- Christopher T. May, Nonlinear Pricing: Theory & Applications, John Wiley & Sons, 1999 (Hardcover, $69.95)
is a difficult, but ultimately valuable treatise on the new pricing techniques that are revolutionizing modern finance. But while it may prove frustrating for many -- it's part history, part philosophy and only partly a how-to guide -- the dedicated trader will find much of value here.
It is May himself who created the nonlinear pricing page on
, which offers demos of this technique to anyone who wants to look. May also runs
, a New York-based hedge fund that depends on nonlinear pricing techniques to estimate entry and exit points. In short, he has plenty of hands-on experience with his topic.
The use of nonlinear math represents a breakthrough in finance because it explodes the myth that securities prices -- if taken as a whole -- are perfectly random.
Of course, technical analysts try to predict prices. But the linear math most technicians use is based on the assumption that an investment's price behavior can always be represented as a bell-shaped curve. And periodically, these bell-shaped assumptions blow up -- as they did in 1987, and again in August 1998.
The nonlinear math saga started in the 1930s when H.E. Hurst, an English hydrographer (1900-1978), used it to predict rainfall in the African uplands feeding the Nile River, so he could tell dam builders how low their dams could be.
Predicting rainfall over thousands of square miles is similar to predicting stock prices: It represents many random inputs over periods of time that somehow have to be organized into meaningful patterns to make predictions. In short, finding order in what looks like chaos. What May's book tells us -- using accounts, adages, analogies, anecdotes and aphorisms -- is that nonlinear math can consistently find order in the chaos of prices.
May is not a natural writer, and readers will have to forgive his many foibles. Unlike the English writer
, he has not learned to "slay his children," that is, pare down his writing.
But those who already know something about nonlinear math and accept its revolutionary role can go directly to the heart of this book, Chapters 4, 5 and 6. There, they will learn how to apply the
, as May calls it, which objectively quantifies the changes between prices. With it, you can summarize a search of multiple trade entry and exit combinations.
Readers will learn that "nonlinear pricing is anticipatory and conceptually analogous to momentum in that, like any other historical technique, some relationship exists between yesterday and today." They will also learn that by using the H, they can filter purely random movement from a price series -- leaving that part which is predictable -- and they can forecast a reversal of a trend, one of the most difficult challenges for forecasters.
The section applying the theory to everyday investment decisions is, at best, sketchy. However, those who want to put nonlinear math to work can program the H and other formulae from Chapter 4 on their computers -- but it is not a trivial task.
Fortunately, for the nonprogrammers among us, there is another way.
Ward Systems offers a relatively cheap nonlinear math program called
that can apply nonlinear techniques for anyone who can point and click a mouse. Of course, predicting tomorrow's
high or low using nonlinear math with good probabilistic accuracy requires a lot of computing power, but easily within the reach of a
computer equipped with 128 megs of RAM.
May's book is not well written, and can be irritatingly repetitive. To benefit from it, you have to "mine" it for its wisdom. But
helps a reader understand the permanent effect nonlinear math is having on finance. Anyone whose livelihood or dreams depend on trading or investing -- which is just about all of us -- should start digging.
Desmond MacRae is a New York based freelance journalist specializing in banking, finance and investments. He is a regular contributor to Managed Account Reports, Global Investment and Plan Sponsor. TheStreet.com has a revenue-sharing relationship with Amazon.com under which it receives a portion of the revenue from Amazon purchases by customers directed there from TheStreet.com.