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

1. 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.

2. 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.

3. 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.

4. 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.

5. 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