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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. In this book, Didier Sornette boldly applies his varied experience in 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. In this book, Didier Sornette boldly applies his varied 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. Sornette 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 the market price, otherwise known as a "bubble." Anchoring his sophisticated, step-by-step analysis in leading-edge physical and statistical modeling techniques, he unearths remarkable insights and some predictions--among them, that the "end of the growth era" will occur around 2050. Sornette 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. He 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. Any investor or investment professional who seeks a genuine understanding of looming financial disasters should read this book. Physicists, geologists, biologists, economists, and others will welcome "Why Stock Markets Crash" as a highly original "scientific tale," as Sornette aptly puts it, of the exciting and sometimes fearsome--but no longer quite so unfathomable--world of stock markets.


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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. In this book, Didier Sornette boldly applies his varied experience in 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. In this book, Didier Sornette boldly applies his varied 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. Sornette 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 the market price, otherwise known as a "bubble." Anchoring his sophisticated, step-by-step analysis in leading-edge physical and statistical modeling techniques, he unearths remarkable insights and some predictions--among them, that the "end of the growth era" will occur around 2050. Sornette 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. He 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. Any investor or investment professional who seeks a genuine understanding of looming financial disasters should read this book. Physicists, geologists, biologists, economists, and others will welcome "Why Stock Markets Crash" as a highly original "scientific tale," as Sornette aptly puts it, of the exciting and sometimes fearsome--but no longer quite so unfathomable--world of stock markets.

30 review for Why Stock Markets Crash: Critical Events in Complex Financial Systems

  1. 5 out of 5

    Jacob

    It is fair to compare this book to the Black Swan by Nassim Taleb. Here an attempt is made to analyze and quantity instabilities of the Black Swan variety; mostly the [stock] market(s) but the final chapter contains an analysis of civilization itself. The book is VERY rich in concepts and ideas, much more so than most books. It also assumes a lot from the reader. If, for example, Ising model, K-selected, or Polya's Urn doesn't ring a bell for you, this may be too difficult a read.

  2. 5 out of 5

    Stephan Pire

    I greatly recommend this book. It goes from the history of crashes down to the detail on how to predict market events through physics (fractal)

  3. 4 out of 5

    Walter Cavinaw

    Because I read this book on and off over the course of a year without taking notes, some of the details are more fuzzy. Didier Sornette took his work on self-organized critical from earthquakes and materials failure and applied it to the stock market. The central idea of the book is to examine the crash pattern formed through log periodic oscillations around a power law (LPPL = log periodic power law). Because power laws are commonly observed in fractals he relates the LPPL to fractals and discret Because I read this book on and off over the course of a year without taking notes, some of the details are more fuzzy. Didier Sornette took his work on self-organized critical from earthquakes and materials failure and applied it to the stock market. The central idea of the book is to examine the crash pattern formed through log periodic oscillations around a power law (LPPL = log periodic power law). Because power laws are commonly observed in fractals he relates the LPPL to fractals and discrete self similarity. One of the more interesting sections looked at the reasons for why this might occur. The description that most stood out to me was around a hierarchical Ising model that can form the log periodic oscillation. Based on my personal experience I believe an Ising model, or agent-based model of market interactions is more realistic so I really liked this description. Some of his students seem to be examining the market from this light. He goes on to demonstrate the LPPL model on a few instances through time. His group has even published some of their calls in real time. They have a few different formulations of the model, the differences of which I can't remember. He goes on to speculate on the possibility that their are log periodic power laws in the structure of society such as technology evolution or demographics. The idea presented in the book was super important and I have spent a significant amount of time reading through papers on this and similar topics. Chris Kaufmann of Parallax Financial Research has also done incredible research in this area (and first got me interested in this topic). His interviews and writing are sparse but worth checking out if these ideas are interesting. Some readers might find the idea untenable because what it implies for things like technical analysis. In a way it is maybe a more advanced form of technical analysis which has a scientific basis and is tested. The writing on the other hand is bland. It is unfortunately obvious that it is written by an academic. In part that's why it took me so long to finish.

