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In recent years, the cryptocurrency market has experienced explosive growth. The success of mainstream cryptocurrencies like Bitcoin and Ethereum has attracted widespread attention from global investors. Emerging projects continue to emerge, offering a variety of investment opportunities. Investors are attracted by their high potential for returns, while also being aware of the market's high volatility and risks. Sui and Aptos are two blockchain projects that have recently garnered significan...
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Why the "Four-Year Cycle Theory" Might Be Wrong: Using Bayes' Formula to Gauge Market Risk by End-2025
Support NFT
Why the "Four-Year Cycle Theory" Might Be Wrong: Using Bayes' Formula to Gauge Market Risk by End-2025
The Whale Who Was Up $100 M: Why I’m Leaving HyperLiquid
Protocol Survived, Users Didn’t I just made a personal—and painful—decision: I will no longer trade on HyperLiquid. I’m not calling for a boycott; I’m simply following the drift of my own values. After clearing $95 M on HL—and crossing nine figures across venues—my P&L is still positive this year. But on 10 October I lost $62 M in a single liquidation cascade. That day showed me the industry has out-grown its “hope and prayer” risk architecture.What Actually Happened on 10·10Binance’s interna...
From Meta to Blockchain Rising Stars: The Rise of Sui and Aptos
In recent years, the cryptocurrency market has experienced explosive growth. The success of mainstream cryptocurrencies like Bitcoin and Ethereum has attracted widespread attention from global investors. Emerging projects continue to emerge, offering a variety of investment opportunities. Investors are attracted by their high potential for returns, while also being aware of the market's high volatility and risks. Sui and Aptos are two blockchain projects that have recently garnered significan...
When the “Infinite-Ammo” mNAV Flywheel Reverses: Hidden Sell-Side Risks in the Crypto-Treasury Narra…
Executive Summary Treasury-driven alt-coins have turbo-charged this bull run. Ethereum has risen from US$1 800 to US$4 700 (+160 %) as listed “mini-MSTRs” like SBET and BMNR relentlessly buy ETH. Solana, BNB and HYPE have spawned copy-cat treasuries of their own. But the same flywheel that lifts prices can spin backwards. WINT—once a BNB-treasury poster-child—was delisted by Nasdaq and fell 91 %. Lion Group just trimmed US$500 k of its own HYPE stack. If mNAV (market-to-NAV ratio) drops below...
Summary
The author questions the validity of the cryptocurrency market's "Four-Year Cycle Theory," arguing it is statistically unreliable due to an excessively small sample size (only 3 complete cycles to date).
A Bayesian probability model is proposed, analyzing Q4 2025 in direct comparison to Q4 2019 to assess market risk.
Using Bayes' formula, the calculated probability of a bear market occurring from late 2025 to early 2026 is approximately 15-20%, significantly lower than the expectation based solely on the four-year cycle theory.
Key parameters include: an 18% prior probability of a bear market, a 45% probability of the economy transitioning from stagflation to recession, and a 33% probability of experiencing this stagflation-to-recession transition given that a bear market occurs.
The final strategic recommendation is "**Tactical Defense, Not Strategic Retreat**," implying a need for cautious risk management without large-scale exit from the market.
---
By the year 2025, we have experienced fewer than 4 full "four-year cycles." Basic statistics tells us that any conclusion drawn from an extremely small sample size (effectively only 3 data points) requires careful verification, not blind faith.
For predicting major market cycles with small samples, deriving an analogy comparing Q4 2025 to Q4 2019 using Bayesian probability methods holds more reference value than the four-year cycle theory.
Translating the "Q4 2025 to Q4 2019 analogy" into Bayesian formula notation gives:
P(Bear Market | Stagflation→Recession) = [P(Bear Market) / P(Stagflation→Recession)] * P(Stagflation→Recession | Bear Market)
Bayesian Probability Parameter Estimation
* P(Bear Market) - Prior Probability
* Since 1929: S&P 500 experienced 27 bear markets.
* Average frequency: Once every 3.5 years.
* Annual probability: ~28.6%.
* Quarterly probability (Q4-Q1 span): ~15-20%.
Conservative estimate: *P(Bear Market) ≈ 18%**.
* P(Stagflation→Recession) - Merrill Lynch Clock Transition Probability
* Historical probability of "Stagflation→Recession" transition:
* 1970s Stagflation: Ultimately led to 3 recessions (1973-74, 1980, 1981-82).
* 2000-2001: Dot-com bubble burst, mild recession.
* 2007-2008: Financial crisis, deep recession.
* 2011-2012: European debt crisis, full recession avoided.
* 2018-2019: Trade war concerns, successful soft landing.
* Statistical Estimate:
* ~6 "Stagflation→Recession" scenarios in the past 50 years.
* 4 transitions resulted in recession (66%).
* 2 resulted in soft landing (34%).
* Current Environment Adjustments:
* Fed proactive rate cuts (vs. 1970s reactive hikes).
* Labor market resilience (vs. 2008 systemic risk).
* Tariff policy uncertainty.
* Global de-dollarization pressure.
Estimate: *P(Stagflation→Recession) ≈ 40-50% (Midpoint: 45%)**.
* P(Stagflation→Recession | Bear Market) - Likelihood Probability
* Probability of experiencing "Stagflation→Recession" given a bear market occurs.
* Historical Bear Market Classification:
* Recession-associated Bear Markets (12): 1929, 1937, 1973-74, 1980, 1981-82, 1990, 2000-02, 2007-09, 2020, 2022.
* Non-Recession Bear Markets (15): Other technical adjustments.
* Among the 12 recession-associated bear markets:
* Experienced stagflation phase: ~4 times (1973-74, 1980, 1981-82, 2007-08).
