SPX: Market Reflexivity & Fractal Patterns

In this idea I would like to walk you through some principles which I use to find and relate historical complexities within rhyming cycles.

Market Reflexivity
Market reflexivity is a concept introduced by George Soros that defies the traditional TA notion of efficient markets by revealing that price movements do not merely reflect fundamentals — they actively shape them. As prices rise, optimism fuels further buying, creating a self-reinforcing loop inflating bubbles. Conversely, declining prices trigger fear, accelerating downturns. Reflexivity explains why trends persist and why reversals can be abrupt, as self-sustaining cycles eventually reach a exhaustion point.

To put it simply, there is a feedback loop between market participants’ perceptions and actual market conditions, suggesting that financial markets are not always in equilibrium because collective investor behavior actively drives price movements, which in turn influences future investor behavior.

1. Feedback Loops
   Each massive rally eventually creates conditions that lead to overvaluation, resulting in sharp corrections.
   
2. Self-Fulfilling Expectations
   Market participants, reacting to past price behavior, reinforce trends until a breaking point.
   
3. Structural Adaptation
   Every major correction resets valuations, allowing for the next cycle to begin with renewed confidence and capital inflows.
   

Practical Application of Reflexivity
Compared to many tickers, SPX has exhibited relatively stable growth throughout history. Over the past 70 years, the most significant panic-driven decline occurred after its 2007 peak, with a 57% drop that defined a major cycle. Growth resumed in 2009, making this swing a key reference point for establishing historical relationships.

I see the Dotcom and Housing crisis-induced declines as part of a broader complexity, shaped by prior long-term growth. The two cycles appear as they do because they stem from an extended structural uptrend, not just the 250% surge from 1994 to the bubble top, which lacked a significant preceding decline. Cause-and-effect logic suggests that these crashes were a reaction to a much larger uptrend that began in 1974. A 2447% rally provides a more compelling reason for mass panic and selling, as corrections of such magnitude are rare.
Intuitively, the 2447% long-term upswing should have been preceded by a decline similar to the Dotcom and Housing crashes. This holds true, as the market experienced a nearly 50% drop after peaking in 1973 and 37% in 1968, following the same cyclical pattern of deep corrections leading to extended expansions. These corrections were relatively smaller than the Dotcom and Housing crashes because they are followed by a comparatively smaller 1452% rally from the end of WWII.

Multi-Fractals
Multifractals in market analysis describe the non-linear, self-similar nature of price movements, where volatility and risk vary across different scales. Unlike simple fractals with a constant fractal dimension, multifractals exhibit multiple fractal dimensions, creating varying levels of roughness. Benoit Mandelbrot introduced multifractal Time Series to refine the classic random walk theory, recognizing that price movements occur in bursts of volatility followed by calm periods. Instead of a single Hurst exponent, markets display a spectrum of exponents, reflecting diverse scaling behaviors and explaining why price action appears random at times but reveals structured patterns over different time horizons.

This justifies viewing price action within its structural cause-and-effect framework, where micro and macro cycles are interdependent, while oscillating at different frequencies. Therefore, we will apply the building blocks independently from boundaries of Full Fractal Cycle.


Since volatility varies, this reserves us the right to extract patterns with identical slope and roughness, and by method of exclusion relate to recent cycles starting from covid.