Broadening the Scope of Technical Analysis

The Importance of an Intermarket Perspective

By Louis B. Mendelsohn

With the proliferation of microcomputers and trading software over the past two decades, there has been a surge of interest by futures and equities traders in applying technical analysis to their trading decisions. Concurrently, a transformation has been underway, due in part to advancements in global telecommunications and information technologies, in which the financial markets have become increasingly globally interconnected and interdependent.

Despite the globalization of the financial markets, technical analysis is still directed primarily at analyzing each individual market by itself. This is done utilizing various technical indicators, many of which have undergone little, if any, change in their construction since first being applied by technicians decades ago. These include subjective charting analysis techniques such as head and shoulders, flags, pennants, and triangles, which attempt to find repetitive patterns in single-market data thought to be useful for market forecasting, and objective trend-following indicators such as moving averages, which due to their mathematical construction tend to lag behind market action.

These recent structural changes in the financial markets call into question the efficacy of trading strategies that rely solely upon single-market technical analysis methods and indicators to examine the price movements of individual markets. Now it is imperative to take external effects of related markets into consideration as well. This realization has brought about the emergence of an approach to market analysis, called intermarket analysis, which I have been involved in developing since the mid-1980s.

While most traders today will readily acknowledge that the world’s financial markets have become interconnected and influence each other, these same traders will just as quickly admit that they still do not perform intermarket analysis. Instead, they continue to focus on one market at a time, while paying scant attention, if any, to what’s occurring in related markets. For instance, a QQQ equities trader or a Nasdaq-100 Index futures trader might keep a cursory eye on one or two related markets, such as Treasury bonds, the Nasdaq Composite, and maybe even crude oil. Typically, this is done by glancing over at price charts of these related markets. Intermarket analysis conducted at this rudimentary level is not amenable to rigorous evaluation or historical testing. While better than not performing intermarket analysis at all, this minimal effort still limits traders’ perceptions of what is really happening – and more importantly what is about to happen – in the markets that they are trading. No wonder there is a revolving door of traders who dabble with technical analysis for a while, only to fail at trading.

As the financial markets become increasingly more complicated over the next few years, with the ongoing melding of futures and equities, both domestically and internationally, and the inauguration of futures contracts on individual stocks, traders who continue to restrict their analysis to a single market’s past prices (or rely solely upon subjective chart pattern analysis or trend-following lagging indicators) for clues regarding an individual market’s future trend direction, will find themselves at a severe disadvantage. Since the stated purpose of technical analysis is to identify market trends and forecast (or at least extrapolate) their likely future direction, it stands to reason that traders could more easily attain this goal through the use of leading indicators that quantify the simultaneous linkages between markets and their effects on the traded market.

Today’s financial markets are an intensely competitive arena, and as in the case of futures markets are a zero-sum game. In this battlefield-like environment predictive intermarket analysis tools – that expand the scope of analysis beyond that of a single-market – demand serious attention by technical analysts and traders. I am not suggesting, however, that traders should quit performing single-market analysis altogether or abandon the use of popular technical indicators such as moving averages which have been the mainstay of technical analysis for decades.

Many widely-used single-market indicators can be adapted to today’s global markets. There is no need to throw the baby out with the bath water. Intermarket analysis is not the elusive Holy Grail of technical analysis. The Holy Grail does not exist. Intermarket analysis is simply another facet of technical analysis, and should be implemented in conjunction with single-market analysis. In this way, marginal trades, which are otherwise indiscernible using only a single-market approach, can be identified and thereby avoided, while potentially outstanding trades can be seized upon early in their formation, with greater confidence.

Intermarket analysis brings an added dimension to the analytical framework so that the behavior of each individual market can be examined from without as well as from within. Intermarket analysis is a natural extension of single-market analysis, thereby broadening the definition of technical analysis. This evolution is necessary, given the complexity of today’s global financial markets.

Yet, it is quite challenging for individual traders to perform intermarket analysis beyond simply “eyeballing” the charts of two or three related markets. Relatively simple quantitative methods have been developed by technical analysts in the past to measure the effects of related markets on a given market. One such approach, widely used by futures traders, performs a “spread analysis” on two markets to measure the degree to which their prices move in relation to one another. This is accomplished by calculating and comparing the ratio of or difference between the prices over time.

As the number of related markets to be taken into consideration increases, the ineffectiveness of such approaches to analyzing intermarket relationships for trend indentification and market forecasting becomes apparent. These methods are limited to price comparisons of only two markets at a time, assume that the effects of one market on another occur without any leads or lags, and that the relationships are linear.

These assumptions are not borne out by how today’s global financial markets actually behave. Intermarket linkages between markets are neither fixed nor linear. They are dynamic, and have varying strengths, as well as varying leads and lags to one another that shift over time. Therefore, in order to perform intermarket analysis effectively, more robust mathematical tools need to be employed. One such tool, which I have worked with for more than a decade and found to be very effective, is neural networks.

