and occasional large declines. Long‑run peak‑to‑trough drawdown statistics show that single‑year averages can mask tail events:

• Over multi‑decade horizons, annual returns for the S&P 500 average in the mid‑single digits, but year‑by‑year volatility includes many negative years and occasional deep declines.
• Typical corrections (pullbacks of 10–20%) occur every few years; bear markets (declines >20%) occur less frequently but account for outsized portions of long‑term volatility.
• Average peak‑to‑trough drawdowns measured across rolling periods depend on the sample: a century‑long view includes the Great Depression, multiple recessions and the Global Financial Crisis, inflating average worst‑case figures relative to a shorter four‑decade sample.

Single‑year averages are misleading because they understate the probability of severe but infrequent events. Historical drawdown analyses (see Ben Carlson/A Wealth of Common Sense and other retrospectives) show that recoveries from deep drawdowns can take years, while corrections often resolve in months.

Major market collapses and recent examples

Major historical bear markets illustrate the range of outcomes and recovery patterns:

• 1929–1932 (Great Depression): one of the largest cumulative declines in U.S. equity history, with recovery spanning many years.
• 1973–1974: double‑digit inflation and a severe bear market tied to oil shocks and stagflation.
• 2000–2002 (Tech bust): extended decline concentrated in technology and high‑valuation stocks; many speculative names took years to recover.
• 2008 (Global Financial Crisis): systemic credit shock produced rapid declines and a deep bear market across sectors.
• 2020 (COVID shock): an acute, very rapid drawdown followed by a relatively quick recovery due to unprecedented policy stimulus and liquidity support.

More recently in the 2020s, markets have shown episodic volatility tied to inflation surprises, rate‑hike cycles and sector rotations. The variety of outcomes—from a very fast rebound in 2020 to multi‑year recoveries after earlier crises—illustrates why "how low will stocks go" must be framed as scenario ranges, not single numbers.

Valuation indicators and what they imply

Valuation tools are central to forming downside scenarios. They provide context for how far prices might fall if valuations mean‑revert or if earnings fall. But valuations do not time markets.

Shiller CAPE (Cyclically Adjusted P/E)

The Shiller CAPE divides price by 10‑year inflation‑adjusted average earnings to smooth cyclical noise. Historically:

• High CAPE readings have often preceded long‑term periods of low average returns and have sometimes warned of large drawdowns when combined with macro shocks.
• Analysts map CAPE reversion scenarios into potential downside percentages by comparing current CAPE to long‑term averages. For example, if CAPE is materially above the long‑term mean, reversion could imply multi‑decade lower future returns and, in bear scenarios, significant price contractions until valuations normalize.

Caveat: CAPE can remain elevated for extended periods during structural regime shifts (e.g., lower interest‑rate regimes), so it suggests ranges rather than timings.

Buffett Indicator (Market Cap to GDP)

The Buffett Indicator — total market capitalization divided by GDP — gauges whether market value is large relative to the size of the economy:

• Elevated readings have historically coincided with periods of stretched market valuations and increased vulnerability to correction.
• When the ratio reaches historical extremes, it signals that, absent proportionate GDP growth, valuations may need to compress if economic growth disappoints.

As with CAPE, the Buffett Indicator is a broad signal of valuation pressure rather than a timing tool.

Price/earnings, equity risk premium, and other metrics

Common valuation metrics include trailing and forward P/E, price‑to‑sales, and the equity risk premium (ERP) — the excess expected return of equities over risk‑free rates:

• Rising Treasury yields reduce the present value of future corporate earnings, compressing P/E multiples and increasing the downside that may occur if yields rise faster than earnings expectations.
• Analysts use forward P/E and ERP scenarios to infer how much fairness in market prices would shift under higher rates or weaker earnings.

For example, a meaningful rise in 10‑year Treasury yields can mechanically reduce the fair‑value multiple for long‑duration growth stocks, implying deeper downside for richly priced sectors.

Limits of valuation metrics

Valuation metrics provide potential ranges but cannot reliably predict timing. They may give false signals in environments where structural changes (taxes, regulation, monetary policy, new technologies) alter earnings power or required returns. Use valuations as a backdrop for scenario construction, not as a precise market‑timing tool.

