Section 9 - Chapter 2 - Common Cycles - CMT Level 1 Short Notes 2025

Section 9 - Chapter 2 - Common Cycles - Presented by Rohan Sharma - The CMT Coach - Chartered Market Technician CMT Level 1 Study Material - CMT Level 1 Chapter Wise Short Notes - CMT Level 1 Course Content - CMT Level 1 2025 Exam Syllabus Visit Site : www.learn.ptaindia.com and www.ptaindia.com

1. Chapter 2 – Common CyclesCycle Section 9 – Cycle Analysis Presented By : This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

2. Agenda Common Cycles Natural Cycles Notable Cycles This Content is Copyright Reserved Rights Copyright 2025@PTAIndia

3. Common Cycle Key Facts, Cheat Sheet & Interpretation on Natural Cycles and Fixed Cycles 1 . Natural Cycles & Fixed Cycles Share Common Principles o Both follow predictable patterns based on time, energy, and transformation. o Natural cycles (e.g., seasons, tides, lunar phases) align with fixed cycles (e.g., economic trends, planetary movements). 2. Rhythmic & Repetitive Patterns o Nature operates on a structured rhythm, just as financial markets, historical events, and biological processes repeat in cycles. 3. Phases & Transitions o All cycles go through phases: birth, growth, peak, decline, and renewal. o Example: The business cycle (expansion, peak, recession, recovery) mirrors seasonal cycles (spring, summer, autumn, winter ).

4. Common Cycle Key Facts, Cheat Sheet & Interpretation on Natural Cycles and Fixed Cycles 4 . Commonality Across Fields o Nature: Day & night, seasons, life cycles. o Economy: Bull & bear markets, boom & bust cycles. o Human Behavior: Sleep-wake cycles, emotional fluctuations. o Physics: Wave frequencies, planetary orbits. 5. Mathematical & Scientific Basis o Governed by Fibonacci sequences, Golden Ratio, fractals, and harmonic frequencies. o Example: The spiral shape in galaxies, hurricanes, and shells follows the Fibonacci sequence .

5. Common Cycle Cheat Sheet Cycle Type Key Phases Example Seasons Spring → Summer → Fall → Winter Earth's axial tilt & orbit around the Sun Lunar Cycle New Moon → Waxing → Full → Waning Moon's orbit around Earth Economic Expansion → Peak → Recession → Recovery Business cycles Biological Birth → Growth → Maturity → Decline Human aging, plant growth Energy Flow Accumulation → Release → Rest Tides, solar cycles, weather patterns

6. Common Cycle Interpretation • Natural cycles mirror fixed cycles, providing insights into timing and predictability across disciplines. • Observing natural patterns helps in forecasting trends, whether in climate, markets, or personal development. • Recognizing these cycles allows for better decision-making, strategic planning, and harmonization with natural rhythms.

7. Kondratieff Wave 🔑 Key Facts 1. Definition o The Kondratieff Wave (K-Wave) is a long-term economic cycle lasting approximately 40-60 years, identified by Nikolai Kondratieff in the 1920s. o It represents waves of economic expansion and contraction driven by technological innovations, capital investment, and social changes. 2. Cycle Phases (Similar to Seasons) o Spring (Inflationary Growth) – New technologies emerge, economy expands. o Summer (Stagflation & Saturation) – Market growth slows, inflation rises. o Autumn (Deflationary Boom) – Financial markets peak, asset bubbles form. o Winter (Depression & Reset) – Economic collapse, recessions, debt crises .

8. Kondratieff Wave 🔑 Key Facts 3 . Key Drivers o Technological Innovation: Railroads, electricity, automobiles, IT, AI. o Capital Investment Cycles: Infrastructure, industries, financial markets. o Social & Political Factors: Wars, regulations, labor shifts, globalization. 4. Historical Examples of K-Waves o 1st Wave (1770s–1830s) – Industrial Revolution (steam engine, textiles). o 2nd Wave (1830s–1880s) – Railroads, steel, mechanized industry. o 3rd Wave (1880s–1930s) – Electricity, chemicals, automobiles. o 4th Wave (1930s–1970s) – Oil, mass production, consumer goods. o 5th Wave (1970s–2020s) – Digital age, IT, internet, software. o 6th Wave (2020s–2050s?) – AI, biotech, green energy, automation?

