Ray Dalio Environment-First Portfolio: Backtest Results

Generated: 2026-01-01 Backtest Period: December 2013 - December 2024 (7.2 years) Data Source: EODHD API (monthly prices)

Executive Summary

The Dalio 20-Holding Environment-First Portfolio has significantly outperformed both the S&P 500 and traditional 60/40 portfolio across all key risk-adjusted metrics over the backtest period.

Key Findings

1. Superior Risk-Adjusted Returns

The Dalio 20 portfolio achieved:

• 1.27 Sharpe Ratio - excellent risk-adjusted returns
• Lower volatility than S&P 500 (13.3% vs 14.8%) with higher returns
• Calmar Ratio of 1.25 - exceptional return per unit of drawdown risk

2. Exceptional Downside Protection (2022 Test)

The 2022 bear market was the key test of the All-Weather concept:

The portfolio limited losses to just 6% while the 60/40 portfolio suffered a 23% drawdown - its worst year in decades. This validates the environment-first diversification approach.

3. Consistent Positive Returns

• Only 1 negative year (2022: -6.0%) vs multiple for benchmarks
• Mean annual return: 11.7%
• Median annual return: 13.0%
• Best year: 2021 (+27.2%)
• Worst year: 2022 (-6.0%)

Annual Returns Comparison

Pattern: The portfolio outperforms in difficult years (2015, 2016, 2018, 2022) but underperforms in strong bull markets (2017, 2019, 2023, 2024). This is the expected All-Weather behavior.

Environment Contribution Analysis

Each of the four economic environments contributed to portfolio performance:

Observations:

• Falling Inflation bucket was the top performer (driven by COST's exceptional 28% CAGR)
• Falling Growth defensives performed well with strong Sharpe (0.92)
• All environments contributed positively to portfolio returns
• The high max drawdown in Falling Inflation is driven by BCHN.SW (biotech volatility)

Maximum Drawdown Analysis

The maximum drawdown of -15.71% is remarkably contained:

• Less than half of S&P 500's drawdown (-23.93%)
• Less than 60/40's drawdown (-26.21%)
• Faster recovery than benchmarks

Holdings Performance Attribution

Top Contributors (Estimated)

1. COST.US (Costco) - 28% CAGR, Sharpe 1.00
2. KOG.OL (Kongsberg) - 18% CAGR, defense secular trend
3. GLD.US (Gold) - Consistent diversifier, lowest correlation
4. RY.TO (Royal Bank of Canada) - 15.5% CAGR, Sharpe 0.55
5. INVE-B.ST (Investor AB) - Strong Swedish conglomerate

Diversification Heroes (Lowest Correlations)

1. GLD.US - Average correlation 0.08 to portfolio
2. ORK.OL - Consumer staples stability
3. NESN.SW - Swiss franc hedge + defensive

Portfolio Characteristics

Current Weights by Environment

Rising Growth:     25.7%  [VTI, INVE-B, SMCAY, KOG, DPZ]
Falling Growth:    27.7%  [ORK, ELE, MRK, NESN, ATD]
Rising Inflation:  27.6%  [GLD, EQNR, AI, ENB, SALM]
Falling Inflation: 19.0%  [COST, DNB, BCHN, SPGI, RY]

Geographic Allocation

USA:          20.3%
Norway:       19.4%
Canada:       13.3%
Switzerland:   9.8%
Sweden:        6.0%
Spain:         4.9%
Germany:       4.2%
France:        3.2%
Japan:         5.2%
Gold (Global): 12.6%

Caveats & Limitations

1. Limited Backtest Period

• Only 7.2 years of full data (SMCAY started Oct 2013)
• Does not include 2008 financial crisis
• Need longer history to validate across full market cycles

2. Look-Ahead Bias Considerations

• Holdings were selected based on recent correlation data
• Selection algorithm used 2019-2024 data which overlaps backtest

3. Excluded Securities

• TLT excluded (negative Sharpe in testing period)

4. Transaction Costs Not Included

• Monthly rebalancing assumption
• International trading costs not modeled
• Currency hedging costs not included

Conclusions

Validated Hypotheses

1. Environment diversification works - The four-quadrant approach delivered consistent returns across market conditions

2. Correlation minimization matters - 0.238 average pairwise correlation vs 0.38 in earlier portfolio

3. Quality filters protect capital - Sharpe >= 0.30 filter excluded securities that would have hurt returns

4. Gold is essential - GLD.US as largest position (12.6%) with lowest correlation was key to downside protection

Investment Implications

• Suitable for: Long-term investors seeking all-weather returns with managed volatility
• Expect: Underperformance in strong bull markets, outperformance in bear markets
• Implementation: Quarterly rebalancing with 30% environment drift trigger

Next Steps

1. Run longer backtest excluding SMCAY to test 2008-2013 performance
2. Stress test against historical scenarios (2008, 2000-2002)
3. Monte Carlo simulation for confidence intervals
4. Implement live tracking dashboard

This analysis is for research purposes only. Past performance does not guarantee future results.

Four Economic Scenarios: All-Weather Performance Analysis

The Bridgewater Framework

Ray Dalio's insight is that there are only four things that matter for asset prices:

1. Growth: Is the economy growing faster or slower than expected?
2. Inflation: Are prices rising faster or slower than expected?

This creates four quadrants. A truly diversified portfolio has assets that perform well in EACH quadrant.

Scenario Matrix

                    INFLATION RISING           INFLATION FALLING
                    ┌────────────────────────┬────────────────────────┐
                    │                        │                        │
    GROWTH          │   OVERHEATING          │   GOLDILOCKS           │
    RISING          │   (Commodities,        │   (Stocks, Bonds,      │
                    │    Gold, TIPS)         │    Quality)            │
                    │                        │                        │
                    ├────────────────────────┼────────────────────────┤
                    │                        │                        │
    GROWTH          │   STAGFLATION          │   RECESSION            │
    FALLING         │   (Gold, Commodities,  │   (Bonds, Gold,        │
                    │    Utilities)          │    Utilities)          │
                    │                        │                        │
                    └────────────────────────┴────────────────────────┘

Scenario 1: GOLDILOCKS (Growth Rising + Inflation Falling)

Historical Examples: 2012-2019, 1995-1999

What Happens:

• Corporate earnings grow
• Central banks are accommodative
• Risk appetite is high
• P/E multiples expand

Portfolio Performance Projection

Key Insight: In Goldilocks, growth assets (semis, quality compounders) drive returns. Gold drags but limits are acceptable given small allocation.

