Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications (New York Institute of Finance)

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Reference
Good Even for a Random Walker
Everything a beginner needs to know
Classic Technical Analysis Book
Excellent introduction to TA - an easy read

Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications (New York Institute of Finance)
John J. Murphy
Manufacturer: Prentice Hall Press
ProductGroup: Book
Binding: Hardcover

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ASIN: 0735200661

"If one could read only one book on technical analysis, this should be the one." --Knight-Ridder Financial Products and News (on the first edition, Technical Analysis of the Futures Markets, 0-13-898008-X)

This outstanding reference has already taught thousands of traders the concepts of technical analysis and their application in the futures and stock markets. Covering the latest developments in computer technology, technical tools, and indicators, the second edition features new material on candlestick charting, intermarket relationships, stocks and stock rotation, plus state-of-the-art examples and figures. From how to read charts to understanding indicators and the crucial role technical analysis plays in investing, readers gain a thorough and accessible overview of the field of technical analysis, with a special emphasis on futures markets. Revised and expanded for the demands of today's financial world, this book is essential reading for anyone interested in tracking and analyzing market behavior.

"One way to get started in technical analysis is to read a good book on the subject. One of my favorites is Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications by John J. Murphy. It's an easy read." Ralph J. Acampora, CMT, Managing Director, Prudential Securities Inc.

Is the material here ambitious and audacious? Well... yes, wouldn't it be if the author were on to something? I think that the key here is to understand where this information can be immediately used in engineering practice. The principle of design decoupling and Okcam's Razor are not particularly new ideas, but the way that the author is approaching the subject is important. He is trying to get a handle on something that has been to date very heuristically practiced, and often not well executed. If one understands the principles of, for instance, software engineering, it doesn't take long to understand where the author is going with his subject. Suh is a mechanical engineer (MIT), and this fact, in itself, is very surprizing and encouraging. The axioms that Suh present are necessary in order to achieve some sort of order to the discussion. I personally have managed to get past the initial objections to his reuse of certain terms and emphasis on certain axioms. For instance, Suh's principle of "minimum information content" is actually a statement about "less is more" in design from a reliability and design-robustness perspective.

Now for potential buyers: understand that there are applications of this method and theory that are extremely powerful and effective. This method has great implications to the field of systems engineering - it ties the SE discipline to something that you can get your hands on. In particular, look to the application of the method to the design of physical systems and their corresponding manufacturing systems. This isn't a trivial or invalid subject.

People who need ways to handle complexity and the total system design problem will benefit as will designers of common products. Software and hardware designers can both benefit, although the author uses language that is more recognizable in the realm of mechanical engineering and manufacturing systems. This material will take some time to integrate into your own storehouse of knowledge - so don't rush it. Approach this material with a need in hand, but with the understanding that it will likely modify your view of your discipline. Then you will make use of this material. Note the bibliographies; dig into the applications of this theory. You won't be disappointed in the outcome.

"This is a first-class book dealing with one of the most important areas of current research in applied statistics…the methods described are widely applicable…the standard of exposition is extremely high."
--Short Book Reviews from the International Statistical Institute

"The new chapters (10-14) improve an already excellent resource for research and instruction. Their content expands the coverage of the book to include models for discrete level-1 outcomes, non-nested level-2 units, incomplete data, and measurement error---all vital topics in contemporary social statistics. In the tradition of the first edition, they are clearly written and make good use of interesting substantive examples to illustrate the methods. Advanced graduate students and social researchers will find the expanded edition immediately useful and pertinent to their research."
--TED GERBER, Sociology, University of Arizona

"Chapter 11 was also exciting reading and shows the versatility of the mixed model with the EM algorithm. There was a new revelation on practically every page. I found the exposition to be extremely clear. It was like being led from one treasure room to another, and all of the gems are inherently useful. These are problems that researchers face everyday, and this chapter gives us an excellent alternative to how we have traditionally handled these problems."
--PAUL SWANK, Houston School of Nursing, University of Texas, Houston

Popular in the First Edition for its rich, illustrative examples and lucid explanations of the theory and use of hierarchical linear models (HLM), the book has been reorganized into four parts with four completely new chapters. The first two parts, Part I on "The Logic of Hierarchical Linear Modeling" and Part II on "Basic Applications" closely parallel the first nine chapters of the previous edition with significant expansions and technical clarifications, such as:

* An intuitive introductory summary of the basic procedures for estimation and inference used with HLM models that only requires a minimal level of mathematical sophistication in Chapter 3
* New section on multivariate growth models in Chapter 6
* A discussion of research synthesis or meta-analysis applications in Chapter 7
* Data analytic advice on centering of level-1 predictors and new material on plausible value intervals and robust standard estimators

While the first edition confined its attention to continuously distributed outcomes at level 1, this second edition now includes coverage of an array of outcomes types in Part III:

* New Chapter 10 considers applications of hierarchical models in the case of binary outcomes, counted data, ordered categories, and multinomial outcomes using detailed examples to illustrate each case
* New Chapter 11 on latent variable models, including estimating regressions from missing data, estimating regressions when predictors are measured with error, and embedding item response models within the framework of the HLM model
* New introduction to the logic of Bayesian inference with applications to hierarchical data (Chapter 13)

The authors conclude in Part IV with the statistical theory and computations used throughout the book, including univariate models with normal level-1 errors, multivariate linear models, and hierarchical generalized linear models.