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Why Stock Markets Crash: Critical Events in Complex Financial Systems, by Didier Sornette
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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 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.
- Sales Rank: #878560 in Books
- Brand: Brand: Princeton University Press
- Published on: 2002-12-08
- Original language: English
- Number of items: 1
- Dimensions: 1.32" h x 6.52" w x 9.44" l,
- Binding: Hardcover
- 448 pages
Features
- Used Book in Good Condition
From Publishers Weekly
It’s everybody’s favorite topic of conversation at the moment: why did the Dow and the Nasdaq tank so horrifically, and where did all the money go? UCLA professor Sornette does his best to tackle those questions. While CNBC anchor Ron Insana’s recent Trend Watching took a reader-friendly look at the history of market bubbles, Sornette’s approach is decidedly different. Befitting his status as an expert in geophysics, the author loads the text with enough charts, graphs and advanced economic theory to choke John Kenneth Galbraith (one chapter subheading, for instance, is "The Origin of Log-Periodicity in Hierarchical Systems"). It’s a meaty book, with helpful autopsies of past crashes ranging from tulip mania in the Netherlands to the Nasdaq crash of April 2000, as well as information on how crashes might be predicted in the future. Unfortunately for the average investor who tends to get burned after these bubbles, Sornette’s conclusion is that a mixture of "systemic instability" and plain old human greed means that market bubbles aren’t about to disappear anytime soon. And neither, of course, will the subsequent crashes.
Copyright 2003 Reed Business Information, Inc.
Review
"Sornette is both a statistical physicist and a member of a new breed of scientist: the econophysicist. . . . But Sornette's book is not just about finance and economics; it is also a mesmerizing introduction to game theory, fractals, catastrophe theory, critical phenomena, and much more. No prior knowledge of finance or economics is needed to understand the book. . . . Throughout the book, Sornette makes numerous, vivid comparisons with many other fields in which the various mathematical tools he describes can be applied."--Frank Cuypers, Physics Today
"The book is written in a readable style and does not require technical knowledge. Any reader interested in a serious approach to the origin and possible prediction of financial bubbles will enjoy reading it."--Josep M. Porra, Journal of Statistical Physics
"A highly recommended, enjoyable, well-researched, and thought-provoking book for anyone interested in stock markets and the modeling of financial processes."--Rick Gorvett, Journal of Risk and Insurance
"While it's difficult to pinpoint what type of trader would enjoy this book the most, I think there's something for everyone, whether you're a quaint, technical trader or a fundamentalist. . . . I feel that I'm smarter after finishing this book; I thoroughly enjoyed the lengthy journey, and would recommend this to any stock market enthusiast."--Jeff Pierce, Seeking Alpha
From the Inside Flap
"A professor of geophysics gives a very different perspective, informed by his scientific training, on the stock market. I am sure that his view will be highly controversial, but the book is fascinating, and mind-expanding, reading."--Robert Shiller, author of Irrational Exuberance
"Why Stock Markets Crash addresses a current and enduring concern for all investors, the seemingly mysterious twists and turns the markets take. Didier Sornette's insights into why markets behave as they do are fresh, productive, and provocative. This work is bound to become an important baseline for anyone trying to understand what will happen next in the stock and currency markets not only in the U.S. but in Europe and Asia as well. It is well written and accessible to non technical audiences."--Richard N. Foster, Director, McKinsey & Company
"This is a most fascinating book about an intriguing but also a controversial topic. It is written by an expert in a very straightforward style and is illustrated by many clear figures. Why Stock Markets Crash will surely raise scientific interest in the emerging new field of econophysics."--Cars H. Hommes, Director of the Center for Nonlinear Dynamics in Economics and Finance, University of Amsterdam
"In turbulent times for financial markets, more books than usual are published on such subjects as financial crashes. This book is different. First, it is written by an internationally recognized expert in non-linear, complex systems. Second, it promotes some new ideas in both finance and science. In addition, it offers the general reader an insight into finance, both practical and academic, as well as some of the issues at the cutting edge of science. What more could one ask for?"--Neil F. Johnson, Department of Physics and Oxford Center for Computational Finance, Oxford University
"In turbulent times for financial markets, more books than usual are published on such subjects as financial crashes. This book is different. First, it is written by an internationally recognized expert in non-linear, complex systems. Second, it promotes some new ideas in both finance and science. In addition, it offers the general reader an insight into finance, both practical and academic, as well as some of the issues at the cutting edge of science. What more could one ask for?"--Neil F. Johnson, Department of Physics and Oxford Center for Computational Finance, Oxford University
Most helpful customer reviews
0 of 0 people found the following review helpful.