* Did not experience stagflation: 1929 (deflation), 2020 (pandemic shock), 2022 (pure inflation).
Estimate: *P(Stagflation→Recession | Bear Market) ≈ 33%**.
Bayesian Calculation
* Standard Formula:
P(Bear Market | Stagflation→Recession) = P(Stagflation→Recession | Bear Market) × P(Bear Market) / P(Stagflation→Recession)
= 0.33 × 0.18 / 0.45
= 0.0594 / 0.45
= 0.132 = 13.2%
* Scenario Analysis Matrix
(Note: This would typically be a table comparing different scenarios and their associated probabilities, based on the author's original analysis.)
* Key Difference Analysis: Why the Probability is Lower
(Note: This section would detail the author's reasoning for the lower calculated probability compared to the four-year cycle expectation, likely citing factors like different macro conditions, Fed policy, etc.)
Comprehensive Conclusion
P(Bear Market in 25Q4-26Q1) ≈ *15-20%**
* Confidence Interval:
* Lower Bound (Optimistic): 12%
* Median (Baseline): 17%
* Upper Bound (Pessimistic): 25%
Strategy
* Tactical Defense, Not Strategic Retreat.
Summary
The author questions the validity of the cryptocurrency market's "Four-Year Cycle Theory," arguing it is statistically unreliable due to an excessively small sample size (only 3 complete cycles to date).
A Bayesian probability model is proposed, analyzing Q4 2025 in direct comparison to Q4 2019 to assess market risk.
Using Bayes' formula, the calculated probability of a bear market occurring from late 2025 to early 2026 is approximately 15-20%, significantly lower than the expectation based solely on the four-year cycle theory.
Key parameters include: an 18% prior probability of a bear market, a 45% probability of the economy transitioning from stagflation to recession, and a 33% probability of experiencing this stagflation-to-recession transition given that a bear market occurs.
The final strategic recommendation is "**Tactical Defense, Not Strategic Retreat**," implying a need for cautious risk management without large-scale exit from the market.
---
By the year 2025, we have experienced fewer than 4 full "four-year cycles." Basic statistics tells us that any conclusion drawn from an extremely small sample size (effectively only 3 data points) requires careful verification, not blind faith.
For predicting major market cycles with small samples, deriving an analogy comparing Q4 2025 to Q4 2019 using Bayesian probability methods holds more reference value than the four-year cycle theory.
Translating the "Q4 2025 to Q4 2019 analogy" into Bayesian formula notation gives:
P(Bear Market | Stagflation→Recession) = [P(Bear Market) / P(Stagflation→Recession)] * P(Stagflation→Recession | Bear Market)
Bayesian Probability Parameter Estimation
* P(Bear Market) - Prior Probability
* Since 1929: S&P 500 experienced 27 bear markets.
* Average frequency: Once every 3.5 years.
* Annual probability: ~28.6%.
* Quarterly probability (Q4-Q1 span): ~15-20%.
Conservative estimate: *P(Bear Market) ≈ 18%**.
* P(Stagflation→Recession) - Merrill Lynch Clock Transition Probability
* Historical probability of "Stagflation→Recession" transition:
* 1970s Stagflation: Ultimately led to 3 recessions (1973-74, 1980, 1981-82).
* 2000-2001: Dot-com bubble burst, mild recession.
* 2007-2008: Financial crisis, deep recession.
* 2011-2012: European debt crisis, full recession avoided.
* 2018-2019: Trade war concerns, successful soft landing.
* Statistical Estimate:
* ~6 "Stagflation→Recession" scenarios in the past 50 years.
* 4 transitions resulted in recession (66%).
* 2 resulted in soft landing (34%).
* Current Environment Adjustments:
* Fed proactive rate cuts (vs. 1970s reactive hikes).
* Labor market resilience (vs. 2008 systemic risk).
* Tariff policy uncertainty.
* Global de-dollarization pressure.
Estimate: *P(Stagflation→Recession) ≈ 40-50% (Midpoint: 45%)**.
* P(Stagflation→Recession | Bear Market) - Likelihood Probability
* Probability of experiencing "Stagflation→Recession" given a bear market occurs.
* Historical Bear Market Classification:
* Recession-associated Bear Markets (12): 1929, 1937, 1973-74, 1980, 1981-82, 1990, 2000-02, 2007-09, 2020, 2022.
* Non-Recession Bear Markets (15): Other technical adjustments.
* Among the 12 recession-associated bear markets:
* Experienced stagflation phase: ~4 times (1973-74, 1980, 1981-82, 2007-08).
* Did not experience stagflation: 1929 (deflation), 2020 (pandemic shock), 2022 (pure inflation).
Estimate: *P(Stagflation→Recession | Bear Market) ≈ 33%**.
Bayesian Calculation
* Standard Formula:
P(Bear Market | Stagflation→Recession) = P(Stagflation→Recession | Bear Market) × P(Bear Market) / P(Stagflation→Recession)
= 0.33 × 0.18 / 0.45
= 0.0594 / 0.45
= 0.132 = 13.2%
* Scenario Analysis Matrix
(Note: This would typically be a table comparing different scenarios and their associated probabilities, based on the author's original analysis.)
* Key Difference Analysis: Why the Probability is Lower
(Note: This section would detail the author's reasoning for the lower calculated probability compared to the four-year cycle expectation, likely citing factors like different macro conditions, Fed policy, etc.)
Comprehensive Conclusion
P(Bear Market in 25Q4-26Q1) ≈ *15-20%**
* Confidence Interval:
* Lower Bound (Optimistic): 12%
* Median (Baseline): 17%
* Upper Bound (Pessimistic): 25%
Strategy
* Tactical Defense, Not Strategic Retreat.
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