In market analysis, price and volume are the most widely used data. Cycles may vary, but open-high-low-close charts with synchronous volume are ubiquitous. There are variations, such as point-and-figure and candlestick charts, and then there are data that include a myriad of advance/decline and options statistics, group averages, sentiment figures, and so on. Still, the data is finite, while the indicators that can massage the data are endless.
Thus, the analyst has a choice. He can shotgun a data stream with multiple indicators each supposedly yielding slightly different information, hoping to weigh the evidence to glean clues to market opportunities, or like a surgeon precisely applying his scalpel, he can use select indicators on different data streams that are unique at providing the market information he needs. However, even to the eyes of an experienced analyst, a plethora of market indicators on a given data stream is rarely the best solution.
Consider an ice fisherman. He can cut a single hole in a lake of ice and then drop his fishing line with 20 baited hooks on that single line. Or, he can cut 20 different holes in the ice in different places around the lake, each with a line and hook of its own. Similarly, the market analyst can apply 20 indicators to the same data stream, or he can select 20 different data streams upon which to apply a single indicator.
The second approach gives the analyst a better chance of arriving at conclusions about valid market anomalies. The conclusions drawn could suggest confirmation of a market theme, or premise, rather than simply a repetition of results from the same data.
DIFFERENT DATA, INDICATORS
In “Options redux: 25 years into the revolution” (November 2008) and in “As smart money goes, so goes the market” (June 2009), we introduced two indicators. The first indicator, a measurement of investor sentiment, relied solely on “weighted” call/put dollar-volume data. The second indicator reflected the tendencies of so-called “big money” by measuring the bias of the most active stocks in an advance/decline series. Both indicators had unique calculations and relied on different data streams to identify key turning points in the stock market. That they produced separate, but coincident, results added to their validity.
Keeping in mind the plausibility of using different indicators on different data streams to produce better results, let’s take a look at some new data on both intermediate (weekly) and major (monthly) cycles.
Created in 1932, the Barron’s Confidence Index (CI) is one of the oldest market sentiment indicators. It has operated on a relatively simple premise for nearly eight decades: a rising CI index is bullish for stocks; a falling CI is bearish. According to Barron’s, the indicator reflects a “high-grade index divided by an intermediate-grade index; decline in latter vs. former generally indicates rising confidence pointing to higher stocks.” More precisely, the index is calculated by dividing the average yield on high-grade bonds by the average yield on intermediate-grade bonds. Investor confidence is supposed to vary relative to the discrepancy in the yields.
Unfortunately, as a pure buy and sell indicator — buying stocks when the index is rising and selling stocks when it is falling — the CI has not worked well. In his seminal work, “The Encyclopedia of Technical Market Indicators,” Robert W. Colby sums up his research on the Confidence Index: “While the Confidence Index produced profits for a long-only strategy in the distant past, it no longer offers worthwhile possibilities for timing the market.”
While we defer to Colby’s broad conclusion about the timing capabilities of the CI, the last 30 years of data show that the CI has some uses beyond just a straight buy and sell indicator. It is possible, especially on the major cycle, to observe inflection points, with levels of support and resistance, trendlines and especially divergent statistical action, that can be used as an adjunct to basic price analysis much as an analyst might use a classic advance/decline series.
The charts accompanying this article depict weekly dates for the S&P 500 index from November 1980 to date, as well as one monthly chart covering the entire period of data collection. The CI is plotted directly below the S&P data. A slow stochastic (input: 10) is applied to both the stock index and the CI. In the major cycle chart, the points where the slow stochastic coincides with the stock index and the CI tend to mark major turning points. For example, notice the lows in mid-1982, late 1987, late 1994, early 2003 and recently in March 2009. Major tops in 2000 and 2007 were coincident with overbought readings and divergent statistical readings in both data series. That underscores the suggestion that an indicator applied to different data streams can produce confirmatory signals.