Why Stock Markets Crash

Author:

Sornette Didier,Sornette Didier

Abstract

The scientific study of complex systems has transformed a wide range of disciplines in recent years, enabling researchers in both the natural and social sciences to model and predict phenomena as diverse as earthquakes, global warming, demographic patterns, financial crises, and the failure of materials. This book applies the author's experience in these areas to propose a simple, powerful, and general theory of how, why, and when stock markets crash. Most attempts to explain market failures seek to pinpoint triggering mechanisms that occur hours, days, or weeks before the collapse. This book proposes a radically different view: the underlying cause can be sought months and even years before the abrupt, catastrophic event in the build-up of cooperative speculation, which often translates into an accelerating rise of stock market prices, otherwise known as a “bubbles.” The book unearths remarkable insights and some predictions—among them, that the “end of the growth era” will occur around 2050. The book probes major historical precedents, from the decades-long “tulip mania” in the Netherlands that wilted suddenly in 1637 to the South Sea Bubble that ended with the first huge market crash in England in 1720, to the Great Crash of October 1929 and Black Monday in 1987, to cite just a few. It concludes that most explanations other than cooperative self-organization fail to account for the subtle bubbles by which the markets lay the groundwork for catastrophe.

Publisher

Princeton University Press

Cited by 36 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Science or scientism? On the momentum illusion;Annals of Finance;2024-07-02

2. Machine Learning Predictive Analysis for Financial Markets;2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM);2024-04-04

3. Noise effects in Schelling metapopulation model with underlying star topology;The European Physical Journal B;2024-03

4. Predicting discrete-time bifurcations with deep learning;Nature Communications;2023-10-10

5. Non-normal interactions create socio-economic bubbles;Communications Physics;2023-09-20

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