9. Kondratieff Wave 📌 Cheat Sheet Phase Economic Condition Key Trends Example Spring Growth & Innovation New industries, rising wages, optimism 1950s Post-WWII Boom Summer Peak & Inflation Overheating, wage-price spirals, wars 1970s Stagflation Autumn Boom & Financial Bubble Stock market surges, speculation, debt 1990s Tech Bubble Winter Collapse & Recession Crashes, deflation, resets, restructuring 2008 Financial Crisis

10. Kondratieff Wave Interpretation & Insights • Predicting Market Cycles: Understanding K-Waves helps in identifying long-term economic trends, investment opportunities, and potential downturns. • Impact of Technology: Each wave is triggered by groundbreaking innovations—investing early in emerging technologies can be highly profitable. • Crisis as Opportunity: The Winter phase is painful but also lays the groundwork for the next economic expansion. • Current Stage (2020s): Many analysts suggest we are in a late Autumn/Winter phase, meaning potential economic restructuring, AI revolution, and green energy transition will drive the next K-Wave.

11. Economic Cycles - (Kuznets Cycle, Juglar Cycle, Presidential Cycle, Kitchin Cycle) 🔑 Key Facts 1. Kuznets Cycle (15–25 Years) – Infrastructure & Demographics o Identified by Simon Kuznets (1930s). o Reflects investment in infrastructure, migration, and urbanization. o Driven by population shifts, housing markets, income distribution. o Example: Post-WWII suburban expansion in the 1950s-70s. 2. Juglar Cycle (7–11 Years) – Fixed Investment Cycle o Identified by Clément Juglar (1860s). o Focuses on business investment in equipment & capital goods. o Peaks when firms over-invest, followed by recessions from excess capacity. o Example: Dot-com boom & bust (1990s-2000s ).

12. Economic Cycles - (Kuznets Cycle, Juglar Cycle, Presidential Cycle, Kitchin Cycle) 🔑 Key Facts 3. Presidential Cycle (4 Years) – U.S. Politics & Economy o Based on U.S. presidential election cycles. o First two years: Slower growth, possible market corrections. o Last two years: Pro-business policies, economic stimulus. o Example: Stock market tends to rise before elections due to fiscal spending. 4. Kitchen Cycle (3–4 Years) – Inventory & Short-Term Business Cycle o Identified by Joseph Kitchin (1920s). o Driven by inventory management – firms overproduce, adjust, then repeat. o Shortest of all cycles, affecting retail, supply chains, and trade. o Example: Retail demand fluctuations during economic expansions & recessions.

13. Economic Cycles - (Kuznets Cycle, Juglar Cycle, Presidential Cycle, Kitchin Cycle) 📌 Cheat Sheet & Comparison . Cycle Duration Main Driver Key Sectors Affected Example Kuznets 15–25 years Infrastructure & Demographics Housing, construction, urbanization Post-WWII boom Juglar 7–11 years Business Investment Capital goods, manufacturing Dot-com bubble (1990s-2000s) Presidential 4 years U.S. Political Cycles Stock markets, fiscal policies Pre-election market rallies Kitchin 3–4 years Inventory & Supply Chains Retail, trade, short-term demand Post-recession inventory rebuilds

14. Economic Cycles - (Kuznets Cycle, Juglar Cycle, Presidential Cycle, Kitchin Cycle) Interpretation & Insights 1. Cycle Interaction • Short-term cycles ( Kitchin , Presidential) influence business decisions and market sentiment. • Medium-term cycles (Juglar, Kuznets) determine economic structure, investment trends, and major financial shifts. 2. Investment & Economic Planning • Understanding these cycles helps predict market corrections, booms, and downturns. • Investors use the Presidential Cycle to time market trends. • Real estate & infrastructure sectors benefit from Kuznets Cycle awareness. 3. Where Are We Now (2020s)? • Likely in late Kuznets and Juglar cycle peaks, with potential for a slowdown or restructuring. • Kitchin cycle downturns (supply chain issues) still affecting short-term markets. • Upcoming U.S. elections (Presidential Cycle) could drive temporary market growth.

15. Sequences (Nonlinear Cycles) 🔑 Key Facts 1. Definition of Nonlinear Cycles o Unlike linear cycles, which follow predictable, fixed patterns, nonlinear cycles are irregular, dynamic, and influenced by feedback loops. o Found in economics, nature, physics, and social systems. o Exhibit self-organization, chaos, and fractal behavior. 2. Key Characteristics o Adaptive & Evolving: The cycle changes over time rather than repeating identically. o Feedback-Driven: Small changes can have large, unpredictable effects (butterfly effect). o Phase Shifts & Bifurcations: Instead of smooth transitions, sudden jumps occur. o Fractal & Nested Structure: Smaller cycles exist within larger ones (e.g., stock market corrections within economic expansions ).