Scenario 2: RECESSION (Growth Falling + Inflation Falling)

Historical Examples: 2008-2009, 2001-2002, 2020 (brief)

What Happens:

• Corporate earnings contract
• Credit spreads widen
• Flight to safety
• Central banks cut rates

Portfolio Performance Projection

Comparison to Unhedged:

• S&P 500 in recession: -35 to -50%
• Our portfolio: -8 to -12%
• Hedging saves 25-40% of capital

Key Insight: Gold and utilities offset equity losses. Portfolio survives to deploy cash at bottom.

Scenario 3: OVERHEATING (Growth Rising + Inflation Rising)

Historical Examples: 2021-2022, 1970s (partial), late 1960s

What Happens:

• Economy grows but overheats
• Central banks forced to tighten
• Input costs rise faster than revenue
• Multiple compression despite earnings growth

Portfolio Performance Projection

Key Insight: Precious metals and inflation-beneficiary insurers carry the portfolio. Growth stocks struggle with rate hikes but commodity-linked semis hold up.

Scenario 4: STAGFLATION (Growth Falling + Inflation Rising)

Historical Examples: 1974-1975, 1980-1982

What Happens:

• Worst of both worlds
• Corporate earnings fall while costs rise
• Central banks in impossible position
• Most assets decline

Portfolio Performance Projection

Comparison to Unhedged:

• 60/40 portfolio in stagflation: -20 to -30%
• Our portfolio: -5 to +2%
• Gold allocation saves the portfolio

Key Insight: Stagflation is the hardest environment. Only gold truly wins. The 17% precious metals allocation prevents catastrophe.

Scenario Summary Matrix

Expected Value Calculation

E[Return] = (0.40 × 10%) + (0.25 × -10%) + (0.20 × 7.5%) + (0.15 × -1.5%)
          = 4.0% + (-2.5%) + 1.5% + (-0.2%)
          = 2.8% (conservative base)

Adding alpha from:

• Entry at good prices: +2-3%
• Dividend yield: +2.8%
• Rebalancing bonus: +0.5-1%

Total Expected Return: ~8-10% annually

Stress Test: Extreme Scenarios

2008-Style Financial Crisis

Actual S&P 500 drawdown: -57%. Our portfolio: ~-25%

1970s Stagflation Replay

Near breakeven in worst stagflation

2022-Style Rate Shock

Portfolio outperforms 60/40 (-15%) and S&P 500 (-19%)

Rebalancing Triggers by Scenario

Next: black-swan-hedging.md for tail risk analysis

Entry Strategy: When and How to Build Positions

Philosophy

"The stock market is a device for transferring money from the impatient to the patient." — Warren Buffett

Entry timing matters. A 20% better entry price on a 10-year hold adds ~1.8% annual alpha. This document provides systematic entry rules.

Position Categories by Entry Urgency

Category A: Buy Now (Attractive Entry)

These positions are at or near entry prices. Deploy immediately.

Total Immediate Deployment: ~CHF 165,000

Category B: Start Position, Complete on Dip

These are slightly above ideal entry. Start 50% now, complete on -10% dip.

Initial Deployment: ~CHF 20,000 (50% of targets)

Category C: GTC Limit Orders Only

These are materially overvalued. Do not chase. Wait for entry prices.

Total in Limit Orders: ~CHF 145,000

Category D: Already Owned

Keep existing positions. Manage with options if appropriate.

Total Already Owned: ~CHF 117,500

Entry Timing Signals

Macro Signals: When to Deploy Cash

Sector-Specific Signals

Semiconductors (4063, 8035, ASML)

Timeline: Semi cycle typically 18-24 months peak-to-trough. Current position: mid-cycle. Expected trough: Q2-Q4 2025.

Utilities (FTS, ENB)

Timeline: Utilities sensitive to rate expectations. Buy when Fed signals cuts.

Insurance (MUV2, SREN)

Timeline: Insurance cycles 5-7 years. Current: mid-hardening. Good entry now.

Position Building Techniques

Technique 1: Immediate Full Position

Use When: Price at/below entry, high conviction, strong balance sheet

Example (FTS):

• Target: 8% = CHF 44,000
• Current: At entry price
• Action: Buy full position immediately
• Cost: 500 shares × CAD 55 × 0.68 = CHF 18,700

Technique 2: 50/50 Split Entry

Use When: Price 5-15% above entry, moderate conviction

Example (WM):

• Target: 3% = CHF 16,500
• Current: 23% above strong buy
• Action: Buy 50% now (CHF 8,250), set limit for remaining 50%
• Limit: $180 for remaining 25 shares

Technique 3: Dollar-Cost Average

Use When: Volatile stock, uncertain timing, want to average in

Example (4063):

• Target: 5% = CHF 22,500
• Current: 17% above entry
• Action: Monthly purchases of CHF 5,625 when limit price hit
• Duration: 4 months to build full position

Technique 4: Scale-In on Weakness

Use When: High conviction but want better average price

Example (ASML):

• Target: 3% = CHF 13,500
• Current: 22% above entry
• Action:
  • At €700: Buy 25% (CHF 3,375)
  • At €650: Buy 25% (CHF 3,375)
  • At €600: Buy 50% (CHF 6,750)
• Result: Average entry €640 vs current €899

Monthly Deployment Schedule

Month 1 (Now)

Month 2

Month 3-6

Month 6-12

Limit Order Management

GTC Limit Order Settings

Limit Order Review Protocol

Monthly Review:

1. Check if any limits within 5% of triggering
2. Adjust for material fundamental changes
3. Raise limits if stock upgraded, lower if downgraded
4. Cancel if investment thesis changed

Adjustment Triggers:

• Earnings beat: Raise limit by 5%
• Earnings miss: Lower limit by 5%
• Moat impairment: Remove from list
• Valuation reset: New limit based on updated fair value

Options Entry Enhancement

Strategy 1: Cash-Secured Put Selling

Purpose: Get paid to wait for entry price

Example (NESN):

• Current: CHF 77
• Target entry: CHF 70
• Sell: April 2025 CHF 70 Put
• Premium: ~CHF 2.50/share
• Outcome A: If NESN <70, buy at CHF 70 - 2.50 = CHF 67.50 effective
• Outcome B: If NESN >70, keep CHF 2.50 premium (3.6% return in 4 months)

Candidates for Put Selling:

Strategy 2: Call Spread Collars

Purpose: Reduce cost of protection while capping upside

Example (GOOG already owned):

• Current: $315
• Buy: June $270 Put (cost: $8)
• Sell: June $360 Call (premium: $5)
• Net cost: $3/share
• Outcome: Protected below $270, capped at $360

Cash Management

Target Cash Levels

Current Cash Plan

Deployment Timeline: ~CHF 375,000 over 6-12 months via limit orders and DCA.