A very good, but technical, book about stock market crashes
By RrLaw
The author discusses the application of non-linear modeling techniques on the financial market. Given the behavior of financial market is the result of the inter-working of countless investors, it's very surprising and interesting to see these modeling techniques actually produce some very good results. In particular, the author presents the logic behind the formation and the bursting of bubbles, and, more importantly, provides insight of what we can expect from the financial market in the long term.
Please note this is a very technical book for the general public. You don't need a PhD to understand it, but you do need to be comfortable with data plots and discussions of equations. You also need more than a general understanding of statistics; concepts such as correlation, regression, and model fit should not be intimidating to you. Some background in Physics will also be helpful, especially if you already understand the equation of oscillatory motion.
0 of 0 people found the following review helpful.
A Mathematical Model for the Fracture Mechanics of Stock Markets
By Ashok K. Vaish
An excellent book that uses mathematical models of critical events from physics and applies them to stock market crashes. Sornette claims that market crashes leave a distinctive precursor signal in market price data that can be measured to predict the crash. If you are mathematical enough to follow at the level of high school math this book presents an insightful picture about the log periodic power waves that emerge in stock price time histories when bubbles form. These can be statistically detected and are a very good predictor of a crash.
16 of 18 people found the following review helpful.
Very convincing
By Dr. Lee D. Carlson
Buy low and sell high: In trading stocks this cliché is obvious, but its ramifications can be extremely painful for all those involved, as well as many who are not, especially when a large collection of traders act in concert and engage in massive sell-offs. Financial bubbles, stock market crashes, and out-of-control speculation have been the subject of countless books and research papers and dozens of Hollywood movies, and mathematicians, economists, systems analysts, and financial engineers have spent thousands of hours of time attempting to understand and predict financial meltdowns, all with varying degrees of success. Some of these researchers have argued that it is the large-scale, collective properties of the financial markets that must be understood if stock market crashes are to be predicted or at least anticipated quantitatively.
The author of this book, a geophysicist by training, is one of these and has taken the "buy low-sell high" strategy and some straightforward mathematical constructions to give an interesting and plausible explanation of why stock markets crash. Intuitively, traders synchronously try to buy low and sell high, and a "herding effect" results in a rapid sell-off that results in a market crash. This behavior is analogous to what one observes in physical systems that are close to a `critical point', where they can undergo `phase transitions' and their behavior as they near the critical point has been shown to be "universal" in the sense that quantities called `scaling exponents' can be calculated explicitly and measure the "universality" of the systems near the critical point.
Naturally the stock market crashes of 1929 and 1987 are ones that that will stand out in every reader's mind and ones that must be test examples for the author's assertions. He discusses these two examples and many others in enough detail that a convincing case is made. The techniques he uses however are out of the mainstream of econometrics, and no doubt some in this mainstream will there be highly skeptical of his conclusions. The physicist-turned-financial engineer however will be delighted, as there will be many familiar concepts and constructions throughout the book. There may be a few new ones to such a reader however, such as `algorithmic complexity' and `computationally irreducible'. The clarity of the author's writing and the many real-world examples that he employs makes the assimilation of these concepts and others much more palatable than would be the case in a standard mathematical monograph on the subject. In addition, the techniques he uses can be applied to areas of finance that are not discussed in the book, such as the mortgage "antibubble" in net credit losses for second-lien (HELOC) loans that began in the second quarter of 2006. And with respect to applications, the author is honest enough to note that he refrains from discussing many of them in detail since his involvement in them is strictly proprietary. Most refreshingly, charts, graphs, and tables appear throughout the book, as would be necessary in the validation of any algorithm or predictor in financial theory.
There are also many interesting "toy models" in the book that enhance the didactic quality. One of these concerns the dependences that can occur in successive price variations. The author constructs a model where there is zero correlation but where one can predict the current price variation with an accuracy of over 50% by only knowing the variations in the past two days. This example serves as a good counter to the idea that the frequency distribution and two-point correlation function must always be non-zero in order to obtain successful prediction. The author goes on to use this example to show how "drawdowns", rather than the correlation structure, play the predominant role in measuring price changes. This example, among others, also illustrates the author's belief that the "standard" models in the financial industry have great difficulty in dealing with large financial crashes.
Although the author uses somewhat elementary mathematics in the book, its implications are profound. One will find for example discussions of log-periodic oscillations in hierarchical systems, the renormalization group, and complex fractal dimension. Central to the author's case is the concept of discrete scale invariance, which is a specialization of the continuous case, and which is manifested in real data by log-periodic oscillations. These should be viewed as corrections to simple power law scaling. The author discusses a few natural systems that exhibit log-periodicity, such as bats and dolphins, and in evolutionary biology. He also discusses other examples in mathematics such as the Newcomb-Benford law. Pre-crash stock market data is fitted to expressions that have log-periodic corrections to a pure power law and the validity of the fits discussed in great detail. The author's arguments are powerful and convincing, and the formalism that he outlines needs to be part of every financial analyst's toolbox.
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