The New Science of Technical Analysis: Using the Statistical Techniques of Neuroscience to Uncover Order and Chaos in the Markets [Hardcover]

Clifford J. Sherry (Author)

Book Description

Publication Date: September 1994

The genesis for The New Science of Technical Analysis began in the unlikeliest of places: the field of Neuroscience. As Dr. Sherry describes, neuroscientists long held that the output of neurons in the central nervous system is an example of a random walk. Dr. Sherry, believing otherwise, created a series of statistical tests that demonstrated that the neuronal output had discernible patterns. Dr. Sherry began applying his findings to another reputedly random process: the financial markets.

--This text refers to the Paperback edition.

Editorial Reviews

About the Author

Dr. Clifford J. Sherry is a neuroscientist. For most of his career, he has studied information processing in single neurons. Dr. Sherry applied the statistical tools he developed in neuroscience to the investigation of price patterns in financial markets. He reported his findings in a series of articles in Technical Analysis of Stocks and Commodities and in his first book, The Mathematics of Technical Analysis. --This text refers to the Paperback edition.

Product Details

• Hardcover: 300 pages
• Publisher: Probus Professional Pub (September 1994)
• Language: English
• ISBN-10: 1557385025
• ISBN-13: 978-1557385024
• Product Dimensions: 9.5 x 6.5 x 1.2 inches
• Shipping Weight: 1.8 pounds
• Average Customer Review: 2.0 out of 5 stars  See all reviews (3 customer reviews)
• Amazon Best Sellers Rank: #8,247,083 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

5 of 5 people found the following review helpful:

1.0 out of 5 stars Disappointing, January 13, 2006

By 

Relentless Mind - See all my reviews

This review is from: The New Science of Technical Analysis: Using the Statistical Techniques of Neuroscience to Uncover Order and Chaos in the Markets (Paperback)

I bought this book expecting to find statistical techniques as a starting point to technical analysis. Unfortunately, while there are some interesting ideas, the author does not expand on them, rather choosing to give numerous examples which are essentially the same thing applied repeatedly. While he tries to explain the statistical tools in a way accessible to those who are not as familiar to mathematics, I find it to spend too much time trying explain the simple stuff that everyone should know, e.g. how to create a histogram, rather than the more advanced reasoning behind his various tests. As such, he justifies his tools by using the phrase "basic probability theory," which I find to be lacking in substance.

As for the presentation of the material, I also find the quality to be subpar. Many of the figures and tables are at least two or three pages away from the relevant text, which is confusing at best. Also, the author refers to an appendix which is not actually in the book. Furthermore, there some glaring errors, such as referring to the bounds on normally distributed data as Chebychev's theorem, when in fact it's the Empirical rule. Additionally, I found other questionable math in relative price changes section, but on that count, I am not entirely sure that it is erroneous.

Overall, I cannot recommend the book to anyone, it's a shame, because there are some ideas in the book that are worth considering, but they are not covered in any detail and the presentation of the material is such that the book is almost unreadable.

5 of 5 people found the following review helpful:

1.0 out of 5 stars Nothing new, just a rehash, March 10, 2005

By 

Dan (Maryland) - See all my reviews

This review is from: The New Science of Technical Analysis: Using the Statistical Techniques of Neuroscience to Uncover Order and Chaos in the Markets (Paperback)

Having purchased Clifford Sherry's first book ("The Mathematics of Technical Analysis: Applying Statistics to Trading Stocks, Options and Futures") some years ago, I was excited to see a second book ("The New Science of Technical Analysis") appear. Thinking this new book was an expansion of the ideas and concepts presented in the first book, I purchased it.

While I would give Dr. Sherry's first book a 5 star rating, I am slightly disappointed with this second book. Why? There's nothing new in this second book, just a brief redescription of the ideas presented in the first book, along with an analysis of several economic time series.

This book is almost all graphs and tables with very little text. Also, some mistakes appear. The text refers to an appendix that isn't in the book, which is very frustrating. The authors seem to have passed on the opportunity of this second book to correct (what I believe to be) at least one error present in the author's first book.

Overall, I highly recommend purchasing the author's first book "The Mathematics of Technical Analysis: Applying Statistics to Trading Stocks, Options and Futures". The ideas and concepts are innovative and thoughtful and approach the markets with a view not found anywhere else.

Unfortunately, I can't recommend this second book as strongly.

1 of 1 people found the following review helpful:

4.0 out of 5 stars Suitable for those entering the field, February 17, 2005

By 

Dr. Lee D. Carlson (Baltimore, Maryland USA) - See all my reviews
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This review is from: The New Science of Technical Analysis: Using the Statistical Techniques of Neuroscience to Uncover Order and Chaos in the Markets (Paperback)

This book does give an overview of some techniques used to analyze financial time series, and some of these techniques are indeed used to analyze neuronal processes in the field of neuroscience, but it might be somewhat of an exaggeration to say that they are inspired by neuroscience, unless you are a neuroscientist who is also involved in financial analysis. The author of this book is an example of the latter, and has written a book that discusses some of the statistical tools he developed for neuronal processes, and how they can be applied to the analysis of price patterns in financial markets. Most of the techniques are well known, and readers with a strong background in probability theory and statistics will find nothing new in the book. However, readers who are in the early stages of educating themselves on the statistical analysis of economic and financial time series should find the book very useful. Most of the page space is devoted to graphs, but the author does give insightful opinions on the use of the statistical tools.

Realizing that a rigorous definition would take sophisticated mathematics, the author gives a nonmathematical definition of the terms `independent' and `random.' An event is `independent' if it is not influenced by the outcome of a previous event in time, and does not influence any events that come after it. Thus the independence of an event must be discussed in relation to other events, i.e. on whether or not past events can influence it, and whether it can influence future events. A `random' event, on the other hand, is one that is determined by chance. The notion of `chance' is related to probability theory by the author, via the law of large numbers. The author also is careful to point out that the techniques he develops in the book can only be applied to `stationary' time series. He again uses a nonmathematical definition, and defines `stationarity' as meaning that the underlying rules that generated the time series do not change with time. If a time series is nonstationary then the rules that generated it change over time. Stationarity, independence, and randomness are the parameters of the time series the author is concerned with in the book, and he emphasizes that these parameters are independent.

The author has developed many of the tools in an earlier book that he has written, and the reader will have to consult this book for more in-depth discussion. He does however include many helpful insights on statistical data analysis. Some of these are: 1. The use of detrending on economic time series that contain a significant trend. What constitutes a "significant" trend is not discussed. 2. The use of the `differential spectrum' to determine if a time series is independent or not. This method is based on the notion that the distribution of positive and negative price changes will be symmetrical for an independent time series. The bin widths and the number of price changes will determine the sensitivity of this method, as the author illustrates using several examples. He also shows how to use the chi-square statistic to compare the relative distribution of positive and negative price changes. 3. The use of the `relative price change' to determine which kinds of serial dependences are present in a time series and to determine the duration of a `temporal window' during which the price changes are not independent. Readers familiar with n-gram methods in computational linguistics will see them here in another guise.