Entry Decision Flowchart

START: Want to buy Stock X
           │
           ▼
    Is price ≤ Entry Target?
           │
    ┌──────┴──────┐
    │             │
   YES           NO
    │             │
    ▼             ▼
  BUY NOW    Is price 5-15% above?
    │             │
    │      ┌──────┴──────┐
    │      │             │
    │     YES           NO (>15%)
    │      │             │
    │      ▼             ▼
    │   BUY 50%     SET GTC LIMIT
    │   + LIMIT        ONLY
    │      │             │
    └──────┴──────┬──────┘
                  │
                  ▼
           MONITOR & WAIT

Summary: Entry Priority List

Immediate Action (Week 1)

1. BUY: FTS, ENB, SREN, MUV2, D05, GJF (full positions)
2. START: WM, BRK.B, CNR (50% positions)
3. SET LIMITS: All 10 stocks in limit order table
4. OPTIONS: GOOG put protection

Ongoing (Monthly)

1. Review limit orders for fills
2. Top up "start" positions if still at entry
3. Add to any position down 10%+ from entry
4. Maintain 7-10% cash for opportunities

On Market Correction (-15%+)

1. Deploy 50% of cash into highest conviction
2. Lower limit orders by 10%
3. Add to existing positions
4. Consider selling puts for enhanced entry

Next: macrotrend-resilience.md for secular force analysis

Bridgewater-Style Correlation Matrix

Executive Summary

This matrix quantifies expected correlations between 8 asset classes in the portfolio, enabling risk parity allocation. The goal is to identify uncorrelated or negatively correlated pairs that provide genuine diversification.

The Correlation Matrix

                    SEMI   PYMNT   UTIL   GOLD   INSUR  INFRA  STAPL  QUAL
Semiconductors      1.00   +0.65  -0.40  -0.35  +0.10  -0.20  -0.30  +0.50
Payments            +0.65   1.00  -0.20  -0.25  +0.20  +0.05  +0.10  +0.70
Utilities           -0.40  -0.20   1.00  +0.15  +0.30  +0.60  +0.50  +0.10
Gold/Silver         -0.35  -0.25  +0.15   1.00  +0.25  +0.20  +0.10  -0.20
Insurance           +0.10  +0.20  +0.30  +0.25   1.00  +0.40  +0.35  +0.25
Infrastructure      -0.20  +0.05  +0.60  +0.20  +0.40   1.00  +0.45  +0.15
Consumer Staples    -0.30  +0.10  +0.50  +0.10  +0.35  +0.45   1.00  +0.30
Quality Compounders +0.50  +0.70  +0.10  -0.20  +0.25  +0.15  +0.30   1.00

Asset Class Definitions

Semiconductors (SEMI)

Portfolio Holdings: 4063 Shin-Etsu, 8035 Tokyo Electron, ASML Correlation Drivers:

• High beta to global growth expectations
• Capex cycle sensitivity (WFE spending)
• China/Taiwan geopolitical risk
• AI infrastructure buildout

Key Correlations:

• +0.65 with Payments: Both are growth/tech plays, benefit from digital economy
• -0.40 with Utilities: Opposite ends of growth spectrum
• -0.35 with Gold: Risk-on vs risk-off dynamic

Payments (PYMNT)

Portfolio Holdings: V, MA (excluded from portfolio due to RTP risk), GOOG (ad payments) Correlation Drivers:

• Consumer spending volume
• Cross-border travel/trade
• Fintech disruption risk

Key Correlations:

• +0.70 with Quality Compounders: Similar high-ROIC, growth characteristics
• -0.25 with Gold: Growth vs crisis hedge

Utilities (UTIL)

Portfolio Holdings: FTS, ENB, WM Correlation Drivers:

• Interest rate sensitivity (inverse)
• Regulated returns (stability)
• Weather/demand patterns

Key Correlations:

• +0.60 with Infrastructure: Both regulated, stable cash flows
• -0.40 with Semiconductors: Defensive vs cyclical
• +0.15 with Gold: Both do well in uncertainty

Gold/Silver (GOLD)

Portfolio Holdings: Physical gold (500g), Physical silver (25kg) Correlation Drivers:

• Real interest rates (inverse)
• Currency debasement fears
• Crisis/tail risk events
• Inflation expectations

Key Correlations:

• -0.35 with Semiconductors: Classic risk-off hedge
• -0.25 with Payments: Crisis vs growth
• +0.25 with Insurance: Both benefit from uncertainty

Insurance/Reinsurance (INSUR)

Portfolio Holdings: MUV2, SREN, GJF, ZVTG Correlation Drivers:

• Investment float returns (rate sensitive)
• Catastrophe event frequency
• Premium pricing cycles
• Claims inflation

Key Correlations:

• +0.40 with Infrastructure: Both stable, regulated
• +0.25 with Gold: Both benefit from volatility
• +0.10 with Semiconductors: Low correlation = diversification

Infrastructure (INFRA)

Portfolio Holdings: CNR (railways), ENB (pipelines) Correlation Drivers:

• Economic activity (freight volumes)
• Regulatory returns
• Long-term contracts

Key Correlations:

• +0.60 with Utilities: Similar regulated model
• +0.45 with Consumer Staples: Both defensive

Consumer Staples (STAPL)

Portfolio Holdings: NESN, 2801 Kikkoman, COST Correlation Drivers:

• Consumer spending (non-discretionary)
• Commodity input costs
• Pricing power

Key Correlations:

• +0.50 with Utilities: Both defensive
• -0.30 with Semiconductors: Defensive vs cyclical

Quality Compounders (QUAL)

Portfolio Holdings: BRK.B, SPGI, GOOG, POOL Correlation Drivers:

• Broad economic growth
• Corporate activity (M&A, IPOs for SPGI)
• Housing cycle (POOL)

Key Correlations:

• +0.70 with Payments: Similar growth characteristics
• +0.50 with Semiconductors: Growth orientation

Correlation Deep Dive: Stock-Level Analysis

Most Negatively Correlated Pairs

Highly Correlated Pairs (Avoid Doubling Up)

Portfolio Implication

To maximize diversification, pair:

• High-beta semis (4063, 8035) with defensive utilities (FTS, ENB)
• Growth tech (GOOG) with physical gold
• Cyclical quality (POOL) with stable insurance (MUV2, SREN)

Correlation by Economic Scenario

Scenario 1: Growth Rising + Inflation Stable (Best Case)

Portfolio Return: +15-20%

Scenario 2: Growth Falling + Inflation Stable (Recession)

Portfolio Return: -8 to -12% (hedged) vs -25% (unhedged)

Scenario 3: Growth Rising + Inflation Rising (Overheating)

Portfolio Return: +10-15%

Scenario 4: Growth Falling + Inflation Rising (Stagflation)

Portfolio Return: -5 to +5% (hedged) vs -20% (unhedged)

Correlation Estimation Methodology

Quantitative Inputs

1. 5-Year Rolling Correlations: Where available from price data
2. Beta to S&P 500: As proxy for growth sensitivity
3. Beta to 10Y Treasury: As proxy for rate sensitivity
4. Sector ETF Correlations: XLU, XLF, XLK as benchmarks

Qualitative Adjustments

1. Revenue Geography: China exposure increases correlation with EM risk
2. Customer Concentration: B2B vs B2C demand drivers
3. Commodity Sensitivity: Input cost pass-through ability
4. Regulatory Regime: Stable regulated returns reduce correlation

Data Sources

Key Takeaways for Portfolio Construction

1. Core Hedges: Gold (-0.35 to semis) and Utilities (-0.40 to semis) provide genuine diversification

2. Avoid Doubling: V+MA (+0.95) would concentrate payment risk; choose one or neither

3. Insurance is Uncorrelated: +0.10 to semis makes MUV2/SREN excellent diversifiers

4. Infrastructure Bridges: CNR/ENB correlate with both growth (+0.15) and defense (+0.60)

5. Quality Compounders are Growth: +0.70 correlation to payments means they move together in crisis

Next: four-scenarios.md for detailed scenario analysis

Black Swan Hedging Analysis

Philosophy

"The key is not predicting black swans, but being robust to them." — Nassim Taleb

A black swan is an event that is:

1. Rare: Outside normal expectations
2. Extreme: Catastrophic impact
3. Rationalized in hindsight: "We should have seen it coming"

Your priority is surviving these events with capital intact to deploy at generational buying opportunities.

Black Swan Scenarios Analyzed

Scenario 1: AI Bubble Burst

Trigger: AI fails to generate revenue, margin compression, earnings misses Historical Analog: 2000-2002 Dot-Com Bust

Impact Assessment:

Portfolio Protection:

Unhedged Losses:

Net Portfolio Impact: +6.8% (hedges) - 15.0% (losses) = -8.2%

Comparison: S&P 500 in dot-com bust: -49%. NASDAQ: -78%. Our portfolio: -8% to -15%

Scenario 2: Global Recession

Trigger: Credit crisis, banking collapse, consumer spending collapse Historical Analog: 2008-2009 Global Financial Crisis

Impact Assessment:

Portfolio Protection:

Cyclical Losses:

Net Portfolio Impact: +0.8% (defense) - 20.8% (losses) = -20.0%

Comparison: S&P 500 in 2008-09: -57%. Our portfolio: -20% Capital preserved: 57% more than index

Scenario 3: China-Taiwan Crisis

Trigger: Military conflict, trade embargo, chip supply disruption Historical Analog: None (unprecedented)

Impact Assessment:

Critical Analysis:

This portfolio has meaningful China/Taiwan exposure:

• 4063: China wafer sales ~15%
• 8035: Taiwan fabs ~30% of customers
• ASML: TSMC ~30% of revenue

Portfolio Protection:

Direct Exposure Losses:

Net Portfolio Impact: +9.5% - 10.5% = -1.0% (plus secondary effects ~-10%)

Total Estimated Drawdown: -15 to -20%

Comparison: Global equities in Taiwan crisis: -30 to -50% Our portfolio: -15 to -20%

Scenario 4: Currency Crisis / USD Devaluation

Trigger: Loss of USD reserve status, debt spiral, hyperinflation Historical Analog: 1970s, Argentina, Zimbabwe

Impact Assessment:

Portfolio Protection:

Your portfolio is well-positioned for this scenario:

USD Exposure:

Net Portfolio Impact: +31.5% - 6.9% = +24.6% real

Key Insight: Physical gold and CHF base provide exceptional currency crisis protection.

Hedge Effectiveness Summary

Hedge Allocation Summary

Total Hedge Allocation: 33-45%

Options Overlay for Tail Protection

For basic options experience, implement only two strategies:

1. Protective Puts on Concentrated Tech

When: VIX <15, portfolio feeling complacent What: 6-month puts at -15% strike on GOOG

Example:

• GOOG at $315
• Buy June 2025 $270 Put
• Cost: $8/share (2.5% of position)
• Protection: Limits loss to 15% + premium

Portfolio Cost: 2.5% × 5% allocation = 0.125% annual drag Protection Value: In AI bubble burst, saves ~3% portfolio loss

2. Collar on Overvalued Positions

When: Position at fair_value_high AND willing to cap upside What: Buy put, sell call to fund it

Example:

• POOL at $231 (near fair value)
• Buy June 2025 $200 Put (cost: $6)
• Sell June 2025 $260 Call (premium: $5)
• Net cost: $1/share
• Outcome: Capped at -13% loss, +12% gain

Early Warning Indicators

Monitor these signals to prepare for black swans:

Tail Risk Budget

Allocate portfolio "insurance premium" as:

Return on Insurance: In black swan (25% probability over 5 years):

• Cost: 3.5% × 5 = 17.5% over 5 years
• Savings: 25% × 25% = 6.25% expected value
• Plus: Ability to deploy at bottom = +10-20% extra gains

Net ROI: Positive. Insurance is worth it.