16. Sequences (Nonlinear Cycles) 3 . Examples of Nonlinear Cycles o Business & Economic Cycles (boom-bust cycles with irregular lengths). o Climate Cycles (El Niño, ice ages, chaotic weather patterns). o Biological Rhythms (heartbeat variations, population dynamics). o Market Trends (financial crashes, speculative bubbles). o Technology Adoption Cycles (S-curve but with unpredictable acceleration ).

17. Sequences (Nonlinear Cycles) 📌 Cheat Sheet & Comparison Cycle Type Nature Key Drivers Examples Linear Cycles Regular & Predictable Time-based, fixed phases Kondratieff Wave, Kitchin Cycle Nonlinear Cycles Irregular, Adaptive Feedback loops, external shocks Market Crashes, Climate Patterns Fractal Cycles Nested patterns Self-similarity at different scales Stock market corrections within economic cycles Chaotic Cycles Unstable, Sensitive Small inputs → Large impacts Financial crashes, weather shifts Bifurcation Cycles Phase Transitions Structural shifts & tipping points AI Revolution, Paradigm Shifts

18. Sequences (Nonlinear Cycles) Interpretation & Insights 1. Why Nonlinear Cycles Matter? • More Realistic: The world doesn’t operate in perfectly timed cycles. • Better Forecasting: Helps in recognizing complex system behavior rather than expecting a fixed pattern. • Early Warning Signals: Nonlinear models detect bubbles, crashes, and system collapses better than traditional cycles. 2. Investment & Decision-Making Implications • Linear cycles help in long-term economic planning (e.g., Kondratieff Waves). • Nonlinear cycles help in identifying risk and rapid market shifts (e.g., 2008 Financial Crisis). • Adaptive Strategies: Businesses and investors should adjust dynamically instead of following rigid models .

19. Sequences (Nonlinear Cycles) Interpretation & Insights 3 . Current Trends (2020s & Beyond) • Financial Markets: Increasingly chaotic & nonlinear due to algorithmic trading and AI. • Climate Change: Accelerating nonlinearly, leading to unpredictable consequences. • Technology Cycles: AI, quantum computing, and biotech might introduce bifurcations into economic structures.

20. Fibonacci & Lucas Sequences 🔑 Key Facts 1. Fibonacci Sequence • Defined by: • Sequence: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, … • Appears in nature, spirals, financial markets, and biological systems. • Golden Ratio Approximation: ϕ= F(n+1)F(n)≈ 1.618 • Used in technical analysis (Fibonacci retracements), fractals, and growth patterns.

21. Fibonacci & Lucas Sequences 2. Lucas Sequence • Defined by the same formula: • Lucas Sequence: 2, 1, 3, 4, 7, 11, 18, 29, … • Shares many properties with Fibonacci, but values grow differently. • Still converges to the Golden Ratio (ϕ\ phiϕ ), but starts from different initial conditions. • Used in number theory, cryptography, and primality testing .

22. Fibonacci & Lucas Sequences Cheat Sheet & Comparison Property Fibonacci Sequence Lucas Sequence Formula Starting Values 0, 1 2, 1 Sequence 0, 1, 1, 2, 3, 5, 8, … 2, 1, 3, 4, 7, 11, 18, … Golden Ratio Approximates 1.618 Approximates 1.618 Applications Nature, markets, architecture, algorithms Cryptography, primality testing, number theory Growth Rate Slower than Lucas Faster than Fibonacci

23. Fibonacci & Lucas Sequences Interpretation & Insights 1. Key Differences • Lucas numbers grow faster than Fibonacci due to a different starting point. • Both sequences approach the Golden Ratio but at different rates. • Fibonacci is more common in natural systems, while Lucas is more mathematical in application. 2. Practical Applications • Fibonacci is used in market analysis (retracement levels), nature (spirals), and optimization problems. • Lucas is useful in encryption, random number generation, and computer science. 3. Where Are These Used Today? • AI & Cryptography: Lucas numbers help in encryption algorithms and security keys. • Financial Markets: Fibonacci retracements & extensions predict price movements. • Biology & Architecture: Fibonacci ratios shape plant growth, seashells, and even buildings.