Neural networks can find reoccurring patterns and relationships within both intra-market and inter-market data. Through a process known as “training”, neural networks can be designed to make highly accurate market forecasts of the trend direction of various financial markets. For instance, in order to forecast the short term trend direction of the Nasdaq-100 Index, neural networks can be trained on past market data on the Nasdaq-100 Index itself (including open, high, low, close, volume and open interest), in addition to inter-market data from any number of related markets. These related markets might include the Dow Jones Industrial Average, 30-year Treasury bonds, S&P 500 Index, U.S. Dollar Index, S&P 100, New York Stock Exchange Composite Index, Bridge/CRB Index, Dow Jones Utility Average, and New York light crude oil.

The trend forecasts made by neural networks through their pattern recognition capabilities often forewarn of impending changes in market direction before they would even show up on conventional price charts or could be identified through the use of single-market trend following indicators. One innovative way in which I have been able to amalgamate intermarket analysis with single-market analysis has been to use both intra-market and inter-market data as inputs into neural networks which are then trained to make forecasts of moving averages.

Moving averages have long been recognized by traders and technical analysts as an important quantitative trend identification tool. While the “lag effect” inherent in the mathematical construction of moving averages has continued to challenge technical analysts and market researchers, moving averages are still extensively relied upon by technicians to gauge current market behavior and discern future market direction. If this shortcoming could be eliminated, moving averages could rank as perhaps the most effective trend forecasting tool in the technical analyst’s arsenal.

For traders it is critical to know what the market direction is expected to be in the immediate future, since profitable trading decisions are predicated on these expectations being correct more often than not. Unlike yesteryear, it is no longer good enough to find out that a market made a top or a bottom two or three days ago. In today’s highly volatile markets even a one day lag can be the difference between profits and losses.

By contrast, if it were possible to forecast accurately, for example, a five-day simple moving average of closes for two days in the future, then the lag effect would be eliminated from a practical trading standpoint. Changes in the trend direction of a market could be identified just before or at the time of their occurrence, not days after the fact.

One approach that I have found highly successful incorporates forecasted moving averages into predictive moving average crossover trading strategies. In this design, the value of a predicted moving average, based upon both intra-market and inter-market data inputs into the training of neural networks, is compared mathematically with the value of a calculated moving average which is based strictly on past single-market prices. The resulting metric indicates the expected market trend direction. When the predicted moving average value for a future date is greater than today’s calculated moving average value, the market can be expected to move higher over that time frame. Similarly, when the predicted moving average value for a future date is less than today’s calculated moving average value, the market is likely to move lower over that time frame.

For instance, a predicted five-day simple moving average for two days from today can be compared to today’s calculated five-day simple moving average. If the predicted average is greater than today’s calculated average, this indicates that the market is likely to move higher over the next two days. The difference between the two moving average values from one day to the next measures the strength of the anticipated move.

Another intriguing application of predicted moving averages is to compare one to another. For example, a predicted five-day moving average for two days from today can be compared to a predicted ten-day moving average for four days from today. When the shorter predicted average is above the longer predicted average (and both are above their respective calculated averages), this is a strong indication of near-term upward movement.

Predictive moving average crossover strategies can be devised to indicate when to enter and exit a position, where to place market or limit orders, and at what price to set trailing stops. Further research on various lengths and types of forecasted moving averages, as well as on the application of optimization techniques to forecasted moving averages, should be conducted.

As the world’s financial markets become increasingly intertwined, intermarket analysis will play a more critical role within the overall field of technical analysis in the 21st century, just as back-testing and optimization of single-market trading strategies became integral to computerized technical analysis in the late 20th century. In fact, I can envision the definition of technical analysis broadening even further, in which technical, intermarket and yes, even fundamental data are brought together within one unified (quantitative) analytical framework.

I have previously referred to this paradigm as “synergistic market analysis”. This concept expands upon an earlier article of mine published in the September, 1991 issue of the MTA Newsletter entitled “It’s Time to Rethink the Role of Technical Analyst”. Given the unprecedented transformation of the global financial markets presently underway, open-mindedness on the part of technical analysts toward accepting a re-definition of technical analysis along these lines is warranted.

Obviously, trend identification and market forecasting will never achieve 100% accuracy, due to randomness and unpredictable events that are inherent in the financial markets, as well as due to the daunting task of developing powerful market forecasting tools. Technical analysis is as much art as science, if not more. Still, it is our responsibility as technical analysts to push the quantitative envelope of financial market analysis as far as possible. That’s what makes this arena so intellectually challenging to those of us who are fortunate enough to be involved professionally in technical analysis.


Louis Mendelsohn is president and chief executive officer of Market Technologies, and has been a full member of the Market Technicians Association since 1988. His recent book entitled Trend Forecasting with Technical Analysis: Unleashing the Hidden Power of Intermarket Analysis to Beat the Market is available through the MTA library or for purchase at Market Technologies’ website, Mr. Mendelsohn can be reached by e-mail at or by phone at 813-973-0496.