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NEURAL NETWORKS


Why an Intermarket Perspective is Needed in Technical Analysis

09/30/02 12:26:00 PM
by Louis B. Mendelsohn

With the integration of the financial markets comes a great opportunity to study and focus on intermarket analysis.

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With the mass availability of personal computer trading software, today's futures and equities traders have embraced technical analysis to assist them in making their trading decisions. At the same time, the emergence of the world economy and technological advancements in global telecommunications and information technologies have caused today's financial markets to become increasingly interconnected.

Yet traders continue to apply popular technical indicators to individual markets, with little, if any, attention paid to what is happening in related markets. In fact, many widely-used technical indicators have remained the same as they were when first introduced decades ago, despite the recent changes that have taken place in the financial markets. These single-market approaches comprise subjective charting analysis formations including head and shoulders, flags, pennants, and triangles, and objective trend-following indicators such as moving averages. Such indicators are applied to single market data in order to discern repetitive price patterns which are considered useful in trend forecasting.

The recent integration of the financial markets, both domestically and internationally, suggests that trading strategies designed around single-market approaches are too limited in scope. Now, the external effects of related markets are every bit as relevant as intramarket price dynamics in determining market trend direction. The result is a relatively new, albeit increasingly important, arena, within the overall field of technical analysis, known as intermarket analysis.

Despite awareness that today's financial markets are interconnected, too many traders admit that they still analyze each market alone, with little attention paid to what's happening in related markets. Sure, a QQQ equities trader or a Nasdaq-100 Index futures trader might keep an eye on treasury notes or crude oil by looking at price charts of these related markets, but this can hardly be considered intermarket analysis.

As the world's financial markets continue to meld together, and the distinction between futures and equities continues to blur, traders who continue to cling to single-market analysis approaches will be at a great disadvantage. Since the raison d'etre of technical analysis is to identify past market trends and price patterns in order to anticipate the likely future trend direction of a given market, the scope of technical analysis will need to broaden if these underlying intermarket linkages are to be discerned in a way that proves useful to traders.

Traders should not stop focusing on the internals of individual markets or abandon the use of trend-identifying technical indicators such as moving averages. Instead, I believe that widely-used single-market indicators need to be adapted to today's global financial markets.

Admittedly, it will be challenging for many individual traders to apply intermarket analysis beyond simply "eyeballing" charts of one or two related markets or performing simplistic "spread analysis" that compares the daily prices of two related markets. Today, due to the complexity of the financial markets, such limited attempts at intermarket analysis will fail to achieve their intended purpose.

Intermarket relationships between numerous related markets are dynamic, and have varying strengths, as well as varying leads and lags to one another that fluctuate over time. Therefore, in order to perform effective intermarket analysis, robust mathematical tools for data mining are needed. One such tool, which I have had much success with, is known as "neural networks."

Neural networks can be used to find hidden patterns and relationships within market data. Through a process called "training," neural networks can be designed to make accurate trend forecasts. For instance, in order to forecast the short term trend direction of the Nasdaq-100 Index, a neural network can be designed utilizing past market data on the Nasdaq-100 Index itself, as well as intermarket data from related markets, such as the Dow Jones Industrial Average, Treasury notes, S&P 500 Index, U.S. Dollar Index, the Bridge/CRB Index, the Dow Jones Utility Average and crude oil.

Trend forecasts made by neural networks utilizing intermarket data often capture impending changes in market direction before they show up on conventional price charts or can be identified through the use of single-market trend following indicators. One intriguing application of neural networks that I have worked with involves the use of intermarket data as inputs into neural networks trained to make short term forecasts of moving averages, thereby overcoming their "lag effect." (For more on neural networks, you can find a sampling of articles written by Louis. B. Mendelsohn -- "Preprocessing Data For Neural Networks," Vol. 11, No. 10; "Training Neural Networks," Vol. 11, No. 11; and "Using Neural Networks For Financial Forcasting," Vol. 11, No. 12 -- at http://store.traders.com/articles.html.)

This is accomplished by using forecasted moving averages within moving average crossover trading strategies. In this design, a predicted moving average, based on both single-market and intermarket data, is compared with a calculated moving average based solely on past single-market price data. The resulting comparison of the predicated to the calculated moving average highlights whether or not a market is about to change trend direction. When the predicted moving average for a future date is greater than today's calculated moving average, the market can be expected to trade higher. Similarly, when the predicted moving average is less than today's calculated moving average, the market can be expected to trade lower.

For instance, a predicted five-day moving average for two days from now can be compared to today's calculated five-day moving average. If the predicted average is greater than today's calculated average, the market is likely to move higher over the next two days. The difference in magnitude between the two moving average values from one day to the next provides insight into the strength of the expected move.

Another way that predicted moving averages can be used within crossover strategies is to compare one predicted moving average to another. For example, a predicted five-day moving average for two days from now can be compared to a predicted ten-day moving average for four days from now. If the short predicted average is greater than the long predicted average, the market is likely to move higher in the near-term. Such crossover strategies are useful in determining where and when to enter or exit a trade, as well as where to set trailing stops.

Obviously, trend identification and market forecasting will never be 100% accurate, given the randomness and unpredictability of unforeseen events that affect the financial markets. Therefore, technical analysis will always be a combination of art and science. Still, as the financial markets become increasingly interconnected, it is important that technical analysis broaden its scope so that innovative technical approaches such as intermarket analysis assume their rightful place, alongside that of single-market analysis, in traders' toolboxes.



Louis B. Mendelsohn

Louis Mendelsohn is president and chief executive officer of Market Technologies Corporation. His recent book entitled Trend Forecasting with Technical Analysis: Unleashing the Hidden Power of Intermarket Analysis to Beat the Market is available for purchase at Market Technologies Corporation's website, www.ProfitTaker.com.

Title: President and CEO
Company: Market Technologies Corporation
Phone # for sales: 813-973-0496
Website: www.ProfitTaker.com
E-mail address: LouM@ProfitTaker.com

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