Michael Sincere’s All About Market Indicators (McGraw-Hill, 2010) is written primarily for novices, and much of the material is available in other books. But for those who are familiar with basic market indicators Sincere’s book offers something more: dozens of brief interviews with market forecasters, traders, and indicator developers. For instance, Richard Arms recalls that when he thought of his “stupidly simple indicator” in 1967 he sent it to the technical analyst at E.F. Hutton, who in turn sent it to Alan Abelson at Barron’s. Abelson called Arms and said that he wanted to publish the indicator. “Getting a call from Abelson, Arms recalls, was like getting a call from the president.” Soon after Abelson published it, the Trader’s Index, as it was called in Barron’s, was “on quote machines all over Wall Street.” The Quotron machines needed a four-letter abbreviation—hence TRIN, as it’s still known. Arms admits, “I would love to have it changed.” (p. 46)
In the first part of the book Sincere describes, often with the help of their creators, sentiment indicators, technical indicators, and miscellaneous indicators (e.g., seasonal, Fibonacci, and anecdotal). He then turns to the thorny question of how traders anticipate market direction. Here he relies heavily on interviews with Fred Hickey, Linda Raschke, Brett Steenbarger, Alexander Elder, and later in the book Thomas DeMark. The third part deals with volume, especially as understood by William J. O’Neil and Pascal Willain, and high-frequency trading as studied by Shah Gilani.
I decided to share a couple of points from the interview with Gilani, a hedge fund manager and editor of WhatMovesMarkets.com, that I thought might interest my readers. Gilani argues that high-frequency trading is creating “a false perception of liquidity” and skewing the results of using volume as a principal indicator. (p. 164) HFT, which allegedly accounts for over two-thirds of trading volume, started its rise to volume dominance with the advent of ECNs and the proliferation of private trading networks. “Years ago,” Gilani says, “I thought that if someone could create a computer system that could bring all these disparate quotes from all the different trading venues together to get a ‘master quote,’ that person could game the system. And that’s exactly what has happened. High-frequency traders have programmed their computers to track and trade off of all that information flow. … It might have cost some of them $100 million to do it, but they can make that money back in less than a month.” (pp. 165-66) Gilani says that he is spending “a lot of time and money playing in [the HFT] sandbox.” One of his strategies is tracking the spreads between ETFs and their underlying stocks. He admits that “the cost to program this data runs into the millions of dollars. If I’m doing it, you can bet that teams of rocket scientists for all the big trading shops are also doing it. It’s all wrong. The tail is wagging the dog. And the dog just happens to be the capital markets that make our economy.” (p. 168)