<s> To my wife Erifili
Intermarket Trading Strategies
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INTERMARKET TRADING STRATEGIES
Markos Katsanos
A John Wiley and Sons, Ltd., Publication
C 2008 Markos KatsanosCopyright 
Published by
John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester,West Sussex PO19 8SQ, EnglandTelephone (+44) 1243 779777
Email (for orders and customer service enquiries): cs-books@wiley.co.ukVisit our Home Page on www.wiley.comAll Rights Reserved. No part of this publication may be reproduced, stored in a retrieval system ortransmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning orotherwise, except under the terms of the Copyright, Designs and Patents Act 1988 or under the terms of alicence issued by the Copyright Licensing Agency Ltd, Saffron House, 6–10 Kirby Street, London,ECIN 8TS, UK, without the permission in writing of the Publisher. Requests to the Publisher should beaddressed to the Permissions Department, John Wiley & Sons Ltd, The Atrium, Southern Gate,Chichester, West Sussex PO19 8SQ, England, or emailed to permreq@wiley.co.uk, or faxed to(+44) 1243 770620.Designations used by companies to distinguish their products are often claimed as trademarks. All brandnames and product names used in this book are trade names, service marks, trademarks or registeredtrademarks of their respective owners. The Publisher is not associated with any product or vendormentioned in this book.This publication is designed to provide accurate and authoritative information in regard to the subjectmatter covered. It is sold on the understanding that the Publisher is not engaged in rendering professionalservices. If professional advice or other expert assistance is required, the services of a competentprofessional should be sought.Other Wiley Editorial OfficesJohn Wiley & Sons Inc., 111 River Street, Hoboken, NJ 07030, USAJossey-Bass, 989 Market Street, San Francisco, CA 94103-1741, USAWiley-VCH Verlag GmbH, Boschstr. 12, D-69469 Weinheim, GermanyJohn Wiley & Sons Australia Ltd, 42 McDougall Street, Milton, Queensland 4064, AustraliaJohn Wiley & Sons (Asia) Pte Ltd, 2 Clementi Loop #02-01, Jin Xing Distripark, Singapore 129809John Wiley & Sons Canada Ltd, 6045 Freemont Blvd, Mississauga, ONT, L5R 4J3, CanadaWiley also publishes its books in a variety of electronic formats. Some content that appears in print maynot be available in electronic books.Library of Congress Cataloging-in-Publication DataKatsanos, Markos.Intermarket trading strategies / Markos Katsanos.p. cm.Includes bibliographical references and index.ISBN 978-0-470-75810-61. Investment analysis. 2. Portfolio management. I. Title.HG4529.K376 2008332.64—dc222008040290British Library Cataloguing in Publication DataA catalogue record for this book is available from the British LibraryISBN 978-0-470-75810-6 (HB)Typeset in 10/12pt Times by Aptara Inc., New Delhi, IndiaPrinted and bound in Great Britain by TJ International Ltd, Padstow, Cornwall, UK
Contents
Acknowledgments
ix
Introduction
xi
PART I
1
1 Intermarket Analysis
3
2 Correlation
17
3 Regression
33
4 International Indices and Commodities
41
5 The S&P 500
69
6 European Indices
85
7 Gold
93
8 Intraday Correlations
111
9 Intermarket Indicators
121
PART II
145
10 Trading System Design
147
11 A Comparison of Fourteen Technical Systems for Trading Gold
175
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Contents
12 Trading the S&P 500 ETF and the e-mini
189
13 Trading DAX Futures
201
14 A Comparison of a Neural Network and a Conventional Systemfor Trading FTSE Futures
215
15 The Use of Intermarket Systems in Trading Stocks
235
16 A Relative Strength Asset Allocation Trading System
247
17 Forex Trading Using Intermarket Analysis
261
18 Conclusion
293
Appendix A MetaStock Code and Test Specifications
297
Appendix B Neural Network Systems
357
Appendix C Rectangles
367
Glossary
371
Bibliography
385
Index
391
Acknowledgments
I am particularly indebted to Dr Evangelos Falaris, Professor of Economics at theUniversity of Delaware, who took the time to review selected chapters and offeredboth technical and expositional comments.Many thanks also to Murray Ruggiero, one of the world’s leading experts inintermarket analysis, who contributed to improving the final version of the book bymaking valuable comments and suggestions on a preliminary manuscript. Murray isVice President of Research and Development of TradersStudio, contributing editorfor Futures Magazine and author of several books on trading systems.Special thanks to my wife, Erifili, for her love and patient support, and for puttingup with late nights, despite having no actual interest in the subject.I also want to thank Jayanthi