Scale Invariance Research: Consolidated Report

Complete End-to-End Analysis with Live Data

Generated: 2025-11-09 20:42:33


Abstract

This report presents a comprehensive scale invariance analysis across multiple assets.

Data Sources:

  • All financial data downloaded LIVE from Yahoo Finance API
  • NO hardcoded data points
  • ALL data cached locally for reproducibility
  • Total Assets Analyzed: 19


    Overall Comparison

    Consolidated Comparison

    Genesis Sensitivity Overview

    Scale invariance strength varies with the assumed genesis date. The chart below shows how R² responds across all tested dates for each asset where genesis optimization was performed.

    Genesis Sensitivity

    Summary Table

    Asset Category R² Exponent Pattern Points Date Range Genesis Source
    Internet Users Technology 0.9776 2.38 ⚡ Power-law 20 2005-01-01 to 2024-01-01 1983-01-01 (Provided) World Bank API (IT.NET.USER.ZS)
    Broadband Subscriptions Technology 0.9580 2.03 ⚡ Power-law 20 2005-01-01 to 2024-01-01 1990-01-01 (Provided) World Bank API (IT.NET.BBND.P2)
    McDonald's Restaurants Business 0.9301 1.10 ⚡ Power-law 130 2009-12-31 to 2025-09-30 1955-04-15 (Provided) Downloaded from SEC EDGAR (CIK: 0000063908), us
    Electricity Access Infrastructure 0.9188 1.06 ⚡ Power-law 26 1998-01-01 to 2023-01-01 1880-01-01 (Provided) World Bank API (EG.ELC.ACCS.ZS)
    Bitcoin Financial Asset 0.9079 5.74 ⚡ Power-law 5566 2010-07-18 to 2025-11-09 2009-01-03 (Provided) btcpricehistory.csv (repository data)
    Microsoft Corp. Financial Asset 0.8151 4.91 ⚡ Power-law 9993 1986-03-13 to 2025-11-07 1975-04-04 (Provided) Yahoo Finance API (yfinance Python library)
    Mobile Subscriptions Technology 0.7794 2.23 ✅ Saturated (103%) 36 1984-01-01 to 2024-01-01 1983-10-13 (Provided) World Bank API (IT.CEL.SETS.P2)
    Tesla Inc. Financial Asset 0.7543 5.05 ✅ Saturated (171%) 3866 2010-06-29 to 2025-11-07 2003-07-01 (Provided) Yahoo Finance API (yfinance Python library)
    Alphabet Inc. Financial Asset 0.7494 2.18 ⚡ Power-law 5341 2004-08-19 to 2025-11-07 1998-09-04 (Provided) Yahoo Finance API (yfinance Python library)
    DTP3 Immunization Health 0.7149 0.05 ✅ Saturated (757%) 22 2003-01-01 to 2024-01-01 2002-05-26 (Optimized vs published) WHO API (WHS4_100)
    Measles Immunization Health 0.6815 0.04 ✅ Saturated (735%) 22 2003-01-01 to 2024-01-01 2002-05-26 (Optimized vs published) WHO API (WHS4_544)
    Gold ETF (SPDR) (Early Period) Financial Asset - Split 0.5747 0.33 🔹 Early 2639 2004-11-18 to 2015-05-14 2004-11-18 (Provided) Yahoo Finance API (yfinance Python library)
    S&P 500 ETF (Early Period) Financial Asset - Split 0.5739 0.36 🔹 Early 4126 1993-01-29 to 2009-06-16 1993-01-22 (Provided) Yahoo Finance API (yfinance Python library)
    Gold ETF (SPDR) Financial Asset 0.5594 0.34 🏒 Hockey (3.6x) 5277 2004-11-18 to 2025-11-07 2004-11-18 (Provided) Yahoo Finance API (yfinance Python library)
    S&P 500 ETF (Late Period) Financial Asset - Split 0.5422 0.37 🔹 Late 4126 2009-06-17 to 2025-11-07 2009-06-17 (Hockey Stick Inflection) Yahoo Finance API (yfinance Python library)
    S&P 500 ETF Financial Asset 0.4041 0.48 🏒 Hockey (7.7x) 8252 1993-01-29 to 2025-11-07 1993-01-22 (Provided) Yahoo Finance API (yfinance Python library)
    Apple Inc. Financial Asset 0.4032 2.87 📈 S-curve (89%) 11319 1980-12-12 to 2025-11-07 1976-04-01 (Provided) Yahoo Finance API (yfinance Python library)
    Ethereum Financial Asset 0.2740 1.90 ✅ Saturated (152%) 2921 2017-11-09 to 2025-11-07 2015-07-30 (Provided) Yahoo Finance API (yfinance Python library)
    Gold ETF (SPDR) (Late Period) Financial Asset - Split 0.1743 0.12 🔹 Late 2638 2015-05-15 to 2025-11-07 2015-05-15 (Hockey Stick Inflection) Yahoo Finance API (yfinance Python library)