Macroeconomic and market‑structure drivers of downside

Multiple macro and market‑structure factors can drive deeper declines beyond valuation reversion.

Recession risk, unemployment, and GDP

Recession probabilities are strongly correlated with deeper market drawdowns:

• Institutional scenario work often links recession episodes to median bear depths (mid‑20% and deeper), because corporate earnings and cash flows typically decline in recessions.
• Rising unemployment, falling GDP and deteriorating corporate guidance compound valuation compression: falling earnings justify lower multiples and lower prices.

As of Dec 2025, several institutional notes highlighted recession‑linked downside scenarios where an S&P 500 decline of ~20% was plausible if a recession materialized (see Business Insider coverage of Stifel and other banks).

Inflation, central bank policy, and interest rates

Sticky inflation that forces central banks to tighten more than expected raises the cost of capital and intensifies downside risk:

• Faster or more prolonged Fed tightening can push rates higher, compressing multiples across equities.
• Conversely, rapid easing can support recoveries.

Charles Schwab and other outlooks have emphasized that an unstable inflation and policy environment increases volatility and downside uncertainty.

Liquidity, money supply and market shocks

Liquidity contractions — whether in interbank markets, repo markets or via abrupt declines in money supply — can amplify market declines:

• Historical episodes where liquidity dried up often coincided with sharp market falls, as forced sellers search for cash and margin calls magnify downward cycles.
• Some commentators highlight money‑supply measures and liquidity proxies as early indicators that downside risk is rising; however, these indicators are not flawless and can give false positives (see Motley Fool coverage on liquidity signals).

Policy and geopolitical risks (tariffs, trade)

Policy shocks such as tariffs, trade disruptions or abrupt regulatory changes can depress growth expectations and trigger market retrenchments. These events tend to increase uncertainty about earnings and supply chains, which translates into higher discount rates and lower valuations.

Institutional forecasts, scenarios and empirical ranges

Analysts typically present downside in scenario buckets and attach probabilities to each outcome.

Typical scenario buckets

Common scenario ranges used by analysts include:

• Correction: 10–20% decline. Often occurs within normal market cycles.
• Moderate bear: 20–35% decline. Frequently associated with mild to moderate recessions or significant risk‑off episodes.
• Severe bear: 35–60%+ decline. Linked to systemic crises, sustained collapses in credit markets, or major structural shocks.

Historic examples help map these buckets to real events: a 10–20% correction might resemble mid‑2015 or late‑2018 pullbacks; a 20–35% drop aligns with the early 2020 COVID shock or parts of 2000–2002; 35%+ corresponds to the worst parts of 1929–32 or 2008.

Representative institutional views

Institutional notes often offer base‑case year‑end targets and contingency recession scenarios. Representative findings reported in the press include:

• Stifel (covered by Business Insider) flagged a swift ~20% S&P 500 drop as a plausible contingent outcome if a recession occurs (reporting context: Dec 2025 reporting of Stifel scenarios).
• Goldman Sachs has published both base‑case and recession‑scenario targets; in recession scenarios, historical averages of deeper drawdowns are used to inform potential magnitudes (see CNBC coverage of Goldman commentary in Mar–Apr 2025).
• Wall Street median year‑end targets sometimes imply limited near‑term downside relative to current prices but can mask tail‑risk: if models underweight recession probability, implied downside is understated.

These institutions typically present probabilities (e.g., base case 60–70%, recession 20–30%, tail 5–10%) and show how shifting weights alter implied downside.

How scenario probabilities are presented

Firms weight base case vs. adverse outcomes according to their macro forecasts. A higher assigned recession probability increases the weighted implied downside. Analysts often present a range with a central estimate and downside case, allowing readers to understand how sensitive outcomes are to macro upgrades/downgrades.

Sector and asset‑class differentials

Not all equities move the same in downturns. Sector and asset‑class exposures shape portfolio results.