Gopalakrishnan, the editor of Technical Analysis ofStocks & Commodities magazine, for encouraging me to write the articles for themagazine and helping me later with the task of finding the best publisher for thebook. Many thanks also to Caitlin Cornish, my editor at John Wiley & Sons, and toher assistant Aimée Dibbens, for their guidance and support during the publicationprocess.It is difficult to appreciate the effort and hours that go into researching and writing abook of this nature until you’ve had a chance to work on one yourself. The correlationstudies involved a huge amount of historical data and I would like to thank Peter Dayfor helping out with the VBA to manage the data in Excel and Rubi Cabal, marketingdirector at CSI data, for allowing me access to CSI’s correlation reports.Finally, I would like to thank the technical support staff at Ward Systems Inc., andalso Lynn Dufrenne, Lead Support Representative at Equis Technical Support fortheir help in solving the unavoidable software problems.
Introduction
With the emergence of the internet and international cross border trading, the world’sfutures and equity markets started to converge, making intermarket analysis anessential constituent of technical analysis.Single market technical analysis indicators were designed in the 1980s for national markets, and are no longer sufficient nor can be relied upon for analyzing theconstantly changing market dynamics.But how do markets interact and influence each other and how can we use intermarket relationships to construct a viable technical system?Because the answers have been so elusive, they became the motivation for myresearch. The more I looked into it, the more I became convinced that there is clear andincontrovertible evidence that the markets are linked to each other, and incorporatingintermarket correlations into a trading discipline can give a trading advantage. Twoyears of research later, I finally came up with some rules and mathematical formulaefor intermarket trading.It has been more than 15 years since John Murphy, a pioneer on the subject, wrotehis first book on intermarket analysis. The material in my book is based on originalresearch not published anywhere else and, unlike Murphy’s intuitive chart-basedapproach, I am going to use mathematical and statistical principles to develop anddesign intermarket trading systems appropriate for long- and short-term and even daytrading.Although the book makes extensive use of market statistics obtained from hundredsof correlation studies, the data and empirical findings are not its heart. They serveas a background in developing the trading systems presented in the second part ofthe book, as well as help shape our thinking about the way the financial marketswork.The key difference between Intermarket Trading Strategies and other books ontrading lies in its philosophy. I believe that knowing how the markets work is, in theend, more important than relying on a “black box” mechanical system that producedprofitable trades in the past but not even the creator of the system can fully explain
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Introduction
why. The focus of this book is how intermarket analysis can be used to forecast futureequity and index price movements by introducing custom indicators and intermarketbased systems. A total of 29 conventional and five neural network trading systemsare provided to trade gold, the S&P ETF (SPY), S&P e-mini futures, DAX and FTSEfutures, gold and oil stocks, commodities, sector and international ETF, and finallythe yen and the euro.Naturally, past results are no guarantee of future performance.Even so, the results of out-of-sample back-testing are compelling enough to meritattention. Some results are more compelling. The multiple regression gold system,presented in Chapter 11, returned an amazing $1.2 million of profits on a $100 000initial equity. The stock index trading systems also produced impressive profits. Theprofitability of the Standard & Poor’s e-mini intraday system was neither standardnor poor, producing a 300 % profit during the test duration. Investors who prefer totrade only stocks will find intermarket systems for trading oil and gold stocks inChapter 15. My favorite is the oil stock system which made more than $1.6 millionon an initial equity of only $100 000.The foreign exchange (or Forex) market, which until recently was dominated bylarge international banks, is gaining popularity among active traders, because of itssuperior liquidity and 24-hour trading. Readers who are interested in forex tradingwill find, in Chapter 17, an intermarket system for trading the yen which made over$70 000 on an initial $3000 account. The