Intermarket analysis uses known relationships between markets to develop trading rules. It has proven a useful approach for a number of markets, including the S&P 500, Treasuries, Eurodollars, gold, crude oil and more. Numerous magazine articles and books have examined the effectiveness of this technique. An early source of note was technician John Murphy’s first book, "Intermarket Technical Analysis" (John Wiley and Sons, 1991).
The book uses the 1987 crash to lay out an intermarket hypothesis. Even by the mid-1990s, these concepts still were new. At the time, there was little public verification they worked. Many institutional traders used the concepts, but mechanical rules generally were not available pubicly.
While you can make market predictions using other techniques, intermarket analysis is one of the few that offers a strong basis for making predictions based on solid fundamental reasoning.
Correlation is not prediction
When most traders get started with intermarket analysis, they begin by looking for correlated markets, or markets whose prices tend to move in tandem. Correlation is important, but it’s not as critical as many think. Even when one perfectly correlated market leads another, it can be misleading.
Consider the same Treasury bond series with one shifted five days backward (see "Perfect lead?"). The correlation of these two markets is not consistent and in fact even dips into negative territory at times. This is why we can’t become infatuated with high-correlated markets. Here, we have what should be the perfect lead market — the market itself — and a simple correlation measure lacks precision. What we are interested in is auto-correlation. Auto-correlation is a measure of similarity of two waveforms as a function of a time-lag applied to one of them. The success of a particular trading system can indicate sufficient auto-correlation for an intermarket relationship to be profitable.
Before we move to developing mechanical trading systems around intermarket relationships, let’s look at the basis of a simple moving average. The lag of a simple moving average is half of the size of the look-back; a 10-day moving average has a lag of five bars. This concept is important and underlies a concept called intermarket divergence. Here’s how intermarket divergence works.
For positively correlated markets:
- If the lead market is in an uptrend, and the traded market a downtrend, buy.
- If the lead market is in a downtrend, and the traded market an uptrend, sell.
For negatively correlated markets:
- If the lead market is in an uptrend, and the traded market in an uptrend, sell.
- If the lead market is in a downtrend, and the traded market is in a downtrend, buy.
Various tools can be used to define an up or downtrend. The moving average works well. One way to define a trend would be the sign of price relative to a selected moving average length. The traded market and lead market can have different length moving averages. The code for a system based on this concept is shown in "Developing in divergence" (below).
Click here to access this code as text.
As an exercise, let’s apply our model to the Treasury data set. Because we are looking for divergence, we only will make trades near market turning points based on our perfect data. When optimizing, we found the best set of parameters is 1, 1, 5 and 10. This is a 2.5-day lag for the Treasury bonds and a five-day lag for our shifted five-day forward series. Our Close-Average(Close,X,0) is close minus a closing price centered X/2 days ago.
In our fantasy world, we would make $1,884,968.75. More important, though, is that we still would lose on a few trades. The winning percent is 92%. This demonstrates that the theory is not perfect. The reasons are auto-correlation within the bond series and the changing lengths of market moves.
Unfortunately, we don’t have a perfect intermarket signal with a consistent lead. The best we can hope for is a market that leads somewhat consistently most of the time.
One example can be found with T-bonds and utility stocks based on the UTY Index.
We’ll use an eight-period moving average for T-bonds and an 18-period moving average for UTY. The eight-period moving average produces four days of lag and the 18-period moving average has nine days of lag. We are looking at a five-bar difference between the center four-bar shift for Treasuries and nine bars for UTY, so we imply a five-bar lead for Treasuries. This is effectively the shift. Results are shown in "Shift results" (below).
Utility stocks aren’t the only effective intermarket partner to Treasuries. Silver, which is negatively correlated, is another good candidate. T-bonds are a rich source of intermarket strategies, but the usefulness of this approach doesn’t stop there. Valuable relationships can be found in energies, live cattle and metals.
To examine the intermarket relationships between metals, we’ll look at the S&P/TSX Global Gold Index and the AMEX Gold Bug Index, or HUI.
The S&P/TSX Global Gold Index includes 25 precious metal mining companies traded on the Toronto Stock Exchange (TSX). It’s designed to be a dynamic international benchmark tracking the world’s leading gold companies. The intention of the index, which is calculated based on a modified market capitalization approach, is to provide an investable benchmark for publicly traded international gold companies.
The HUI is a modified equal-dollar-weighted index of companies involved in gold mining. The HUI and the Philadelphia Gold and Silver Index (XAU) are the two most watched gold indexes on the market. The HUI was designed to provide significant exposure to near-term gold price movement by including companies that do not hedge their gold production beyond 1.5 years. The HUI currently includes 15 of the largest and most widely held public gold production companies.
The vagaries of the mining business and the ascending Canadian dollar have caused the S&P/TSX Canadian Gold Index to underperform the HUI. From Feb. 19, 2001, to Aug. 16, 2011, the HUI was up 1,211.40% while the Canadian index was up only 413%. This relative underperformance creates opportunity.
With the Canadian dollar exceeding parity and probably topped out against the U.S. dollar, Canadian gold mining stocks may find greater traction compared to their American-listed counterparts.
We will use the S&P/TSX Global Gold Index and the HUI as our markets for trading the gold market. The S&P/TSX will be market one and HUI market two. We will trade the electronic gold /pit gold merged contract from Pinnacle Data. We tested both markets independently and, of course, they are correlated positively to gold. We optimized from four to 30 in two steps for both of our parameters.
The stronger of the two was the S&P/TSX Global gold index with 25 of the top 30 parameter sets (see "Two views of gold," above). Clearly, while these indexes are similar, their performance is not. Over our optimization, the average net profit for S&P/TSX is $57,525.82 vs. $44,793.52 for HUI test cases. For our best set of S&P/TSX parameters, we used a six-bar moving average for gold and a 14-day moving average for the index (see "Leading the way," below).
Intermarket analysis is a powerful tool for traders, and provides a true economic rationale for generating predictive analysis. Traders would do well to explore ways to incorporate it into their models.
Murray A. Ruggiero Jr. is the author of "Cybernetic Trading Strategies" (Wiley). E-mail him at firstname.lastname@example.org.