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By Darrell Jobman
Traders may have different views about the prospects for the stock market or the price outlook for a commodity, but most would agree with the premise of intermarket analysis:
Other markets influence a target market, and what happens in one market influences what happens in other markets.
Those statements may seem too obvious to point out. Intuitively and instinctively, most traders realize that markets are interrelated to some degree.
That's not a new revelation, of course, nor will it open the gates to the Holy Grail trading system. But it's taken a little twist in recent years with the "all-one-market" theme and the "risk on/risk off" approach that seems to pervade market thinking. (Actually, there is always risk in a market position - being in when you shouldn't, not being in when you should - so the "risk off" label is a little misleading.)
The premise, in short, is that when traders want to take on risk, they will turn to markets like commodities, natural resources, emerging markets and stocks in those areas where returns can be lucrative but far from certain. When they want to take off risk, they turn to cash, Treasuries, blue chip stocks or other "safe" investments in the "sure things".
Trader attitudes and perception are playing an important role in the current cycle where various classes of assets seem to be moving up or moving down in unison, emphasizing the value of including intermarket analysis in studying the prospects for an individual market.
In the 1980s, analysts such as Lou Mendelsohn, developer of VantagePoint, a software program based on intermarket analysis first released in 1991 ([You must be registered and logged in to see this link.] and John Murphy, author of the definitive book on intermarket analysis and founder of StockCharts.com , recognized the increasing importance of the global nature of markets and began to focus on quantifying the effect of intermarket analysis on individual markets.
They did not see intermarket analysis as a new approach but as an extension of technical analysis that could provide some predictive capabilities. Traders still have to pay close attention to what an individual chart and its price patterns are suggesting but also need to be aware of the influence of other markets. In Mendelsohn's view, market analysis includes fundamentals, traditional chart analysis and intermarket analysis in what he calls "synergistic market analysis".
Incorporating aspects of intermarket analysis into an analytical routine can be done in several ways:
Eyeball method. This is simple, basic Intermarket Analysis 101. Put charts from two related markets side by side and note how closely the price patterns and/or technical indicators of the two markets follow each other. This provides a rather rudimentary view of intermarket analysis but is better than not considering the impact of related markets at all.
Spread relationships. To reduce their risk exposure, commercials and other professional traders frequently use spreads to capture profits from changes in the relationship between two markets or two time periods. In the forex markets, every trade is a spread - buy one currency and sell another - making intermarket analysis an essential element in deciding which currency is the stronger, based on a number of factors.
Traders in other markets do the same type of intermarket analysis when they determine whether one market might outperform another. In a risk-on trending scenario, for example, they may decide to mitigate - but certainly not eliminate - risk by buying Microsoft and selling the Nasdaq index. Or vice versa if their analysis suggests Microsoft is the weak sister in the technology sector.
Agricultural producers also have to do their intermarket analysis studies to decide what to grow. For example, a soybean price about 2.2 or 2.3 times the price of corn has long been considered a balancing point for deciding which of the two crops to plant, considering production costs and yields. If the ratio is much lower than that, go with corn and its higher return; if the ratio is much higher, plant soybeans.
That's essential intermarket analysis that traders can also apply in trading relationships that get out of line. Similar analysis can also be used to trade different futures months based on seasonal patterns - buy July and sell November soybean futures if supplies of the old-crop July are tight and the supply of new-crop November looks like it may be large.
Correlation studies. Studying how closely the movement in one market tracks the movement in another is another aspect of intermarket analysis. If the two markets move in the same direction, they are said to be positively correlated; if one moves up and another down during the same time period, they are negatively correlated. Think of the price of gold vs. the value of the U.S. dollar as a historically negative correlation.
To be sure, there is no assurance that one market will always match what another one does. However, it is useful to know that, historically, at least, movements in the past between the two markets have resulted in intermarket relationships that might be expected to continue in the future.
Understanding the correlation between markets is also helpful in asset allocation and setting up diversified portfolios of markets that can produce a desired return. A trader trying to offset the risk of a portfolio heavy in stocks, for example, might include a negatively correlated commodities index to offset losses in case the stocks plunge - unless, of course, all asset classes move in the same direction as may occur in the risk on/risk off situations.
A few limitations
Eyeballing charts, spread relationships and correlation studies are all helpful in intermarket analysis, but these traditional approaches do have some limitations:
They are based only on one-to-one relationships when, in reality, a target market is influenced by a number of related markets. It is important to know the status of "a market" in the context of "the markets."
They do not account for the leads and lags that may exist between the various markets. Intermarket linkages between markets are not fixed in time or in scope of their response to one another. These links are not static but are dynamic, with different strengths and, more than likely, different leads and lags that will shift over time.
Perhaps most important, what is "normal" in the relationship between any two markets? In fact, what is "normal" for the price of any market? Traders can use historical data to extrapolate what might happen in the future, but that's about the best that can be expected.
From an intermarket analysis perspective, keep in mind that conditions change and markets adjust. There may be a time when central banks aren't competing to see who can print the most money the fastest. There may be a time when governments aren't running up billions and trillions of debt. There may be a time when asset classes go their "normal" separate ways.
The evidence suggests this is not that time. Meanwhile, keep up with the markets that most influence the markets you are trading and perhaps even use neural networks to analyze these relationships to make trading decisions, as Mendelsohn does.