EUR/USD is the most popular currencypair among forex traders and the chapter on forex wouldn’t be complete without asystem for trading the euro. The next section in Chapter 17 presents two systems:a conventional and a hybrid system. The latter is an excellent example of how youcan enhance a classic system by adding intermarket conditions. The hybrid systemimproved considerably on the profitability of the traditional trend-following system,almost doubling the profit factor while reducing drawdown.The system design is fully described from the initial concept to optimization andactual implementation. All systems are back-tested using out-of-sample data and theperformance statistics are provided for each one.The MetaStock code for all systems is provided in Appendix A and a detailedprocedure for recreating the artificial neural network systems in NeuroShell Traderis included in Appendix B.The benefits of diversification, and an example of static portfolio diversification byoptimizing the portfolio allocation based on the desired risk and return characteristics,are discussed in the first chapter and a dynamic portfolio allocation method, basedon market timing and relative strength, is included in Chapter 16.The book is divided into two parts. Part I serves as a background to Part II andincludes an overview of the basics of intermarket analysis, correlation analysis inChapter 2 and custom intermarket indicators in Chapter 9.Part II uses many of the concepts presented in Part I to develop custom tradingsystems to trade popular markets like US and European stock index futures, forexand commodities.
Introduction
xiii
The chapters in this book are divided as follows.
r The book begins with a discussion of the basic principles of intermarket analysisand the benefits of portfolio diversification by including uncorrelated assets suchas commodities and foreign currencies.r Chapter 2 explains the concept of correlation and the basic assumptions used.r Chapter 3 explains the linear regression method used for predicting one securitybased on its correlation with related markets. An example using the S&P, the VIXand the Euro Stoxx is also included. Nonparametric regression (involving nonlinearrelationships) is also briefly discussed.r Chapter 4 is an overview of major international financial, commodity and equityindices including the DAX, the CAC 40, the FTSE, the Euro Stoxx 50, the Nikkei225, the Hang Seng, the dollar index, the oil index, the CRB index, the GoldmanSachs Commodity Index, the XAU, the HUI, and the Volatility Index (VIX).r Chapter 5 examines the correlation between the S&P 500 and major US andinternational equity, commodity and other indices.r Chapter 6 examines long-term, short-term and even intraday correlations betweenmajor European indices and stock index futures and explains how they can beapplied to develop trading systems.r Chapter 7 is about gold and its correlation with other commodities, indices andforeign exchange rates with special emphasis on leading/lagging analysis.r In Chapter 8 of this book I examine intraday correlations, especially useful forshort-term traders. A leading/lagging correlation analysis between the S&P e-mini,the Dow e-mini, DAX and Euro Stoxx futures is also included.r Chapter 9 presents various custom intermarket indicators and explains how eachone can be used within the framework of a trading system. These include eightnew custom intermarket indicators, published for the first time in this book. TheMetaStock code for all indicators is included in Appendix A.r The personal computer has revolutionized the creation and testing of trading systems. The inexpensive software takes out much of the work needed for testing,allowing the trader to test endless permutations of rules and parameters. In Chapter10, I present some techniques for developing a trading system and evaluating the testresults. I suggest methods of avoiding curve fitting and the illusion of excellencecreated by optimization. I also discuss stop-loss and other money managementtechniques. Finally, a brief introduction to neural network systems will explain thebasic principles of this alternative approach for designing trading systems.r Chapter 11 compares the performance of 15 intermarket systems for trading goldbased on the intermarket indicators in Chapter 9 and using gold’s correlations withthe dollar index, the XAU and silver.r Chapter 12 describes a long-term market timing system for trading the S&P ETF(SPY) using a market breadth indicator, and for traders who prefer the emotionallycharged world of short-term trading, a hybrid system for trading the S&P e-miniusing 5-minute intraday correlation with Euro Stoxx 50 futures.