  4. 5 out of 5

    Richard Crowder

    Do not buy this as an e-book; a couple of Princeton e-books on mathematical subjects that I've bought had bad misprints in the formulas. For this book, I read the paperback 2017 edition with a new preface by the author. Stock-market crashes generally take everyone by surprise--they feel like bolts from the blue. They're usually not. Sornette shows how the interplay of greed, fear, and imitation among investors and traders creates an accelerating rhythm of sudden rises alternating with increasingl Do not buy this as an e-book; a couple of Princeton e-books on mathematical subjects that I've bought had bad misprints in the formulas. For this book, I read the paperback 2017 edition with a new preface by the author. Stock-market crashes generally take everyone by surprise--they feel like bolts from the blue. They're usually not. Sornette shows how the interplay of greed, fear, and imitation among investors and traders creates an accelerating rhythm of sudden rises alternating with increasingly brief pauses. This "mathematical signature" can begin months or years in advance, but its predictive value rises in the last year before the death of the bubble (which may be relatively calm, but usually is followed by a crash). Sornette presents the results of several predictions made using this technique. While his track record is not perfect, it is strongly better than what could be expected from chance. Although the math is advanced, the discussion and the graphs make the argument clear to the lay reader. What about the everyday investors who don’t have access to Sornette’s computational skills? The lesson is straightforward: as markets rise, and especially as they rise sharply, so does the danger of a crash. As they watch a sharp rise, investors should reduce their equity positions to capture gains made so far and limit the danger to their portfolios. But let's assume that you're not in the stock market and don't plan to be. The last chapter broadens the discussion to consider a wide range of problems confronting the world in the period from the year of publication (2002) to the potential "end of the growth era" around 2050. Many of the trends described have only become more pressing since 2002. This book is both important and fascinating--not just for investors but also for citizens of an uncertain world.

  5. 4 out of 5

    Andrew Davis

    An extremely informative text for those interested in economics and econometrics. The author is a leading authority in the field. He covers a range of approaches in analysing variations of the stock markets. This is not a book to be read and put aside, but rather referred to from time to time. The additional benefit of the book is almost 500 references that extend on the topics discussed in the book. Due to limited size of the book, some of them are critical in understanding the various topics. T An extremely informative text for those interested in economics and econometrics. The author is a leading authority in the field. He covers a range of approaches in analysing variations of the stock markets. This is not a book to be read and put aside, but rather referred to from time to time. The additional benefit of the book is almost 500 references that extend on the topics discussed in the book. Due to limited size of the book, some of them are critical in understanding the various topics. The book covers in details the GARCH models, complex fractal dimensions and log-periodicity. It conducts a forensic analysis of major crashes and provides guidelines for predicting any future disturbances.

  6. 4 out of 5

    Jesse Ammon

    Overly detailed on tangential topics, and highly mathematics based, it does provide an interesting model for predicting stock market crashes through power laws and log-periodicity. This gains my attention because it appears to improve on Hurst cycles and other work on finding waves in the market. For those seeking to find the holy grail of a mathematical model for the market, this is compelling. Translating it into an actionable tool is probably far away.

  7. 5 out of 5

    Lukasz

    recommendation: [The best books on Physics and Financial Markets | A Five Books interview](https://fivebooks.com/best-books/jame...) recommendation: Chapter 7 of [The Physics of Wall Street: A Brief History of Predicting the Unpredictable by James Owen Weatherall | Goodreads](https://www.goodreads.com/book/show/1...) recommendation: [The best books on Physics and Financial Markets | A Five Books interview](https://fivebooks.com/best-books/jame...) recommendation: Chapter 7 of [The Physics of Wall Street: A Brief History of Predicting the Unpredictable by James Owen Weatherall | Goodreads](https://www.goodreads.com/book/show/1...)

  8. 5 out of 5

    Yates Buckley

    An important technical text that evalutes the dynamics of market crashes, through simulation and mathematical modeling. Thr text is also confusignly written, disorienting in structure, so that by the end you are not sure what to take away to your everyday life.

  9. 4 out of 5

    Yannick Daelemans

    Good book, about a good idea. The idea that bubbles are statistical anomalies, and how to recognize them, was very good given. However, the book is a bit too wide, going over too many topics sometimes.

  10. 5 out of 5

    Ethan Drower

    I'd skip this one.