Next: uncorrelated-pairs.md for specific pair analysis

Macrotrend Resilience: 24-Trend Secular Force Analysis

Framework Overview

Beyond cyclical economic environments, long-term portfolio returns are affected by secular macrotrends that operate on 5-30 year timescales. This analysis applies the 24-trend framework to stress-test the portfolio.

The 24 Macrotrends (8 Categories)

Category 1: Monetary & Debt (Weight: 15%)

Category 2: Geopolitical (Weight: 15%)

Category 3: Technology Disruption (Weight: 15%)

Category 4: Demographic (Weight: 10%)

Category 5: Climate & Environment (Weight: 15%)

Category 6: Regulatory (Weight: 10%)

Category 7: Governance (Weight: 10%)

Category 8: Market Structure (Weight: 10%)

Portfolio-Level Macrotrend Scoring

Scoring Methodology

Each holding rated from -3 (severe headwind) to +3 (strong tailwind) on each trend. Portfolio score = Σ (holding weight × holding score × trend weight)

Aggregate Portfolio Score

Interpretation: Mild-moderate headwinds. Portfolio needs +4% additional margin of safety.

Detailed Trend Analysis

Category 1: Monetary & Debt (+8)

Trend 1: Sovereign Debt Burden

Trend 2: Currency Debasement

Trend 3: Interest Rate Regime

Category 1 Total: +8 (Tailwind)

Category 2: Geopolitical (-12)

Trend 4: US-China Decoupling

Trend 5: Supply Chain Reshoring

Trend 6: Energy Security Shift

Category 2 Total: -12 (Headwind from China exposure)

Category 3: Technology Disruption (-6)

Trend 7: AI/Automation

Trend 8: Real-Time Payments (RTP)

Trend 9: Quantum Computing

Category 3 Total: -6 (Slight headwind from RTP risk)

Category 4: Demographic (-4)

Trend 10: Aging Populations

Trend 11: GLP-1 / Obesity Drugs

Trend 12: Labor Shortage

Category 4 Total: -4 (GLP-1 impact on staples)

Category 5: Climate & Environment (-8)

Trend 13: Climate Transition Costs

Trend 14: Physical Climate Risk

Trend 15: Water Scarcity

Category 5 Total: -8 (Climate risk in insurance and energy)

Category 6: Regulatory (-10)

Trend 16: Antitrust Revival

Trend 17: ESG/Sustainability Mandates

Trend 18: Financial Regulation

Category 6 Total: -10 (GOOG antitrust is primary risk)

Category 7: Governance (+2)

Trend 19: SBC Dilution

Trend 20: Board Independence

Trend 21: Stakeholder Capitalism

Category 7 Total: +2 (Quality company bias helps)

Category 8: Market Structure (-15)

Trend 22: Passive Indexing

Trend 23: Private Market Growth

Trend 24: Valuation Compression

Category 8 Total: -15 (Valuation risk in semis and tech)

Macrotrend-Adjusted Margin of Safety

Standard Margin of Safety: 25%

Macrotrend Adjustment: +4%

Based on portfolio score of -45 (mild-moderate headwinds):

Required Margin of Safety: 25% + 4% = 29%

Mitigation Strategies

Highest Risk Trends

Portfolio Adjustments Made

1. V/MA excluded: RTP trend (-8) avoided entirely
2. GOOG limited to 5%: Antitrust risk contained
3. Semis in limit orders: Don't pay premium multiples
4. Gold at 10%: Monetary/debt hedge (+8 contribution)
5. Swiss stocks included: CHF/governance quality

Trend Watch List

Monitor These Quarterly

Annual Review Triggers

• Any trend score moves ±2 points
• New major trend emerges (e.g., CBDC adoption)
• Portfolio composition changes >10%

Summary

Required Action: Increase margin of safety to 29% (from 25%)

Return to: README.md for overview

Ray Dalio Macro Framework Analysis

Applying "Changing World Order" Principles to Portfolio Construction

Analysis Date: 2025-12-31 Framework: Bridgewater-style Big Cycle Assessment

Executive Summary

This analysis applies Ray Dalio's macro framework from "Principles for Dealing with the Changing World Order" to assess:

1. Where we are in the long-term debt cycle
2. Current position in the short-term business cycle
3. Country lifecycle positioning (US, China, Europe, Japan, Switzerland)
4. Currency and reserve status risks
5. Internal and external order dynamics
6. Portfolio implications and positioning

Key Finding: We are in the late stage of a long-term debt cycle (similar to 1930-1945 period) with rising great power conflict. This calls for defensive positioning, inflation hedges, and geographic diversification.

1. Long-Term Debt Cycle Position (50-75 Year Cycles)

Historical Context

Dalio identifies major long-term debt cycles:

• 1850-1900: British Empire peak, gold standard
• 1900-1945: Transition period, two world wars, debt deleveraging
• 1945-2000: American Empire rise, Bretton Woods to fiat
• 2000-Present: Late cycle, debt accumulation, power transition

Current Cycle Metrics

Debt Cycle Stage Assessment

Stage 1: Early Cycle (Low Debt)        ░░░░░░░░░░
Stage 2: Bubble Building               ░░░░░░░░░░
Stage 3: Top (2007-2008)               ░░░░░░░░░░
Stage 4: Depression/Deleveraging       ░░░░░░░░░░
Stage 5: Beautiful Deleveraging        ░░░░░░░░░░
Stage 6: Pushing on String (2020-now)  ██████████ ← WE ARE HERE
Stage 7: Currency Crisis/Reset         ░░░░░░░░░░

Current Position: Stage 6 - "Pushing on a String"

Characteristics of Stage 6:

• Interest rates near or at zero bound (were until 2022)
• Central bank buying assets (QE) to stimulate
• Wealth gaps widening
• Populism rising
• Currency debasement beginning
• Fiscal dominance (monetary policy constrained by debt)

Implications for Portfolio

2. Short-Term Business Cycle (5-8 Year Cycles)

Current Cycle Timeline

2020: Recession (COVID crash)
2021: Early recovery (stimulus boom)
2022: Mid-cycle (inflation surge, Fed hiking)
2023: Late cycle (recession fears, bank stress)
2024: Soft landing (disinflation, Fed pivot)
2025: ??? (Election transition, tariff risks)

Cycle Indicators Dashboard

Cycle Position Assessment

Current Position: Late Cycle / Early Pre-Recession

The data shows classic late-cycle patterns:

• Unemployment bottomed and rising (from 3.4% to 4.6%)
• Fed cutting after aggressive tightening
• Yield curve uninverting (historically happens 6-12 months before recession)
• Corporate earnings growth decelerating
• Consumer spending shifting to services

Historical Analogs

Portfolio Positioning by Cycle Phase

Portfolio Alignment: Well-positioned for mild recession with 30%+ in defensive assets.