Traders may have different views about the prospects for the stock market or the price outlook for a commodity, but most would agree with the premise of intermarket analysis:
Other markets influence a target market, and what happens in one market influences what happens in other markets.
Those statements may seem too obvious to point out. Intuitively and instinctively, most traders realize that markets are interrelated to some degree.
That's not a new revelation, of course, nor will it open the gates to the Holy Grail trading system. But it's taken a little twist in recent years with the "all-one-market" theme and the "risk on/risk off" approach that seems to pervade market thinking. (Actually, there is always risk in a market position - being in when you shouldn't, not being in when you should - so the "risk off" label is a little misleading.)
The premise, in short, is that when traders want to take on risk, they will turn to markets like commodities, natural resources, emerging markets and stocks in those areas where returns can be lucrative but far from certain. When they want to take off risk, they turn to cash, Treasuries, blue chip stocks or other "safe" investments in the "sure things".
Trader attitudes and perception are playing an important role in the current cycle where various classes of assets seem to be moving up or moving down in unison, emphasizing the value of including intermarket analysis in studying the prospects for an individual market.
In the 1980s, analysts such as Lou Mendelsohn, developer of VantagePoint, a software program based on intermarket analysis first released in 1991 ([You must be registered and logged in to see this link.] and John Murphy, author of the definitive book on intermarket analysis and founder of StockCharts.com , recognized the increasing importance of the global nature of markets and began to focus on quantifying the effect of intermarket analysis on individual markets.
They did not see intermarket analysis as a new approach but as an extension of technical analysis that could provide some predictive capabilities. Traders still have to pay close attention to what an individual chart and its price patterns are suggesting but also need to be aware of the influence of other markets. In Mendelsohn's view, market analysis includes fundamentals, traditional chart analysis and intermarket analysis in what he calls "synergistic market analysis".
Incorporating aspects of intermarket analysis into an analytical routine can be done in several ways:
Eyeball method. This is simple, basic Intermarket Analysis 101. Put charts from two related markets side by side and note how closely the price patterns and/or technical indicators of the two markets follow each other. This provides a rather rudimentary view of intermarket analysis but is better than not considering the impact of related markets at all.
Spread relationships. To reduce their risk exposure, commercials and other professional traders frequently use spreads to capture profits from changes in the relationship between two markets or two time periods. In the forex markets, every trade is a spread - buy one currency and sell another - making intermarket analysis an essential element in deciding which currency is the stronger, based on a number of factors.
Traders in other markets do the same type of intermarket analysis when they determine whether one market might outperform another. In a risk-on trending scenario, for example, they may decide to mitigate - but certainly not eliminate - risk by buying Microsoft and selling the Nasdaq index. Or vice versa if their analysis suggests Microsoft is the weak sister in the technology sector.
Agricultural producers also have to do their intermarket analysis studies to decide what to grow. For example, a soybean price about 2.2 or 2.3 times the price of corn has long been considered a balancing point for deciding which of the two crops to plant, considering production costs and yields. If the ratio is much lower than that, go with corn and its higher return; if the ratio is much higher, plant soybeans.
That's essential intermarket analysis that traders can also apply in trading relationships that get out of line. Similar analysis can also be used to trade different futures months based on seasonal patterns - buy July and sell November soybean futures if supplies of the old-crop July are tight and the supply of new-crop November looks like it may be large.
Correlation studies. Studying how closely the movement in one market tracks the movement in another is another aspect of intermarket analysis. If the two markets move in the same direction, they are said to be positively correlated; if one moves up and another down during the same time period, they are negatively correlated. Think of the price of gold vs. the value of the U.S. dollar as a historically negative correlation.
To be sure, there is no assurance that one market will always match what another one does. However, it is useful to know that, historically, at least, movements in the past between the two markets have resulted in intermarket relationships that might be expected to continue in the future.
Understanding the correlation between markets is also helpful in asset allocation and setting up diversified portfolios of markets that can produce a desired return. A trader trying to offset the risk of a portfolio heavy in stocks, for example, might include a negatively correlated commodities index to offset losses in case the stocks plunge - unless, of course, all asset classes move in the same direction as may occur in the risk on/risk off situations.
A few limitations
Eyeballing charts, spread relationships and correlation studies are all helpful in intermarket analysis, but these traditional approaches do have some limitations:
They are based only on one-to-one relationships when, in reality, a target market is influenced by a number of related markets. It is important to know the status of "a market" in the context of "the markets."
They do not account for the leads and lags that may exist between the various markets. Intermarket linkages between markets are not fixed in time or in scope of their response to one another. These links are not static but are dynamic, with different strengths and, more than likely, different leads and lags that will shift over time.
Perhaps most important, what is "normal" in the relationship between any two markets? In fact, what is "normal" for the price of any market? Traders can use historical data to extrapolate what might happen in the future, but that's about the best that can be expected.
From an intermarket analysis perspective, keep in mind that conditions change and markets adjust. There may be a time when central banks aren't competing to see who can print the most money the fastest. There may be a time when governments aren't running up billions and trillions of debt. There may be a time when asset classes go their "normal" separate ways.
The evidence suggests this is not that time. Meanwhile, keep up with the markets that most influence the markets you are trading and perhaps even use neural networks to analyze these relationships to make trading decisions, as Mendelsohn does.