    Business

    McDonald's Restaurants

  • **R² (Scale Invariance):** 0.9301
  • **Power Law Exponent:** 1.10
  • **Data Points:** 130
  • **Date Range:** 2009-12-31 to 2025-09-30
  • **Log Residual Std Dev:** 0.0117
  • **Data Source:** Downloaded from SEC EDGAR (CIK: 0000063908), us
  • **Genesis Date:** 1955-04-15 (Provided)
  • **Power Law Fit:** Value ~= 0.5871 * t^1.1022
  • Saturation Analysis (S-Curve Detection):

  • **⚡ POWER-LAW GROWTH** (Not saturating)
  • **Sigmoid R²:** 0.9767 vs Linear R²: 0.9729
  • **Current Value:** 0.0% of sigmoid model maximum
  • **Assessment:** No evidence of saturation or S-curve behavior
  • McDonald's Restaurants Scatter Plot McDonald's Restaurants Log-Log Fit

    Financial Asset

    Bitcoin

  • **R² (Scale Invariance):** 0.9079
  • **Power Law Exponent:** 5.74
  • **Data Points:** 5,566
  • **Date Range:** 2010-07-18 to 2025-11-09
  • **Log Residual Std Dev:** 0.6935
  • **Data Source:** btcpricehistory.csv (repository data)
  • **Genesis Date:** 2009-01-03 (Provided)
  • **Power Law Fit:** Value ~= 2.143e-17 * t^5.7385
  • Saturation Analysis (S-Curve Detection):

  • **⚡ POWER-LAW GROWTH** (Not saturating)
  • **Sigmoid R²:** 0.8811 vs Linear R²: 0.6086
  • **Current Value:** 0.0% of sigmoid model maximum
  • **Assessment:** No evidence of saturation or S-curve behavior
  • Bitcoin Scatter Plot Bitcoin Log-Log Fit

    Microsoft Corp.

  • **R² (Scale Invariance):** 0.8151
  • **Power Law Exponent:** 4.91
  • **Data Points:** 9,993
  • **Date Range:** 1986-03-13 to 2025-11-07
  • **Log Residual Std Dev:** 0.5848
  • **Data Source:** Yahoo Finance API (yfinance Python library)
  • **Genesis Date:** 1975-04-04 (Provided)
  • **Power Law Fit:** Value ~= 2.596e-19 * t^4.9138
  • Saturation Analysis (S-Curve Detection):

  • **⚡ POWER-LAW GROWTH** (Not saturating)
  • **Sigmoid R²:** 0.9792 vs Linear R²: 0.5089
  • **Current Value:** 67.9% of sigmoid model maximum
  • **Assessment:** No evidence of saturation or S-curve behavior
  • Microsoft Corp. Scatter Plot Microsoft Corp. Log-Log Fit

    Tesla Inc.