3. Country Lifecycle Analysis

Dalio's Country Power Framework

Countries rise and fall through predictable stages based on 18 determinants:

1. Education
2. Innovation/Technology
3. Competitiveness
4. Economic Output
5. Trade
6. Military Strength
7. Financial Center Status
8. Reserve Currency Status
9. Internal Order (social cohesion)
10. External Order (geopolitical position)

Country Scorecards

United States

US Position: Late-stage empire with strong military/financial dominance but deteriorating internal cohesion and rising debt burdens. Similar to Britain 1920s-1940s.

China

China Position: Rising power facing demographic cliff, property crisis, and Western containment. Similar to Germany 1910s or Japan 1980s in some respects.

Europe (EU/Eurozone)

Europe Position: Post-imperial consolidation phase. Strong institutions but weak growth and demographic decline.

Japan

Japan Position: Lost decades ending. Weak yen making exports competitive. Beneficiary of China+1 reshoring.

Switzerland

Switzerland Position: Optimal positioning as neutral, wealthy, innovative safe haven. Benefits from global uncertainty.

Country Allocation Implications

Current Portfolio Alignment: Good - diversified away from US, overweight Switzerland/Canada/Japan.

4. Currency and Reserve Status Analysis

Reserve Currency Lifecycle

Stage 1: Emerging reserve    │ China today
Stage 2: Rising reserve      │ Euro 2000s
Stage 3: Dominant reserve    │ USD 1945-2000
Stage 4: Challenged reserve  │ USD TODAY ←
Stage 5: Declining reserve   │ GBP post-1945
Stage 6: Legacy reserve      │ GBP today

De-Dollarization Metrics

Currency Regime Scenarios

Portfolio Currency Exposure

Assessment: Well-diversified currency exposure with meaningful non-USD allocation.

5. Internal Order Analysis

Dalio's Internal Order Framework

Internal order breaks down through:

1. Wealth/income gaps widening
2. Political polarization
3. Loss of shared truth
4. Rise of populism
5. Institutional trust decline
6. Civil conflict risk

US Internal Order Assessment

European Internal Order

Swiss Internal Order

Implications

Countries with poor internal order face:

• Policy uncertainty
• Regulatory unpredictability
• Currency volatility
• Higher risk premiums

Portfolio Response: Overweight stable internal order countries (Switzerland, Canada, Japan) vs unstable (US, Europe).

6. External Order Analysis

Great Power Dynamics

Current External Order: Transitioning from US unipolar hegemony to US-China bipolar rivalry.

Trade War / Tariff Risk

The portfolio has meaningful China exposure through semiconductors:

• 4063 Shin-Etsu: 15% China wafer sales
• 8035 Tokyo Electron: Taiwan fab exposure
• ASML: China export restrictions

Risk Scenarios:

External Order Portfolio Implications

7. Portfolio Mapping to Macro Regime

Current Regime Assessment

Based on the analysis above:

Regime-Optimized Allocation

Scenario Performance Estimates

Expected Portfolio Return: Probability-weighted ~+3-6% (conservative, recession-adjusted)

8. Key Risk Factors & Watch List

Immediate Risks (0-12 months)

Medium-Term Risks (1-5 years)

Long-Term Structural Risks (5-20 years)

9. Summary: Dalio Framework Conclusions

Where We Are

What This Means for Portfolio

Core Principles for Current Regime:

1. Hold real assets: Gold 10%, Silver 7% provides crisis hedge
2. Diversify currencies: Only 35% USD vs typical 60%+ US portfolios
3. Favor stable countries: Switzerland, Canada, Japan > US overweight
4. Own pricing power: Insurance, utilities can pass through inflation
5. Avoid leverage: Fortress balance sheets only
6. Maintain dry powder: 6% cash for regime shifts
7. Limit cyclical exposure: Semi positions via limit orders only

Regime Change Triggers

If these occur, reassess positioning:

10. Integration with Quantitative Analysis

How Qualitative Informs Quantitative

The quantitative analysis (correlation matrix, risk parity weights) tells us how to allocate. The qualitative analysis tells us what regime we're in and where to adjust.

Adjustments to Risk Parity Weights Based on Macro Regime:

Final Macro-Adjusted Portfolio

GmbH (Dividends): 55%

• Canadian Utilities: FTS 9%, ENB 8% = 17%
• European Insurance: MUV2 5%, SREN 5% = 10%
• Defensive: WM 12%, GJF 7%, ELE 4% = 23%
• Staples: NESN 5% = 5%

Personal (Growth): 45%

• Precious Metals: Gold 10%, Silver 7% = 17%
• Quality Growth: GOOG 5%, BRK.B 5%, SPGI 5% = 15%
• Japan: SHECY 4%, TOELY 5%, KIK 5% = 14%
• Cyclicals (limit orders): COST 7%, POOL 3% = 10%

This qualitative analysis complements the quantitative results in quantitative_results.md and macrotrend scoring in macrotrend-resilience.md

Return to: README.md for portfolio overview

Portfolio Weights: Final All-Weather Allocation

Executive Summary

This document presents the final portfolio allocation derived from correlation analysis, scenario modeling, and macrotrend resilience testing.

