The Mystery of Market Movements: An Archetypal Approach to Investment Forecasting and Modelling (Bloomberg)

Niklas Hageback

Language: English

Pages: 192

ISBN: 111884498X

Format: PDF / Kindle (mobi) / ePub

A quantifiable framework for unlocking the unconscious forces that shape markets

There has long been a notion that subliminal forces play a great part in causing the seemingly irrational financial bubbles, which conventional economic theory, again and again, fails to explain. However, these forces, sometimes labeled ‘animal spirits’ or ‘irrational exuberance, have remained elusive - until now. The Mystery of Market Movements provides you with a methodology to timely predict and profit from changes in human investment behaviour based on the workings of the collective unconscious.

Niklas Hageback draws in on one of psychology's most influential ideas - archetypes - to explain how they form investor’s perceptions and can be predicted and turned into profit. The Mystery of Market Movements provides;

  • A review of the collective unconscious and its archetypes based on Carl Jung’s theories and empirical case studies that highlights and assesses the influences of the collective unconscious on financial bubbles and zeitgeists
  • For the first time being able to objectively measure the impact of archetypal forces on human thoughts and behaviour with a view to provide early warning signals on major turns in the markets. This is done through a step-by-step guide on how to develop a measurement methodology based on an analysis of the language of the unconscious; figurative speech such as metaphors and symbolism, drawn out and deciphered from Big Data sources, allowing for  quantification into time series
  • The book is supplemented with an online resource that presents continuously updated bespoken archetypal indexes with predictive capabilities to major financial indexes

Investors are often unaware of the real reasons behind their own financial decisions. This book explains why psychological drivers in the collective unconscious dictates not only investment behaviour but also political, cultural and social trends. Understanding these forces allows you to stay ahead of the curve and profit from market tendencies that more traditional methods completely overlook.

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on Archetypal Forces: Part III: The Model” provides the model and a step-by-step guide that gives insights on the statistical methods applied to develop and test archetypal symbol time series. Chapter 9: “Examples of Archetypal Influences on the Formation of Financial Bubbles” demonstrates the connection between specific archetypes and the dot-com bubble and US property bubble. Chapter 10: “Conclusion.” • • • The first two chapters give an introduction to the key concepts of psychology and its

these conditions are in place, the forecasting methodology lends itself to empirical testing and verification, which will ensure accuracy in terms of identical data extractions when repeated. To classify any forecasting methodology as successful, it needs to produce, with the described conditions being in place, over time, an overperformance; that is, the statistical probability of recurring relationships between the related symbol words and asset prices must beat the correlation with any random

through a continuous monitoring of data sources—allinclusive enough to represent the collective unconscious. Typically the symbol occurrences will dramatically increase as the archetypal force begins to transcend into awareness. Once the archetypal force starts to recede, this will be represented through a similar downturn in symbol frequency. Plotted on a chart, the particular symbol frequency will form a spike. Eventually the archetype will return to its dormant state and the related symbol

slightly skewed Mean True signals Assumed noise + Spike distribution; Gaussian Signal area Noise area Relative frequency Type II error false signal which largely concur with the empirical parameters of the noise distribution but the spikes are more leptokurtic in shape, meaning more extreme in terms of minimums and maximums above the threshold than a Gaussian distribution would suggest. However, for ease of calculation, a Gaussian distribution is applied with the model error acknowledged,

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