  • **R² (Scale Invariance):** 0.7543
  • **Power Law Exponent:** 5.05
  • **Data Points:** 3,866
  • **Date Range:** 2010-06-29 to 2025-11-07
  • **Log Residual Std Dev:** 0.5591
  • **Data Source:** Yahoo Finance API (yfinance Python library)
  • **Genesis Date:** 2003-07-01 (Provided)
  • **Power Law Fit:** Value ~= 5.278e-18 * t^5.0530
  • Saturation Analysis (S-Curve Detection):

  • **📈 S-CURVE DETECTED** (Sigmoid R² = 0.8865 vs Linear R² = 0.6847)
  • **Current Progress:** 171.4% of estimated maximum
  • **Saturation Level:** 2.51e+02 (estimated carrying capacity)
  • **✅ HAS REACHED SATURATION** (≥95% of maximum)
  • **Growth Pattern:** Decelerating (-244.1% slowdown in recent period)
  • Tesla Inc. Scatter Plot Tesla Inc. Log-Log Fit Tesla Inc. S-Curve Saturation

    Alphabet Inc.

  • **R² (Scale Invariance):** 0.7494
  • **Power Law Exponent:** 2.18
  • **Data Points:** 5,341
  • **Date Range:** 2004-08-19 to 2025-11-07
  • **Log Residual Std Dev:** 0.3312
  • **Data Source:** Yahoo Finance API (yfinance Python library)
  • **Genesis Date:** 1998-09-04 (Provided)
  • **Power Law Fit:** Value ~= 2.253e-07 * t^2.1758
  • Saturation Analysis (S-Curve Detection):

  • **⚡ POWER-LAW GROWTH** (Not saturating)
  • **Sigmoid R²:** 0.9511 vs Linear R²: 0.7689
  • **Current Value:** 0.0% of sigmoid model maximum
  • **Assessment:** No evidence of saturation or S-curve behavior
  • Alphabet Inc. Scatter Plot Alphabet Inc. Log-Log Fit

    Gold ETF (SPDR)

  • **R² (Scale Invariance):** 0.5594
  • **Power Law Exponent:** 0.34
  • **Data Points:** 5,277
  • **Date Range:** 2004-11-18 to 2025-11-07
  • **Log Residual Std Dev:** 0.2191
  • **Data Source:** Yahoo Finance API (yfinance Python library)
  • **Genesis Date:** 2004-11-18 (Provided)
  • **Power Law Fit:** Value ~= 8.49 * t^0.3366
  • Saturation Analysis (S-Curve Detection):

  • **⚡ POWER-LAW GROWTH** (Not saturating)
  • **Sigmoid R²:** 0.7333 vs Linear R²: 0.6669
  • **Current Value:** 0.0% of sigmoid model maximum
  • **Assessment:** No evidence of saturation or S-curve behavior
  • Hockey Stick Pattern Detected:

  • **🏒 INFLECTION POINT:** 2015-05-15
  • **Early Period (Blade):**
  • - R² = 0.7670

    - Exponent = 0.37

    - Data points = 2638

  • **Late Period (Shaft):**
  • - R² = 0.8114

    - Exponent = 1.34

    - Data points = 2638

  • **Growth Acceleration:** 3.6x faster in late period
  • **Model Improvement:** Split model R² = 0.8504 vs Single R² = 0.7523 (+0.0981)
  • **Analysis:** Asset shows distinct regime change - early slow growth followed by acceleration
  • Gold ETF (SPDR) Scatter Plot Gold ETF (SPDR) Log-Log Fit Gold ETF (SPDR) Early Period (Hockey Stick Blade) Gold ETF (SPDR) Late Period (Hockey Stick Shaft)

    S&P 500 ETF

  • **R² (Scale Invariance):** 0.4041
  • **Power Law Exponent:** 0.48
  • **Data Points:** 8,252
  • **Date Range:** 1993-01-29 to 2025-11-07
  • **Log Residual Std Dev:** 0.5001
  • **Data Source:** Yahoo Finance API (yfinance Python library)
  • **Genesis Date:** 1993-01-22 (Provided)
  • **Power Law Fit:** Value ~= 2.132 * t^0.4757
  • Saturation Analysis (S-Curve Detection):

  • **⚡ POWER-LAW GROWTH** (Not saturating)
  • **Sigmoid R²:** 0.9778 vs Linear R²: 0.7296
  • **Current Value:** 0.0% of sigmoid model maximum
  • **Assessment:** No evidence of saturation or S-curve behavior
  • Hockey Stick Pattern Detected:

  • **🏒 INFLECTION POINT:** 2009-06-17
  • **Early Period (Blade):**
  • - R² = 0.7576

    - Exponent = 0.39

    - Data points = 4126

  • **Late Period (Shaft):**
  • - R² = 0.9794

    - Exponent = 3.03

    - Data points = 4126

  • **Growth Acceleration:** 7.7x faster in late period
  • **Model Improvement:** Split model R² = 0.9600 vs Single R² = 0.7052 (+0.2547)
  • **Analysis:** Asset shows distinct regime change - early slow growth followed by acceleration
  • S&P 500 ETF Scatter Plot S&P 500 ETF Log-Log Fit S&P 500 ETF Early Period (Hockey Stick Blade) S&P 500 ETF Late Period (Hockey Stick Shaft)

    Apple Inc.

  • **R² (Scale Invariance):** 0.4032
  • **Power Law Exponent:** 2.87
  • **Data Points:** 11,319
  • **Date Range:** 1980-12-12 to 2025-11-07
  • **Log Residual Std Dev:** 1.4324
  • **Data Source:** Yahoo Finance API (yfinance Python library)
  • **Genesis Date:** 1976-04-01 (Provided)
  • **Power Law Fit:** Value ~= 9.735e-12 * t^2.8716
  • Saturation Analysis (S-Curve Detection):

  • **📈 S-CURVE DETECTED** (Sigmoid R² = 0.9805 vs Linear R² = 0.4632)
  • **Current Progress:** 88.7% of estimated maximum
  • **Saturation Level:** 3.03e+02 (estimated carrying capacity)
  • **Estimated Time to 95% Saturation:** ~4.1 years
  • **Growth Pattern:** Decelerating (-5078.8% slowdown in recent period)
  • Apple Inc. Scatter Plot Apple Inc. Log-Log Fit Apple Inc. S-Curve Saturation

    Ethereum

  • **R² (Scale Invariance):** 0.2740
  • **Power Law Exponent:** 1.90
  • **Data Points:** 2,921
  • **Date Range:** 2017-11-09 to 2025-11-07
  • **Log Residual Std Dev:** 0.7583
  • **Data Source:** Yahoo Finance API (yfinance Python library)
  • **Genesis Date:** 2015-07-30 (Provided)
  • **Power Law Fit:** Value ~= 0.0004909 * t^1.8979
  • Saturation Analysis (S-Curve Detection):

  • **📈 S-CURVE DETECTED** (Sigmoid R² = 0.7000 vs Linear R² = 0.5743)
  • **Current Progress:** 152.3% of estimated maximum
  • **Saturation Level:** 2.26e+03 (estimated carrying capacity)
  • **✅ HAS REACHED SATURATION** (≥95% of maximum)
  • **Growth Pattern:** Decelerating (22.3% slowdown in recent period)
  • Ethereum Scatter Plot Ethereum Log-Log Fit Ethereum S-Curve Saturation

    Financial Asset - Split

    Gold ETF (SPDR) (Early Period)

  • **R² (Scale Invariance):** 0.5747
  • **Power Law Exponent:** 0.33
  • **Data Points:** 2,639
  • **Date Range:** 2004-11-18 to 2015-05-14
  • **Log Residual Std Dev:** 0.2031
  • **Data Source:** Yahoo Finance API (yfinance Python library)
  • **Genesis Date:** 2004-11-18 (Provided)
  • **Power Law Fit:** Value ~= 9.028 * t^0.3268
  • Gold ETF (SPDR) (Early Period) Scatter Plot Gold ETF (SPDR) (Early Period) Log-Log Fit

    S&P 500 ETF (Early Period)

  • **R² (Scale Invariance):** 0.5739
  • **Power Law Exponent:** 0.36
  • **Data Points:** 4,126
  • **Date Range:** 1993-01-29 to 2009-06-16
  • **Log Residual Std Dev:** 0.2184
  • **Data Source:** Yahoo Finance API (yfinance Python library)
  • **Genesis Date:** 1993-01-22 (Provided)
  • **Power Law Fit:** Value ~= 3.759 * t^0.3627
  • S&P 500 ETF (Early Period) Scatter Plot S&P 500 ETF (Early Period) Log-Log Fit