Total Portfolio: CHF 1,000,000 Tax Structure: GmbH (55%) + Personal (45%) Base Currency: CHF

Complete Portfolio Allocation

GmbH Portfolio (55% = CHF 550,000) - Dividend Focus

Personal Portfolio (45% = CHF 450,000) - Growth Focus

Risk Parity Breakdown

By Asset Class

Note: Semis contribute disproportionate risk (25% risk from 12% weight). This is intentional for upside capture.

By Geography

Currency Exposure:

• CHF-denominated: 17.5%
• EUR-denominated: 15%
• USD-denominated: 22%
• CAD-denominated: 11%
• JPY-denominated: 11%
• SGD-denominated: 5%
• Physical (no currency): 17%

By Cyclicality

Correlation-Optimized Weights

Why These Weights?

Each weight is derived from three factors:

1. Conviction Level: Higher conviction = higher weight
2. Correlation Benefit: Lower correlation to portfolio = higher weight
3. Volatility Adjustment: Higher volatility = lower weight (risk parity)

Weight Derivation Examples

Gold at 10%:

• Conviction: HIGH (ultimate crisis hedge)
• Correlation: -0.30 to equities (excellent diversifier)
• Volatility: 15% (moderate)
• Result: Maximum weight for non-equity hedge

4063 at 5%:

• Conviction: HIGH (near-monopoly wafer supplier)
• Correlation: +0.85 to 8035 (need to cap combined)
• Volatility: 35% (high)
• Result: Moderate weight, volatility-constrained

FTS at 8%:

• Conviction: HIGH (51-year dividend streak)
• Correlation: -0.40 to semis (excellent offset)
• Volatility: 12% (low)
• Result: High weight, acts as semi hedge

MUV2 at 6.5%:

• Conviction: HIGH (144 years, pricing discipline)
• Correlation: +0.08 to semis (near-zero = great diversifier)
• Volatility: 18% (moderate)
• Result: Strong weight, key diversifier

Dividend Analysis

Expected Annual Income

Dividend Growth Expectations

5-Year Income Projection: CHF 23,400 → CHF 30,000+ (5% CAGR)

Limit Order Positions (Not Yet Bought)

GTC Limit Orders - Immediate to Set

Total in Limits: CHF 145,000 (14.5% of portfolio)

Position Sizing Rules

Maximum Individual Position

Rebalancing Triggers

Expected Returns by Scenario

Adding:

• Dividend yield: +2.3%
• Rebalancing bonus: +0.5%
• Entry price alpha: +2-3%

Total Expected Return: 8-10% annualized

Implementation Checklist

Immediate Buys (Deploy ~CHF 185,000)

• FTS: 500 shares @ CAD 55 = CHF 27,500
• ENB: 600 shares @ CAD 65 = CHF 35,000
• SREN: 200 shares @ CHF 133 = CHF 26,600
• MUV2: 70 shares @ EUR 467 = CHF 35,750
• D05: 400 shares @ SGD 45 = CHF 20,000
• GJF: 600 shares @ NOK 195 = CHF 16,500
• WM: 50 shares @ USD 221 = CHF 10,000
• BRK.B: 30 shares @ USD 450 = CHF 13,500

Set GTC Limit Orders

• All 10 stocks listed in limit order table above

Options (After Positions Built)

• Buy GOOG June 2025 $270 Put (protect 5% position)
• Sell POOL March 2025 $260 Call (generate income)

Monthly Deployment (CHF 25,000/month)

• Month 1: Additional FTS/ENB if still at entry
• Month 2: First limit order fills
• Month 3+: Opportunistic on -10% dips

Next: entry-strategy.md for detailed timing

Uncorrelated Pairs: Risk Reduction Through Diversification

The Math of Correlation

For two assets with correlation ρ, the portfolio volatility is:

σ_portfolio = √(w₁²σ₁² + w₂²σ₂² + 2w₁w₂ρσ₁σ₂)

Key Insight: When ρ = -1 (perfect negative correlation), volatility can be reduced to zero. When ρ = 0, volatility reduction is substantial. When ρ = +1, no diversification benefit.

Top 10 Uncorrelated Pairs in Portfolio

Pair 1: 4063/8035 vs FTS/ENB

Diversification Value:

• When semis crash -50%, utilities typically flat or +5%
• When utilities struggle (rate hikes), semis may boom (growth mode)
• Combined position reduces portfolio volatility by ~20%

Recommended Pairing:

• 4063 (5%) + 8035 (4%) = 9% semis
• FTS (8%) + ENB (8%) = 16% utilities
• Ratio: 1.8:1 utilities to semis (volatility-weighted)

Pair 2: Physical Gold vs GOOG

Diversification Value:

• Gold gains when fear rises (GOOG falls)
• GOOG gains when growth accelerates (gold flat)
• Perfect "barbell" strategy

Recommended Pairing:

• Gold: 10%
• GOOG: 5%
• Ratio: 2:1 gold to GOOG (risk-weighted)

Pair 3: MUV2/SREN vs ASML

Diversification Value:

• Insurance profits from volatility (higher premiums)
• ASML suffers from uncertainty (capex delays)
• Near-zero correlation = pure diversification

Recommended Pairing:

• MUV2 (6.5%) + SREN (5%) = 11.5%
• ASML: 3%
• Ratio: 3.8:1 insurance to ASML (volatility-weighted)

Pair 4: 2801 Kikkoman vs 8035 Tokyo Electron

Diversification Value:

• Both Japanese, but opposite cycle exposure
• Kikkoman stable through recessions
• 8035 volatile but high upside in booms

Recommended Pairing:

• 2801: 2%
• 8035: 4%
• Ratio: 1:2 (accept semi volatility with staple anchor)

Pair 5: SALIK (if added) vs POOL

Diversification Value:

• SALIK has NEGATIVE beta (rises when markets fall)
• POOL highly cyclical with housing
• Ultimate counter-cyclical pair

Note: SALIK not in current portfolio but strong diversification candidate.

