SPX: Market Reflexivity & Fractal Patterns

12 Feb

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.

10 Apr

Dagangan ditutup: sasaran tercapai

RECAP & EVALUATION

As it turned out, one of the fractal patterns (marked in red on the chart) ended up closely matching the market’s actual trajectory. This red pattern correctly signaled the downturn that followed the extended uptrend. The other patterns I had identified did not play out as clearly in real time. However, that doesn’t mean they were useless. If we adjust or “squeeze” those other patterns to account for timing differences, most of them convey the same warning as the red pattern. In other words, they too hinted at a sharp correction — their alignment was just off due to scaling or timing, not because the idea behind them was wrong.

Looking back at this chart now, I have learned a crucial lesson about using fractal pattern replication tools. After choosing a pattern to overlay, we must recognize that at certain critical turning points the market’s behavior can deviate from the pattern’s original pace. In practical terms, the frequency or pace of the pattern’s repetition can change at those points. Therefore, the chosen fractal pattern often needs to be refined and broken down into smaller phases rather than applied in one large piece.

For example, high volatility can accelerate the completion of a pattern’s phases. During the rapid growth phase on the way to the all-time high (ATH), one particular fractal (the black pattern on my chart) aligned very closely with the market’s movements. However, when the market reversed from that ATH into a correction down to the local bottom, the timing no longer matched as well. Paradoxically, the overall shape of the decline still mirrored the fractal’s shape — it just unfolded over a different timeframe than the pattern initially suggested. In hindsight, I should have split that fractal and scaled the bearish portion independently to accommodate the change in market speed.

◇ Overdue corrections can be severe: A prolonged uptrend with no substantial pullback often leads to a sharper and more severe correction when it finally happens.
◇ Historical patterns provide clues, not certainties: Fractal patterns from past market cycles can highlight potential scenarios, but they usually need adjustment (in timing or scale) to fit the current market context.
◇ Fractals are tools, not crystal balls: Use fractal analysis as a guide to market structure, not as a definitive forecast. Always remain critical and consider other evidence.
◇ Adjust for volatility and phase changes: If you apply a fractal pattern, be ready to modify it at key turning points. Market volatility can speed up or slow down moves, so consider breaking the pattern into phases and re-aligning it as needed.
◇ Picking Large patterns may distort when scaled: The larger the fractal pattern (in time or scope), the more likely it is to become distorted when overlaid on current price action. In such cases, analyzing the pattern in smaller segments can improve accuracy and relevance.