Fractal Market Analysis: Applying Chaos Theory to Investment and Economics

by Edgar E. Peters

A leading pioneer in the field offers practical applications of this innovative science. Peters describes complex concepts in an easy-to-follow manner for the non-mathematician. He uses fractals, rescaled range analysis and nonlinear dynamical models to explain behavior and understand price movements. These are specific tools employed by chaos scientists to map and measure physical and now, economic phenomena.

• Format: Hardcover
• Language: English
• ISBN: 0471585246
• ISBN13: 9780471585244
• Release Date: January 1994
• Publisher: Wiley
• Length: 336 Pages
• Weight: 1.40 lbs.
• Dimensions: 1.1" x 6.6" x 8.8"

Customer Reviews

Gets you up and running with chaos theory for time series

This book includes a very detailed description of how to apply some chaos theory techniques - primarily R/S analysis - to time series data. With this technique, one can gauge whether a time series is completely random, completely predictive, or a mixture of these. This book glosses over some conceptual topics such as Efficient Market Theory and the Fractal Market Hypothesis in favor of details to perform a rigorous statistical analysis. These conceptual topics are better covered in Peters' earlier work "Chaos and Order in the Capital Markets".For the analytically oriented reader, there can be much frustration as equations are often initially presented in sloppy and unusable forms with undefined parameters (hence 4 of 5 stars). However, these are subsequently broken down and presented in a step-by-step manner that will allow most readers to implement his techniques.Overall, this is an excellent introductory book for the practitioner or economist, not so great for the non-technical reader.

simple as possible intro to fractals and markets

The title only indicates part of the true subject matter of this book. The book teaches about fractal analysis of any data set, and uses financial markets as special cases to illustrate the concepts involved in fractal analysis. He begins with a brief, but facinating history of fractals, and you learn the concepts you will need to form your own trading strategies. Mr. Peters demonstrates an easy familiarity with fractals, and this serves to keep the book interesting through its most difficult mathmatical passages.

THE book for your money in the market

The author has excellent experiences in all kinds of markets and true understanding of how to put them all together. The explaination of relationship between price and volatility is truth telling. A timeless classic.

The best reference I've seen for the non-statistician.

This, the second of Edgar Peter's books advancing and investigating an advanced market view, is clearly the best reference I've seen that is still mostly comprehensible to someone outside the field of formal market statistics. And I've spent a fortune on books trying to grasp what Mr. Peters explains successfully in this one volume.For readers trying to comprehend a market model beyond the standard EMH, or interested in the most common-sense piece of work I've seen to date in market-relevant time-series analysis, please check out Edgar's book. I don't see how you could be disappointed.Dan Reinhart (the Danimal)

Hands down the best book on finance I have ever read.

Reviewed by Michael P. Corning Edgar E. Peters wasn't satisfied with the Efficient Market Hypothesis (EMH). With the publication of his first book, Chaos and Order in Capital Markets, John Wiley & Sons, New York, 1991, he went public with his concerns about its underlying assumptions and with its empirical shortcomings. That book, a manifesto really, was followed last year by Fractal Market Analysis: Applying Chaos Theory to Investment & Economics (FMA). Where his first book broke ground, FMA has laid the foundation of a new conceptual infrastructure of capital markets. Risk From The Past Much of Peters argument is based on two things: one hundred three years of daily Dow Jones Industrial Average data, and Rescaled Range (R/S) analysis. He begins FMA by demonstrating that capital market returns in the United States are not a truly random walk. Instead, he contends they are a biased random walk and indicate a long memory process; they are persistent. Specifically. he characterizes their short term behavior (less than 1,200 days) as a stochastic nonlinear process and their long term behavior as a nonlinear dynamic, or chaotic, process. As a result, he enlarges the definition of risk to include a phenomenon he discovered about persistent processes: they are mirrored by antipersistent ones. If persistent processes are less random than random ones, antipersistent processes reverse themselves more often than random ones. An early insight due to this discovery is that risk in not merely the deviation from an expected value, viz., standard deviation, but the velocity of the second difference of price changes. Peters offers the Stable-Levy, or fractal, frequency distribution as a more faithful representation of capital markets. When two key variables are fixed at certain levels, the normal distribution becomes a special case of fractal distributions. To hear that the random walk is a special case should be no more surprising than to hear that visible light is a special case of the electromagnetic spectrum. It is not so much a matter of losing something; instead, vast amounts of knowledge remain invisible as long as the old assumption remains intact and tools tuned to the different frequencies remain undeveloped. Instruments tuned to gamma, X-ray, infrared, and radio frequencies have shown astronomers far more about our universe than the special case of visible light ever could. Both these facts, that finance time series are not random and that the Gaussian assumption is a special case of fractal distributions, suggest that: 1. major rethinking about risk and diversification is necessary, 2. new statistical tools need to be created, and 3. very exciting discoveries are in store for us. Risk in the Present While examining the same historic data at different time scales, Peters made another discovery. He found that the frequency distributions of investment horizons ranging from 1-day to 90-day intervals had the same shape. As a result, he conc