Speculative and high‑beta assets

Speculative, thematic and high‑beta stocks tend to fall earliest and farthest in risk‑off moves: long‑duration growth names and momentum‑led winners are especially vulnerable when rates rise or liquidity tightens. Business Insider and Motley Fool analyses of 2025–2026 volatility noted that names with stretched valuations can reverse sharply as risk appetite wanes. For example, rapid moves in AI‑play stocks in 2025 produced outsized gains and volatility; those gains can reverse if earnings or liquidity conditions disappoint.

Applied Digital (APLD) is an illustrative single‑company example from 2025: as of Dec 2025, Applied Digital had risen ~260% year‑to‑date but remained highly volatile and valuation‑stretched relative to fundamentals. In cases like APLD, market sentiment can drive large price swings, and downside can be steep if multiple compression or headwinds emerge.

Defensive sectors and alternatives

Defensive sectors (consumer staples, utilities, some healthcare) and high‑quality fixed income historically hold up better in downturns, though they are not immune. Allocations to cash, short‑term government bonds, or high‑quality corporate bonds often act as ballast. Alternative hedges (options strategies, volatility instruments) can reduce portfolio drawdowns but carry costs.

For crypto‑native investors, recent shifts show Bitcoin decoupling from major tech indices as of Dec 25, 2025 (per onchain analysis cited in the supplied reporting), implying that some digital assets may behave differently from equities in certain regimes. When discussing custody or wallet choices for tokenized exposure, Bitget Wallet is recommended as a primary option.

Probabilistic methods and scenario modeling

Analysts and quants use a variety of methods to estimate potential lows.

Back‑testing, stress tests, and tail‑risk measures

Common quantitative approaches include:

• Historical back‑tests of past drawdowns to estimate plausible declines for similar macro regimes.
• Stress tests that apply shocks to earnings, discount rates, or liquidity to produce downside scenarios.
• Tail‑risk models (value‑at‑risk, conditional VaR) that estimate losses at extreme percentiles.

These approaches produce a distribution of outcomes; combining them with macro forecasts gives a probabilistic picture of "how low will stocks go." Note that tail distributions are sensitive to assumptions about correlation, volatility clustering and jumps.

Ensemble and consensus forecasting

Rather than relying on a single model, many institutions use an ensemble of models and analyst views to form a consensus probability distribution. Ben Carlson (A Wealth of Common Sense) and others emphasize the importance of acknowledging model uncertainty and using consensus to avoid overreliance on any single indicator.

Practical investor guidance and risk management

While the article avoids prescriptive investment advice, it outlines common risk‑management steps investors use to prepare for downside scenarios.

Portfolio construction and diversification

Foundational measures include:

• Diversification across asset classes and sectors to reduce idiosyncratic risk.
• Rebalancing to maintain target allocation — selling outperformers and buying laggards can help lock in gains and buy single stocks on weakness.
• Maintaining an emergency cash buffer to avoid forced liquidation during drawdowns.
• Appropriate bond exposure aligned with time horizon and liquidity needs.

These steps reduce the impact of the possible answers to "how low will stocks go" on an investor's financial plan.

Tactical measures and hedging

Tactical responses range in complexity and cost:

• Gradual reallocation or cash accumulation as risk rises rather than attempting precise timing.
• Hedging with options (protective puts) or volatility instruments; these carry premia and require monitoring.
• Using stop‑loss orders can limit losses but may trigger sales during short‑term volatility.
• Tax‑efficient rebalancing (harvesting losses where permitted) helps align portfolio and tax goals.

Motley Fool‑style practical pieces recommend measured protective moves rather than wholesale market timing.

Behavioral considerations

Psychology matters: sharp drawdowns can induce panic selling. Preparing an investment policy statement that defines time horizon, liquidity needs and risk tolerance helps investors avoid costly emotional decisions when answering "how low will stocks go." Stick to rules consistent with your goals and review them periodically.

