The thesis introduces the Archetypal Market Hypothesis (AMH). Based on complex psychology and supported by insights from other (mind) sciences it describes the unconscious nature of investing and how it shapes price patterns. Specifically, it emphasises the central role of numerical archetypes in price discovery. Its ontological premise is the market’s mind, a complex adaptive system in the form of collective consciousness which originates from the collective unconscious. This premise suggests that investing involves more than cognition and reaches beyond rationality and logic. Among others, the thesis clarifies the affective impact of price discovery: it is not only what we can do with prices, but also what they can do with us. Numbers receive their affective powers from the numerical archetypes. They preconsciously create order in the mind by facilitating the dynamics of symbolic mapping as the mind attempts to make sense of what it senses, bridging the imaginative with the real. This autonomous and often dominating impact of the numerical archetypes manifests itself: • in individual consciousness via numerical intuition, and • in crowd consciousness via participation mystique which underlies intersubjectivity. The thesis will argue that both are supported cerebrally. The collective intersubjective nature of the market’s mind and its symbolic expression via prices make it an exemplary phenomenon to be researched because the archetypal dynamics are strongest in such spheres. The PhD’s goal, as part of the AMH proposition, is twofold. First, to formalise theoretically the concept of the market’s mind, in particular the collective experience of market states, generally known as market moods, and how these shift as a result of herd instinct. Second, to propose a framework for further empirical research to show that representing market data in a non-traditional way, based on Jung’s active imagination and similar techniques, can improve investors’ understanding of those states. If successful, the method (including bespoke software) can complement analytical investment research methods currently used by investors.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:685793 |
| Date | January 2015 |
| Creators | Schotanus, Patrick R. |
| Publisher | University of Essex |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://repository.essex.ac.uk/16544/ |
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