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Rating: 
Summary: California Institute of Technology Graduate Student
Review: I do not recommend this book for the following reasons.1) It could be summarized in 5 pages 2) Some of the assumptions used in the model are not valid 3) I tested the model and it does not work 4) few equations makes it difficult to follow at some points. There are some major shortcomings in this book. Major ones include assuming that the change in the log of the price of a stock is a gaussian random variable and that day to day prices are not correlated. I tested this and it was not true in any of the cases I tried. The world is not gaussian and events are often correlated. For purely academic reading, this book may be interesting, but I suggest that anyone doing so supplement their reading with some real-life statistics of their own.
Rating:
Summary: California Institute of Technology Graduate Student
Review: I do not recommend this book for the following reasons. 1) It could be summarized in 5 pages 2) Some of the assumptions used in the model are not valid 3) I tested the model and it does not work 4) few equations makes it difficult to follow at some points. There are some major shortcomings in this book. Major ones include assuming that the change in the log of the price of a stock is a gaussian random variable and that day to day prices are not correlated. I tested this and it was not true in any of the cases I tried. The world is not gaussian and events are often correlated. For purely academic reading, this book may be interesting, but I suggest that anyone doing so supplement their reading with some real-life statistics of their own.
Rating: 
Summary: Read this book before you invest in your next growth stock
Review: If you aren't too familiar with statistics, this book will be a challenge. Nevertheless, it is a very valuable book, and will help you separate fact from fiction on wall street. Good points: Learn how to use probability theory to determine the expected returns of a stock, its likelihood of profit (or loss), change in margins, etc. Of particular interest--learn how to estimate the standard deviation of a stock's returns by using its high and low prices. This can save some significant number crunching, and you can use another short-hand rule to estimate the annual standard deviation from daily, weekly, or monthly data. Of special importance are the 5 laws of finance-especially law 2. Law 2 states simply: You cannot use historical percentage changes in growth to predict future changes (more technically, past percentage growth has no correlation to future growth). What does Wall Street try to do--predict future growth by past growth (William O'neil readers take note.) A history of rapid growth is no guarantee of future growth. Likewise, a history of poor growth is no guarantee of poor future performance. Maybe Ben Graham was right after all. The one reviewer who said it is typical "technical analysis" must not have read the same book. The main premise is that stock prices follow a "random walk"--meaning you cannot use simple technical rules to predict future returns with any degree of accuracy. The author also pokes some holes in the components of "efficient market theory" especially CAPM. Beta as a description of an individual stock's price moves is questioned. Bad Points: The lognormal distribution was not explained in enough detail. This is a significant flaw, as the rest of the book requires understanding of this vital concept. Once you can get that, you will reap immense benefit from this book.
Rating:
Summary: Read this book before you invest in your next growth stock
Review: If you aren't too familiar with statistics, this book will be a challenge. Nevertheless, it is a very valuable book, and will help you separate fact from fiction on wall street. Good points: Learn how to use probability theory to determine the expected returns of a stock, its likelihood of profit (or loss), change in margins, etc. Of particular interest--learn how to estimate the standard deviation of a stock's returns by using its high and low prices. This can save some significant number crunching, and you can use another short-hand rule to estimate the annual standard deviation from daily, weekly, or monthly data. Of special importance are the 5 laws of finance-especially law 2. Law 2 states simply: You cannot use historical percentage changes in growth to predict future changes (more technically, past percentage growth has no correlation to future growth). What does Wall Street try to do--predict future growth by past growth (William O'neil readers take note.) A history of rapid growth is no guarantee of future growth. Likewise, a history of poor growth is no guarantee of poor future performance. Maybe Ben Graham was right after all. The one reviewer who said it is typical "technical analysis" must not have read the same book. The main premise is that stock prices follow a "random walk"--meaning you cannot use simple technical rules to predict future returns with any degree of accuracy. The author also pokes some holes in the components of "efficient market theory" especially CAPM. Beta as a description of an individual stock's price moves is questioned. Bad Points: The lognormal distribution was not explained in enough detail. This is a significant flaw, as the rest of the book requires understanding of this vital concept. Once you can get that, you will reap immense benefit from this book.
Rating: 
Summary: Great book for sophisticated market analysis
Review: This is an excellent book for traders. It successfully uses probability analysis to predict market and stock fluctuations. Nevertheless, it is too sophisticated for the typical compulsive investor. I recommend this book for people who have an above average understanding of market forecasting.
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