The Theory Of Reflexivity Explained Simply

The Theory Of Reflexivity – All You Need To Know

In this post, we’ll unpack all you need to know about the Theory Of Reflexivity, defining exactly what it is, how it works, a real-world example of the phenomena in action and more.

What Is The Theory Of Reflexivity?

Developed by Hungarian-American investor George Soros, the Theory Of Reflexivity is a social science concept that suggests a bidirectional relationship between perceived reality and actual reality.

Under this proposed theory, our thoughts and actions influence economic activity which in turn influence our thoughts and actions, creating a self-fulfilling cycle between our expectations and the outcomes we experience.

Reflexivity refers to how momentum feeds on itself, until it doesn’t.

How Does The Theory Of Reflexivity Work?

In the context of investing, according to the Theory Of Reflexivity, investors don’t base their decisions on objective reality, but instead on subjective reality. Thus, the Theory Of Reflexivity works through a two-step process:

  1. Cognitive Function: Individuals generate perceptions of reality based on numerous factors including beliefs, cognitive biases and social conditioning.
  2. Participatory Function: Individuals act on their existing perceptions which changes actual reality and affects future perceptions.

These two functions create a continuous feedback loop, where changes in reality shape our perceptions which lead to further changes in reality. This feedback loop can lead to self-reinforcing cycles such as booms and busts, as investors’ perceptions and actions influence market prices and fundamentals.

Perceptions Influence Actions → Actions Influence Outcomes Outcomes Influence Perceptions → Cycle Repeats

The Theory Of Reflexivity, Underlying Trend & Market Bias

Markets are always biased towards one direction or another. In the stock market, market participants’ bias finds expression in either purchases or sales. Therefore, a positive bias (reflected in increased purchases) leads to rising stock prices and a negative bias (reflected in increased sales) leads to falling stock prices.

The trend in market prices can thus be envisioned as a composite of the underlying trend (either up or down) and the prevailing bias (either purchases and sales). The underlying trend influences participants’ perceptions and actions and therefore the prevailing bias. The participants’ perceptions and actions influences market prices and therefore the underlying trend.

Eventually, the trend cannot sustain prevailing expectations and a reversal sets in and the cycle repeats. The longer the period, the greater the likelihood of a reversal.

Booms & Busts

Financial markets constantly anticipate events — both positive and negative — which fail to materialise precisely because they have been anticipated. This is the Theory of Reflexivity in action. Thus, financial booms and busts are most likely occur when they are least expected.

Both booms and busts become self-filling prophecy’s because people behave in alignment with their subjective perception of reality instead of objective reality.

An Example Of The Theory Of Reflexivity

A classic example of reflexivity in action is the housing market bubble that led to the 2008 financial crisis.

During the bubble, rising housing prices fuelled optimism and the belief that prices would continue to increase. This lead more people to invest in real estate. This increased demand drove prices even higher, reinforcing the initial belief and attracting more investors.

Eventually, the bubble burst when housing prices became unsustainable and the market crashed. This caused a sharp decline in prices and a negative feedback loop as perceptions shifted to pessimism.


The Theory Of Reflexivity suggests that there is a feedback loop between subjective reality and objective reality. This bidirectional relationship means that our thoughts and actions influence the outcomes we experience and the outcomes we experience influence our thoughts and actions, leading to self-reinforcing cycles.

By understanding this theory, we can better recognize the potential for self-reinforcing cycles, make more effective predictions about the future and adapt our decision-making accordingly.


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