Pair 6: BRK.B vs Physical Silver

Diversification Value:

• BRK benefits from market dislocations (buying opportunities)
• Silver spikes during panic but BRK stays stable
• Both are "stores of value" with different mechanisms

Recommended Pairing:

• BRK.B: 4%
• Silver: 7%
• Ratio: 1.75:1 silver to BRK (crisis hedge emphasis)

Pair 7: NESN vs 4063

Diversification Value:

• Both quality companies, opposite cycle exposure
• NESN provides stability when 4063 crashes
• Currency diversification bonus

Recommended Pairing:

• NESN: 4%
• 4063: 5%
• Ratio: 0.8:1 (slight semi overweight for growth)

Pair 8: D05 DBS vs CNR

Diversification Value:

• Low correlation despite both being economically sensitive
• Different geographic exposures
• DBS benefits from Asia growth, CNR from North American trade

Recommended Pairing:

• D05: 5%
• CNR: 3%
• Ratio: 1.7:1 (Asian bank overweight)

Pair 9: WM vs GOOG

Diversification Value:

• Both quality compounders, different volatility
• WM provides ballast during tech selloffs
• GOOG provides upside during growth phases

Recommended Pairing:

• WM: 3%
• GOOG: 5%
• Ratio: 0.6:1 (growth tilt with defensive anchor)

Pair 10: GJF vs SPGI

Diversification Value:

• Low correlation despite both being financial services
• GJF benefits from volatility (pricing)
• SPGI benefits from M&A/IPO activity

Recommended Pairing:

• GJF: 3%
• SPGI: 3%
• Equal weight (balanced exposure)

Correlation Heatmap: All Pairs

         GOOG  4063  8035  ASML  FTS   ENB   MUV2  SREN  GOLD  SLVR  BRK   NESN  POOL  WM    CNR   SPGI  D05
GOOG     1.00  0.55  0.58  0.62  -0.20 -0.15 0.08  0.10  -0.35 -0.30 0.45  -0.12 0.40  0.20  0.25  0.65  0.35
4063     0.55  1.00  0.85  0.78  -0.40 -0.35 0.05  0.08  -0.32 -0.28 0.35  -0.28 0.30  -0.15 0.10  0.45  0.40
8035     0.58  0.85  1.00  0.82  -0.45 -0.40 0.08  0.10  -0.35 -0.30 0.38  -0.30 0.32  -0.18 0.08  0.48  0.42
ASML     0.62  0.78  0.82  1.00  -0.38 -0.32 0.08  0.10  -0.30 -0.25 0.40  -0.22 0.35  -0.12 0.12  0.52  0.38
FTS      -0.20 -0.40 -0.45 -0.38 1.00  0.75  0.30  0.28  0.15  0.12  0.10  0.50  -0.30 0.60  0.40  -0.05 0.15
ENB      -0.15 -0.35 -0.40 -0.32 0.75  1.00  0.28  0.25  0.18  0.15  0.12  0.45  -0.25 0.55  0.45  -0.02 0.18
MUV2     0.08  0.05  0.08  0.08  0.30  0.28  1.00  0.80  0.25  0.22  0.20  0.30  0.15  0.35  0.25  0.18  0.45
SREN     0.10  0.08  0.10  0.10  0.28  0.25  0.80  1.00  0.22  0.20  0.18  0.28  0.12  0.32  0.22  0.15  0.42
GOLD     -0.35 -0.32 -0.35 -0.30 0.15  0.18  0.25  0.22  1.00  0.85  -0.20 0.10  -0.28 0.08  0.12  -0.22 0.05
SLVR     -0.30 -0.28 -0.30 -0.25 0.12  0.15  0.22  0.20  0.85  1.00  -0.25 0.08  -0.25 0.05  0.10  -0.20 0.02
BRK      0.45  0.35  0.38  0.40  0.10  0.12  0.20  0.18  -0.20 -0.25 1.00  0.25  0.38  0.30  0.32  0.50  0.35
NESN     -0.12 -0.28 -0.30 -0.22 0.50  0.45  0.30  0.28  0.10  0.08  0.25  1.00  -0.15 0.48  0.35  0.10  0.20
POOL     0.40  0.30  0.32  0.35  -0.30 -0.25 0.15  0.12  -0.28 -0.25 0.38  -0.15 1.00  0.18  0.22  0.42  0.30
WM       0.20  -0.15 -0.18 -0.12 0.60  0.55  0.35  0.32  0.08  0.05  0.30  0.48  0.18  1.00  0.40  0.15  0.22
CNR      0.25  0.10  0.08  0.12  0.40  0.45  0.25  0.22  0.12  0.10  0.32  0.35  0.22  0.40  1.00  0.20  0.28
SPGI     0.65  0.45  0.48  0.52  -0.05 -0.02 0.18  0.15  -0.22 -0.20 0.50  0.10  0.42  0.15  0.20  1.00  0.40
D05      0.35  0.40  0.42  0.38  0.15  0.18  0.45  0.42  0.05  0.02  0.35  0.20  0.30  0.22  0.28  0.40  1.00

Portfolio Construction Implications

Rule 1: Limit Highly Correlated Pairs

Rule 2: Ensure Uncorrelated Anchors

For each 5%+ position, ensure an uncorrelated hedge:

Rule 3: Sector Concentration Limits

Rebalancing Based on Correlation Drift

Trigger: Correlation Increase

If rolling 6-month correlation between two holdings exceeds +0.80:

1. Trim combined position to max limit
2. Add to uncorrelated asset
3. Re-evaluate diversification benefit

Trigger: Correlation Decrease

If previously correlated assets de-correlate:

1. Opportunity to add to underperformer
2. "Correlation reversion" often follows
3. Capture rebalancing bonus

Expected Diversification Benefit

Without Correlation Optimization

• Individual asset volatility: ~25% average
• Portfolio volatility (naive): ~20%
• Max drawdown: -35%

With Correlation Optimization

• Weighted average correlation: +0.15
• Portfolio volatility: ~12%
• Max drawdown: -18 to -22%

Diversification Benefit: 40% volatility reduction, 35% drawdown reduction

Next: portfolio-weights.md for final allocation

Quantitative Analysis Results

Ray Dalio All-Weather Portfolio

Analysis Date: 2025-12-31 Data Period: 2020-01-06 to 2024-12-30 Trading Days: 1191 Assets Analyzed: 17

Executive Summary

This analysis applies Bridgewater-style risk parity methodology to calculate:

1. Actual correlation matrix from 5 years of daily returns
2. Risk metrics (volatility, Sharpe ratio, max drawdown)
3. Regime performance (COVID crash, 2022 bear, etc.)
4. Optimal risk parity weights

1. Correlation Matrix