Many markets are interrelated. These interrelationships can offer predictive capabilities for many markets. The study of these interrelationships is called intermarket analysis. In this article I will briefly explain a robust method for generating robust signals for a wide range of markets. I will also offer a free TradeStation tool to help you explore intermarket relationships.
Standard correlations between markets are not useful if our goal is to either predict future prices or generate profitable signals because current correlation does not tell us anything about future prices. A methodology we originally developed in the mid 1990s called intermarket divergence allows us to gauge the predictive power of an intermarket relationship and produce 100% objective signals. During the past 17 years we have used this methodology to develop trading systems which have produced robust and reliable trading signals even 17 years after the models were originally developed without any re-optimization. Other methodologies of processing intermarket relationships to develop trading signals might perform as well during in-sample periods, but do not perform as well during walk forward periods and during real trading.
A widely known intermarket relationship is the one between the S&P 500 and the 30 year Treasury bond. Bond prices generally are positively correlated with the S&P 500 (while yields are negatively correlated), although this is not always true, bonds should generally lead stocks at turning points. Another important fact is that one of the best trades you can make in the S&P 500 is when 30 year Treasury bond diverge from the S&P 500; for example, when (a) bonds are rising and the S&P 500 is falling, buy the S&P 500 and (b) bonds are falling and the S&P 500 is rising, sell S&P 500. Although this relationship has broken down over the past few years, its long term existence is of historical importance to the science of intermarket analysis.
Simple But Powerful Method of Market Predictions
We will use classic mechanical methods for trading intermarket relationships, applying them to 30 year Treasury bond using a concept called “intermarket divergence,” (first coined in 1998) which is when a traded market moves in an opposite direction to what is expected. For example, if we trade the S&P 500, 30 year Treasury bond rising and the S&P 500 falling would be divergence since these are positively correlated. If we were trading the 30 year Treasury bond, both bonds and gold rising would classify as divergence since they are negatively correlated. We will define an uptrend as when prices are above a moving average and a downtrend as when they are below the moving average. Now we can predict with some reliability the future direction of bonds, stocks, gold, crude and even currencies using this simple intermarket divergence model. Pseudo code for this basic model is as follows:
Price relative to a simple moving average
Let InterInd = Close of Intermarket - Average (Close of Intermarket,N) Let MarkInd = Close Traded Market - Average (Close of Traded Market,M)
If InterInd > 0 and MarkInd < 0 then buy at next bars open If InterInd < 0 and MarkInd > 0 then sell at next bars open
If InterInd < 0 and MarkInd < 0 then at buy at next bars open If InterInd > 0 and MarkInd > 0 then sell at next bars open
This simple concept represented above has proven to be a robust methodology for predicting future price action using intermarket analysis. In 1998, I published a simple intermarket based system for trading 30 year Treasury bond futures. This model used ‘The NYSE Utility Average (NNA)’, which was a basket of Utility stocks. The NNA was discontinued in 2004. Another utility index which also worked fairly well was the Philadelphia Electrical Utility index which was used as a replacement for NNA in our research. Back in 1998, when I did the original research and article, both indexes worked similarly, but NNA had a longer price history than UTY did. The original analysis using NNA was done as follows. We used a positive correlated intermarket divergence model and a moving average of eight days for 30 year Treasury bond and 18 days for NNA. We tested over the period Jan 1, 1988 to Dec 31, 1997. We did not deduct anything for slippage and commission. My original published results were as follows:
Net profit: $111,293.00
Win %: 60%
Average trade: $883.38
Profit factor: 2.83
Now let us see how UTY worked during this same period using the original set of parameters used with NNA. This set of parameters was suboptimal for UTY but we are using the NNA set of parameters for consistency to show the robustness of our model.
Total Net Profit: $83,557.98
Total # of trades: 141
Percent Profitable: 58.87%
Avg. Trade (win & loss): $592.61
Max intraday drawdown: ($11,722.50)
Profit Factor: 2.03
Let us study just the out-of-sample period with a first trade after 01/01/1998 to 10/25/2011.
Total Net Profit: $129,166.32
Total # of trades: 257
Percent Profitable: 61.87%
Avg. Trade (win & loss): $502.59
Max intraday drawdown: ($26,133.36)
Profit Factor: 1.67
We can see these out-of-sample results are very similar to the results over the whole period and the average trade differs by less than 20% between the in-sample and out-of-sample period. Let us look at the year by year out-of-sample results (see Table I).
We have seen that intermarket divergence is a powerful concept. When an intermarket divergence occurs we stay in that position until an opposite divergence occurs. One question is, “Why does this divergence concept work?” Also, what is interesting is that my research shows that the zero crossing is significant, we cannot improve the results of intermarket divergence by using a non-zero threshold. It is my belief that this concept works as an arbitrage play. Since we do not know the relative equilibrium between the traded market and underlying market, for example in the case of Treasury bonds and UTY, divergence is the only confirmed mispricing; we have in terms of a reliable arbitrage play. We know that this cannot be the most efficient signal. We can see by studying our Treasury bond trades that some trades are early; for others we give back large percentage of open profits and sometime large winning trades can become losers, even though intermarket divergence still produces outstanding results.
Here, we have a very profitable trade but we gave back almost all of the profit and then the market moved back in the direction of the trade. This shows a problem with intermarket divergence namely “Reversal Strategy” which is always in the market. There are other cases including (a) a winning trade ending up as a losing one and (b) trades which never become profitable. Despite these issues our results are amazing. One solution to this problem is to build a finite state machine which covers all possible states of the intermarket relationship during the process of going from ‘long to short’ or ‘short to long’. My research has shown that this state map of all possibilities is the key in greatly improving the performance of these simple divergence models. We can also create a state map which will allow us to combine multiple intermarkets against a market we are trading. Correlation and forward correlations analysis between markets can also be used to filter and improve these models. Sometimes correlation analysis can make the long term out-of-sample performance less robust if it is not integrated carefully. Hence, it is important to do the surface analysis discussed earlier to make sure that the correlation relationships we are looking at are robust and stationary.
Intermarket divergence is not something which just works on the bond market. It works on a broad range of markets from bonds, to stock groups, to currencies; even markets like gold, crude, live cattle and copper.
Intermarket analysis is an exciting area of market production. New methodologies of representing these relationships will help not only classic trading system development but also using advance technologies as for example using a finite state model can allow machine learning methods to easily see patterns which can be used to build more reliable models.
Build robust and profitable systems that predict market turning points with this tool.
— by Murray Ruggiero of Using EasyLanguage.
This article was created based upon the paper, Intermarket Divergence, which was published within Computational Intelligence for Financial Engineering & Economics (CIFEr), 2012 IEEE Conference on March of 2012.