    S&P 500 ETF (Late Period)

  • **R² (Scale Invariance):** 0.5422
  • **Power Law Exponent:** 0.37
  • **Data Points:** 4,126
  • **Date Range:** 2009-06-17 to 2025-11-07
  • **Log Residual Std Dev:** 0.3355
  • **Data Source:** Yahoo Finance API (yfinance Python library)
  • **Genesis Date:** 2009-06-17 (Hockey Stick Inflection)
  • **Power Law Fit:** Value ~= 12.2 * t^0.3742
  • S&P 500 ETF (Late Period) Scatter Plot S&P 500 ETF (Late Period) Log-Log Fit

    Gold ETF (SPDR) (Late Period)

  • **R² (Scale Invariance):** 0.1743
  • **Power Law Exponent:** 0.12
  • **Data Points:** 2,638
  • **Date Range:** 2015-05-15 to 2025-11-07
  • **Log Residual Std Dev:** 0.2204
  • **Data Source:** Yahoo Finance API (yfinance Python library)
  • **Genesis Date:** 2015-05-15 (Hockey Stick Inflection)
  • **Power Law Fit:** Value ~= 65.03 * t^0.1215
  • Gold ETF (SPDR) (Late Period) Scatter Plot Gold ETF (SPDR) (Late Period) Log-Log Fit

    Health

    DTP3 Immunization

  • **R² (Scale Invariance):** 0.7149
  • **Power Law Exponent:** 0.05
  • **Data Points:** 22
  • **Date Range:** 2003-01-01 to 2024-01-01
  • **Log Residual Std Dev:** 0.0177
  • **Data Source:** WHO API (WHS4_100)
  • **Genesis Date:** 2002-05-26 (Optimized vs published)
  • **Power Law Fit:** Value ~= 56.33 * t^0.0473
  • Saturation Analysis (S-Curve Detection):

  • **📈 S-CURVE DETECTED** (Sigmoid R² = 0.9094 vs Linear R² = 0.6163)
  • **Current Progress:** 757.5% of estimated maximum
  • **Saturation Level:** 1.12e+01 (estimated carrying capacity)
  • **✅ HAS REACHED SATURATION** (≥95% of maximum)
  • **Growth Pattern:** Decelerating (17.9% slowdown in recent period)
  • Genesis Date Optimization:

  • **Optimal Genesis:** 2002-05-26 (R² = 0.7195)
  • **Note:** No published genesis date available for comparison
  • **Tested:** 11 candidate dates across range 2002-05-26 to 2004-11-11
  • DTP3 Immunization Scatter Plot DTP3 Immunization Log-Log Fit DTP3 Immunization S-Curve Saturation

    Measles Immunization

  • **R² (Scale Invariance):** 0.6815
  • **Power Law Exponent:** 0.04
  • **Data Points:** 22
  • **Date Range:** 2003-01-01 to 2024-01-01
  • **Log Residual Std Dev:** 0.0186
  • **Data Source:** WHO API (WHS4_544)
  • **Genesis Date:** 2002-05-26 (Optimized vs published)
  • **Power Law Fit:** Value ~= 58.51 * t^0.0426
  • Saturation Analysis (S-Curve Detection):

  • **📈 S-CURVE DETECTED** (Sigmoid R² = 0.8641 vs Linear R² = 0.5063)
  • **Current Progress:** 734.6% of estimated maximum
  • **Saturation Level:** 1.14e+01 (estimated carrying capacity)
  • **✅ HAS REACHED SATURATION** (≥95% of maximum)
  • **Growth Pattern:** Decelerating (-7.8% slowdown in recent period)
  • Genesis Date Optimization:

  • **Optimal Genesis:** 2002-05-26 (R² = 0.6767)
  • **Note:** No published genesis date available for comparison
  • **Tested:** 11 candidate dates across range 2002-05-26 to 2004-11-11
  • Measles Immunization Scatter Plot Measles Immunization Log-Log Fit Measles Immunization S-Curve Saturation