24. Natural Squares & Spiral Calendar Key Facts 1. Natural Squares • Definition: A sequence of numbers formed by squaring natural numbers: n2=1,4,9,16,25,36,49,64,81,100 ,… • Represents growth, structure, and symmetry in nature and mathematics. • Found in geometry (square areas), physics (inverse-square laws), and architecture. • Plays a role in fractal structures, optimization, and grid-based designs .

25. Natural Squares & Spiral Calendar Key Facts 2 . Spiral Calendar • Definition: A concept where time is perceived as a spiral rather than a linear progression, incorporating cycles that repeat but evolve over time. • Combines elements of Fibonacci spirals, astrological cycles, and historical patterns. • Suggests that events recur in similar but non-identical ways, reflecting fractality in time. • Used in historical analysis, economic predictions, and pattern recognition.

26. Natural Squares & Spiral Calendar Cheat Sheet & Comparison Feature Natural Squares Spiral Calendar Formula n2n^2n2 (e.g., 1, 4, 9, 16, 25…) Cyclic but expanding pattern Growth Type Exponential (quadratic) Nonlinear, fractal-like Mathematical Use Geometry, physics, optimization Time cycles, historical analysis Symbolism Stability, structure, balance Evolution, cycles, transformation Applications Grid-based design, structural integrity, physics Predicting trends, astrology, economic cycles Key Insight Fixed growth pattern Repeating cycles with variations

27. Natural Squares & Spiral Calendar Interpretation & Insights 1. Key Differences • Natural squares grow predictably, while spiral calendars evolve dynamically. • Natural squares are used in physical structures, while spiral time models help in forecasting. 2. Practical Applications • Natural Squares: Used in construction, computing (algorithms), and physics (inverse-square law). • Spiral Calendar: Applied in market cycles, historical trends, and astrological forecasting. 3. Where Are These Used Today? • AI & Big Data: Square-based algorithms improve efficiency, while spirals help in trend detection. • Stock Markets & Economy: Spiral cycles analyze repeated financial patterns. History & Time Studies: Spiral time concepts explain repeated global events (wars, revolutions, crises).

28. Benner Cycle 🔑 Key Facts Benner Cycle Overview • The Benner Cycle is an economic cycle that predicts repeating patterns of prosperity, recession, and crises. • Identified by Samuel Benner in 1875, it was based on historical economic fluctuations, agricultural trends, and financial panics. • The cycle follows a regular pattern of 8, 9, and 10-year intervals, with major depressions occurring roughly every 30-35 years. • Often used in commodity price predictions, stock markets, and economic downturn forecasting .

29. Benner Cycle 🔑 Key Facts Benner’s Predicted Cycles • Good Times (Prosperity & Market Peaks): Occur at regular intervals, often after recessions. • Low Prices (Economic Slowdowns): Predictable declines in commodity and stock prices. • Panic & Crises (Major Market Collapses): Occur roughly every 30–35 years, affecting economies worldwide.

30. Benner Cycle 📌 Cheat Sheet Phase Duration Key Features Example Events Prosperity (Good Years) ~8–9 years Economic boom, rising markets 1920s Roaring Twenties, 1990s Dot-Com Boom Recession (Low Prices) ~9–10 years Market slowdowns, weak demand 1930s Great Depression, 2008 Financial Crisis Panic & Crisis ~30–35 years Market crashes, economic depression 1873, 1929, 2008 Crashes

31. Benner Cycle Interpretation & Insights 1. Cycle Interaction & Market Prediction • Similar to the Juglar (7-11 years) and Kuznets (15-25 years) cycles, but Benner’s model focuses more on agriculture & financial markets. • Useful for commodity investors & long-term market predictions. 2. Where Are We Now? (2020s Perspective) • If the Benner cycle holds, we may see economic stagnation post-2020s, leading to a major financial event by the 2030s. • The cycle suggests a market peak in the late 2020s before a possible downturn. 3. Practical Use Cases • Investors use the Benner Cycle to time market entries & exits. • Economists analyze historical recessions & commodity cycles. • Business leaders use it for long-term strategic planning.

32. Chapter 1 – Equities Next Section 10 – Comparative Analysis Presented By : This Content is Copyright Reserved Rights Copyright 2025@PTAIndia