Summary of "Larry Williams on Inflation and the Coming Bear Market | Cycles TV Feb. 17, 2026"

Core thesis

Cycles (time-based patterns) are useful decision-support tools for business and investing, but they define “zones” of higher probability rather than precise predictors of magnitude. Combine cycle timing with exogenous fundamental drivers — inflation, interest rates, earnings, supply/demand, money supply — to improve reliability for strategy, capital planning, and trading.

Use cycles to identify higher-probability timing windows; overlay fundamentals to assess move reliability and magnitude.

Tactical stance (Larry Williams)

Expect inflation to pick up in 2026–2027. This can be bullish for equities until inflation becomes clearly problematic.
Consider accelerating essential capital spending now to avoid higher costs later.
Watch a potential bear market window roughly in 2027–2032; use cycles to plan entry/exit windows rather than making absolute calls.

Frameworks, processes, and playbooks

Cycle + Exogenous Data framework
- Use mathematical cycle projectors to identify timing “zones.”
- Overlay exogenous fundamentals (inflation, rates, earnings, supply/demand) to assess reliability and expected magnitude.
- Treat cycles as probability zones/conditions, not automatic triggers.
Wave breakdown inside long cycles
- Decompose long cycles (e.g., 18‑year) into constituent waves; some waves show higher price correlation than the parent cycle.
Reliability measurement
- Measure the percentage of historical occurrences where a given cycle/wave produced the expected directional move.
- Prioritize cycles/waves with higher historic hit rates.
Mechanical discipline for execution
- Use mechanical trend-change signals or rules to keep positions through volatile phases.
- Behavioral control and execution rules are essential — identical indicators can produce different outcomes for different traders.
Historical validation playbook
- Backtest cycles and historical reports (e.g., Foundation for the Study of Cycles archives) forward-looking to test persistence before operational deployment.

Key metrics, correlations, thresholds, and timelines

Cycle-fit / correlation examples:
- 18‑year cycle: ~40% correlation with price (lower predictive fit).
- 9‑year cycle: ~61% correlation (higher fit).
- 40‑month cycle: ~50% fit.
- 80‑day cycle: ~62% fit (useful for short-term trading).
Inflation cycle:
- Approximate 6.3‑year cycle identified; Williams projects inflation picking up in 2026 and into 2027.
- Threshold: inflation up to ~5% is seen as bullish for stocks; above ~5% risks broader economic problems and policy changes.
Auto sector cycle:
- Historical 13‑year cycle in vehicle sales (Foundation magazine); projection implies decline in vehicle sales into the early-to-mid 2030s, with a trough around 2034–2035.
- Ford/GM: a 13‑year cycle mapped to share prices aligned with the 2020 low and a rally through ~2025–2027.
Forward-looking windows:
- Near term: inflation pick-up anticipated later in 2026 and 2027 → potentially bullish equities and rising rates.
- Medium term risk window: 2027–2032 flagged as a possible bear market period to monitor.

Concrete examples and evidence

Inflation cycle vs Dow Jones
- A 6.3‑year inflation cycle overlaid with the Dow showed earlier instances where inflation turnups preceded major equity moves (examples: mid‑1990s, 1999–2001).
Auto sales and auto stocks
- A 1969 Foundation article identified a 13‑year cycle in automobile factory sales. Testing that cycle on FRED total vehicle sales and Ford/GM share prices found persistence and alignment with the 2020 low and the subsequent rally into ~2027, then decline toward a trough around 2034–2035.
Behavioral execution example
- In a live trading seminar, traders using identical indicators produced four different outcomes (big gain, moderate gain, stopped out, wrong‑way trade), underscoring that execution rules and psychology materially affect results.

Actionable recommendations

Corporate strategy / capital planning
- If inflation/rates are expected to rise (per Williams: 2026–2027), accelerate essential capital investments now to avoid higher future costs.
- Plan pricing, procurement, and supply‑chain strategies around expected inflation windows (shorten lead times, hedge where appropriate).
Portfolio / treasury
- Use cycles as timing zones to adjust asset allocation — equities may be favorable into an inflation pickup until inflation reaches problematic levels; bonds become attractive when yields rise materially.
- Implement mechanical entry/exit rules to reduce emotional selling during interim volatility.
Product / marketing / sales
- Align product launches, inventory, and promotions to higher-probability demand windows tied to macro cycles (e.g., auto sales cycles).
Policy / leadership
- Include cycle analysis as a scenario in macro forecasting and risk planning — historical signals could have offered early warnings.
Analysts / quants
- Backtest legacy cycle signals (Foundation archives) and quantify wave-level reliability before deployment.
- Separate timing probability (cycle) from expected amplitude (fundamental analysis).

Behavioral and organizational tactics

Enforce mechanical rules or objective trend-change signals to keep teams disciplined through noisy periods.
Provide training/mentoring (e.g., approaches like Jake Bernstein’s) to improve execution consistency.
Build cross-functional teams that combine quantitative cycle timing with fundamental/exogenous analysis.

High-level market implications (operational)

Rising inflation and higher rates can be stimulative for equities for a time (higher income/spending), but risk increases if inflation accelerates beyond manageable levels (approximately 5% cited).
Cycles can provide early-warning windows for both opportunities (buy windows) and risk management (when to hedge or reduce equity exposure), but must be validated and combined with fundamentals.

Limitations and cautions

Cycles indicate timing zones, not magnitude. Many short-term “wiggles” are randomness; smoothing choices affect perceived cycles.
Historical persistence varies by cycle length and market; always measure reliability before operational use.
Avoid absolute or overly bearish predictions — long historical stretches show market recoveries; use validated probabilities.

Presenters and data sources

Speaker: Larry Williams
Interviewer: Richard (Cycles TV)
Referenced experts and contributors: Jake Bernstein, Wells Wilder, Gresham Northcut, Gertrude Sher (Foundation for the Study of Cycles), Arthur Burns, George Marishelle, Warren Mosler (MMT context)
Data sources referenced: Federal Reserve Economic Data (FRED), Foundation for the Study of Cycles archives