    Infrastructure

    Electricity Access

  • **R² (Scale Invariance):** 0.9188
  • **Power Law Exponent:** 1.06
  • **Data Points:** 26
  • **Date Range:** 1998-01-01 to 2023-01-01
  • **Log Residual Std Dev:** 0.0102
  • **Data Source:** World Bank API (EG.ELC.ACCS.ZS)
  • **Genesis Date:** 1880-01-01 (Provided)
  • **Power Law Fit:** Value ~= 0.0008957 * t^1.0629
  • Saturation Analysis (S-Curve Detection):

  • **⚡ POWER-LAW GROWTH** (Not saturating)
  • **Sigmoid R²:** 0.9805 vs Linear R²: 0.9766
  • **Current Value:** 0.5% of sigmoid model maximum
  • **Assessment:** No evidence of saturation or S-curve behavior
  • Electricity Access Scatter Plot Electricity Access Log-Log Fit

    Technology

    Internet Users

  • **R² (Scale Invariance):** 0.9776
  • **Power Law Exponent:** 2.38
  • **Data Points:** 20
  • **Date Range:** 2005-01-01 to 2024-01-01
  • **Log Residual Std Dev:** 0.0368
  • **Data Source:** World Bank API (IT.NET.USER.ZS)
  • **Genesis Date:** 1983-01-01 (Provided)
  • **Power Law Fit:** Value ~= 8.467e-09 * t^2.3786
  • Saturation Analysis (S-Curve Detection):

  • **⚡ POWER-LAW GROWTH** (Not saturating)
  • **Sigmoid R²:** 0.9951 vs Linear R²: 0.9913
  • **Current Value:** 40.4% of sigmoid model maximum
  • **Assessment:** No evidence of saturation or S-curve behavior
  • Internet Users Scatter Plot Internet Users Log-Log Fit

    Broadband Subscriptions

  • **R² (Scale Invariance):** 0.9580
  • **Power Law Exponent:** 2.03
  • **Data Points:** 20
  • **Date Range:** 2005-01-01 to 2024-01-01
  • **Log Residual Std Dev:** 0.0575
  • **Data Source:** World Bank API (IT.NET.BBND.P2)
  • **Genesis Date:** 1990-01-01 (Provided)
  • **Power Law Fit:** Value ~= 1.065e-07 * t^2.0251
  • Broadband Subscriptions Scatter Plot Broadband Subscriptions Log-Log Fit

    Mobile Subscriptions

  • **R² (Scale Invariance):** 0.7794
  • **Power Law Exponent:** 2.23
  • **Data Points:** 36
  • **Date Range:** 1984-01-01 to 2024-01-01
  • **Log Residual Std Dev:** 1.0850
  • **Data Source:** World Bank API (IT.CEL.SETS.P2)
  • **Genesis Date:** 1983-10-13 (Provided)
  • **Power Law Fit:** Value ~= 3.676e-08 * t^2.2330
  • Saturation Analysis (S-Curve Detection):

  • **📈 S-CURVE DETECTED** (Sigmoid R² = 0.9992 vs Linear R² = 0.9330)
  • **Current Progress:** 103.0% of estimated maximum
  • **Saturation Level:** 1.09e+02 (estimated carrying capacity)
  • **✅ HAS REACHED SATURATION** (≥95% of maximum)
  • **Growth Pattern:** Decelerating (61.4% slowdown in recent period)
  • Mobile Subscriptions Scatter Plot Mobile Subscriptions Log-Log Fit Mobile Subscriptions S-Curve Saturation

    Data Sources

    All data downloaded from live APIs:

  • **Yahoo Finance API**: Financial assets (stocks, crypto, ETFs)
  • Data cached in `data_cache/` directory
  • Each cache file includes metadata with source URL and download date

  • Methodology

    Scale invariance is measured by:

    1. Selecting random pairs of dates (t₁, t₂)

    2. Computing time_ratio = (t₂ - t₀) / (t₁ - t₀)

    3. Computing value_ratio = V(t₂) / V(t₁)

    4. Fitting power law in log-log space

    5. Measuring R² (goodness of fit)

    High R² (>0.95) indicates true scale invariance and power law behavior.