  11. 4 out of 5

    Mario C

    Thoughts on what is a very interesting book on capital markets behavior that is not well understood. Stock market crashes are caused by the slow build-up of long range correlations leading to a global cooperative behavior of the market and eventually resulting in a collapse in a short, critical time interval • Crash may be caused by local self-reinforcing imitation between traders. If traders are more likely to imitate other traders which increases up to a certain point – critical point – by placi Thoughts on what is a very interesting book on capital markets behavior that is not well understood. Stock market crashes are caused by the slow build-up of long range correlations leading to a global cooperative behavior of the market and eventually resulting in a collapse in a short, critical time interval • Crash may be caused by local self-reinforcing imitation between traders. If traders are more likely to imitate other traders which increases up to a certain point – critical point – by placing the same type of order – all buys or all sells – increases the probability of a crash. Frame work for understanding needs to be probabilistic b/c crashes are not certain outcomes • Bubbles manifest themselves as overall super-exponential power-law acceleration in the price decorated by log-periodic precursors – a concept related to fractals • Bubbles across epochs share a common underlying background as well as structure o Rationale: rooted in the fact that humans are endowed with the same basic human emotions across history – fear and greed. • Large crashes are not the same as a single large decline – they are fundamentally different events. i.e. A 1% decline is not the same event as a 10% decline and cannot be modeled as such Financial Crashes are Outliers • In the Bachelier-Samuelson financial world, in which distributions are normally (Gaussian, bell shaped) distributed, all returns are scaled according to a fundamental “ruler” called Standard deviation • In reality, returns are not Gaussian • They are not far from exponential law • Under Gaussian assumptions: Oct 19/1987 -22.6% & Oct 20, 1987 rebound +9.7% should not occur. They are essentially impossible • Under exponential law: rebound of 9.7% less extraordinary, once every 22,026 days or 88 yrs, -22.6% should occur every 520 million yrs – still qualifies as an outlier • Can’t look at individual return data and assume that each successive return is uncorrelated. Drawdowns preserve the information in busts of activity that demonstrate local dependence. • Body and tail of distributions are made up of 2 different populations that have different physics, scaling & properties. • Large outliers are not scaled up versions of small fluctuations o Distribution is made up of 2 different populations – the body & tail, which have different physics, scaling & properties • Drawdown calculation, rather than daily or weekly returns or any other fixed time scale returns, are more adequate time-elastic measures of price moves o Simply looking at daily returns and their distributions destroys information that the daily returns may correlated at specific times Positive Feedbacks • positive feedback asserts that the higher the price or the price return in the recent past, the higher will be the price growth in the future • There is growing empirical evidence of the existence of herd or “crowd” behavior in speculative markets. It Is Optimal to Imitate When Lacking Information Modeling Financial Bubbles and Market Crashes Models are synthetic sets of rules, pictures, and algorithms providing us with useful representations of the world of our perceptions and of their patterns • More correct – bounded rationality. They do not have perfect knowledge. “long list of irrational or anomalous behavior shown by human beings in certain specific systematic ways should not confuse us: the relevant task for understanding stock markets is not so much to focus on these irrationalities but rather to study how they aggregate in the complex, long-lasting, repetitive, and subtle environment of the market” The Risk-Driven Model • Models investor behaviours, developed to formalize herd behavior or mutual mimetic contagion in speculative markets • The emergence of bubbles is explained as a self-organizing process of infection among traders • Its key assumption is that a crash may be caused by local selfreinforcing imitation between traders. This self-reinforcing imitation process leads to the blossoming of a bubble. If the tendency for traders to “imitate” their “friends” increases up to a certain point called the “critical” point, many traders may place the same order (sell) at the same time, thus causing a crash. The interplay between the progressive strengthening of imitation and the ubiquity of noise requires a stochastic description: a crash is not certain but can be characterized by its hazard rate that is, the probability per unit time that the crash will happen in the next instant provided it has not happened yet. • Since the crash is not a certain deterministic outcome of the bubble, it remains rational for traders to remain invested provided they are compensated by a higher rate of growth of the bubble for taking the risk of a crash, because there is a finite probability of “landing smoothly,” that is, of attaining the end of the bubble without crash. In this model, the ability to predict the critical date is perfectly consistent with the behavior of the rational agents: they all know this date, the crash may happen anyway, and they are unable to make any abnormal riskadjusted profits by using this information

  12. 4 out of 5

    Stevenglinert

    I read most of this while shit was running on my computer in the GSB library. I'm sure the MBAs love it. So, like Lacan always uses math terminology in this like weird bullshit way and this was similar. This felt like a guy trying to rewrite Kindleberger with more complex math and a cool cover. Wow such fractal. But he's a geophysicist and like, his attempts at economics and social science came off as forced. Just read Kindleberger and Shiller's Irrational Exuberance and like don't bother.

  13. 5 out of 5

    Andre Luis

    Não pude ler o livro todo. Muito matemático e demonstra muita confiança em equações baseadas em dados históricos. Apresenta algumas argumentações interessantes sobre como modelar o mercado de capitais e sobre "rational expectations". Válido para lembrar sobre bolhas e seus efeitos.

  14. 4 out of 5

    Chris

    http://seekingalpha.com/article/11887... http://seekingalpha.com/article/11887...

  15. 5 out of 5

    Chang Lan

    Ising + Hierarchical Structure = Power Law + Log Periodic

  16. 5 out of 5

    Platzer Peter

  17. 5 out of 5

    Tim Kohn

  18. 4 out of 5

    Daniel

  19. 4 out of 5

    Balraj Mattu

  20. 5 out of 5

    Jeff Wilsbacher

  21. 5 out of 5

    Nick Gogerty

  22. 4 out of 5

    Nudgem

  23. 4 out of 5

    Clare

  24. 5 out of 5

    Paresh

  25. 4 out of 5

    Matheus Melhado

  26. 4 out of 5

    Stanley Lee

  27. 4 out of 5

    Jovany Agathe

  28. 4 out of 5

    Oliver

  29. 4 out of 5

    Sean Mckeown

  30. 4 out of 5

    jency patel

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