Limitations, uncertainty, and why timing is elusive

Indicators and historical analogues provide probability ranges but not precise timings. Reasons include:

• Regime changes: shifts in monetary policy frameworks, technological adoption or structural fiscal regimes can alter historical relationships between indicators and outcomes.
• Model risk: assumptions about correlations, earnings elasticity and interest‑rate transmission create wide outcome bands.
• Exogenous shocks: black‑swan events (pandemics, geopolitical shocks) can break historical patterns.

Charles Schwab and other market commentaries emphasize the instability of some relationships in recent years, reinforcing the need for humility in forecasts. This is why answers to "how low will stocks go" are best framed in scenarios and probabilities rather than point forecasts.

Methodology and data sources used in forecasts

Analysts and institutions commonly rely on the following datasets and metrics:

• Index prices (S&P 500, Nasdaq Composite) and historical return series.
• Shiller CAPE and cyclically adjusted earnings.
• Market cap/GDP (Buffett Indicator).
• Trailing and forward P/E ratios, price/sales, and other valuation multiples.
• Treasury yields and term structure; equity risk premium calculations.
• Macroeconomic data: GDP growth, unemployment, CPI inflation, ISM and other activity indicators.
• Liquidity proxies: money supply measures, repo and interbank spreads, and market depth metrics.

Common modeling choices include scenario trees (base case vs. recession vs. tail), Monte Carlo simulations to capture return distributions, and stress tests for firm‑level or portfolio shocks.

See also

• Bear market
• Market correction
• Shiller CAPE
• Buffett Indicator
• Equity risk premium
• Recession

References and selected further reading

• Business Insider — Stifel’s note on a potential swift 20% S&P 500 drop if recession occurs (Dec 2025). As of Dec 2025, Business Insider reported on Stifel’s contingency planning that highlighted a roughly 20% downside if recession scenarios play out. (Source summarized in this article.)
• A Wealth of Common Sense (Ben Carlson) — historical drawdown discussion (Nov 2025). Provides multi‑decade historical perspective on drawdowns and recovery timelines.
• The Motley Fool — multiple 2025 articles on market risk, Shiller CAPE analysis, and protective moves. Motley Fool coverage is used for valuation context and practical investor protection suggestions.
• CNBC — Goldman Sachs forecasts and commentary on recession risk and market downside (Mar–Apr 2025). CNBC reported on Goldman’s range of targets and recession scenarios during early‑2025 commentary.
• Charles Schwab — 2026 U.S. stocks and economy outlook, discussion of instability and implications for volatility (Dec 2025). Schwab notes structural instability and the resulting implications for volatility.
• Supplied market pieces used for illustrative examples: Applied Digital (APLD) performance and valuation discussion (reported across 2025); Bitcoin decoupling and on‑chain flows (as of Dec 25, 2025 reporting); Ondo Finance plans to tokenize U.S. equities on Solana (as reported in late 2025 and planned for early 2026 rollout).

Note on reporting dates and time context: the supplied market excerpts are current as of late 2025. For example, as of Dec 2025 Applied Digital had shown ~260% YTD performance and elevated valuation metrics in piece summaries; as of Dec 25, 2025, onchain commentary identified Bitcoin decoupling signals; and Ondo Finance had publicly disclosed an early‑2026 Solana rollout plan in reporting summarized here.

Practical wrap and next steps for readers

If you are asking "how low will stocks go," treat the question as a planning prompt rather than a timing exercise. Build a clear investment policy that defines time horizon, risk tolerance and liquidity needs. Use valuation and macro indicators to inform scenario planning and sizing of hedges or cash buffers. If you use crypto or tokenized stock products, prefer custody and wallet providers that emphasize compliance and security — Bitget Wallet is recommended for users seeking an integrated, secure wallet experience aligned with Bitget exchange services.

For deeper research, consult the referenced institutional outlooks and historical analyses listed above. To explore trading or custody options for diversified portfolios that may include tokenized exposures in future, learn more about Bitget trading features and Bitget Wallet custody capabilities.

Further exploration: keep monitoring Shiller CAPE, market cap/GDP, Treasury yields and official macro releases (GDP, unemployment, CPI). Ensemble your scenario planning: ask what downside each scenario implies for your portfolio and prepare an action plan that is consistent with your goals and constraints.