xiv
Introduction
r Chapter 13 presents intermarket systems for trading DAX futures based on itscorrelation with Euro Stoxx and S&P 500 futures.
r Artificial neural network systems are gaining popularity but are they the panaceathat software vendors claim them to be? A comparison between a conventional rule-based system and a neural network strategy for trading the FTSE inChapter 14 will shed some light on this innovative trading method, exposing theadvantages and disadvantages of using neural network-based systems in predictingfuture price movements using intermarket relationships.r Oil and gold have been so far (through June 2008) two of the best performingcommodities in 2007–2008. For the benefit of readers who prefer to trade onlystocks, Chapter 15 presents examples of intermarket trading systems for trading oiland gold stocks, using their correlation with the corresponding sector indices.r Chapter 16 presents a dynamic asset allocation strategy by trading across differentasset classes according to their relative strength.r Forex trading is also gaining popularity and the final chapter (which is, by theway, the longest chapter of the book) is all about forex. Chapter 17 is dividedinto four parts. The first part discusses fundamental and technical factors affectingthe foreign exchange markets and intermarket correlations between currencies,commodities, interest rates and equities, with a brief explanation of the carry trade.The correlation analysis included in the first part is used to develop an intermarketsystem to trade the yen and a hybrid system for trading the euro. The Australiandollar has received considerable attention lately as it is the favorite currency of“yen carry” traders. The last part of this chapter contains fundamental factorsaffecting the Australian dollar and its correlation with major international indices,commodities and other currencies.The systems presented in this book are by no means exhaustive, and they are limitedby space and the author’s imagination and experience. They are presented as achallenge to the serious trader to reevaluate intermarket techniques as a working tooland to introduce a testing framework which amalgamates both intermarket and classictechnical analysis indicators.This book does not require any mathematical skills beyond those taught in highschool algebra. Statistical and correlation analysis is used throughout the book butall of the theoretical tools are introduced and explained within. All other concepts(such as indicators, trading system evaluation, neural networks, etc.) are motivated,defined, and explained as they appear in the book.Although all systems (except neural networks) are written in the MetaStock formulalanguage it is not necessary to purchase MetaStock in order to use them, as they arefully explained in English and the MetaStock functions used are explained in theGlossary.Not long ago, I ran into an acquaintance who asked me why I need to write a bookabout trading systems if I can make more money by trading in a day than by theroyalties from the book in a year. I don’t think that it makes a lot of sense to write a
Introduction
xv
book for money and I don’t know any author who became rich from writing (unlessthe book is a huge success). There are, of course, other valid reasons that motivatepeople to write books, like impressing your friends or establishing yourself as anexpert in the subject or even visibility and media coverage. Although I have to admitthat all these have crossed my mind, my main reason was the motivation to researchthe highly complex subject of intermarket relationships and write down my findingsin an orderly, methodical and timely manner (which I wouldn’t have done had I nothad the deadlines imposed by my contract with the publisher).I hope that readers will find the information useful and exploit my intermarketindicators and strategies in order to detect profitable trading opportunities.
Part I
1Intermarket Analysis
It’s not that I am so smart; it’s just that I stay with the problems longer.–Albert Einstein
The basic premise of intermarket analysis is that there is both a cause and effect tothe movement of money from one area to another. Consider, for example, the priceof gold and the dollar. Because gold is denominated in US dollars, any significantfluctuation of the dollar will have an impact on the price of gold, which in turn willaffect the price of gold mining stocks.The strength and direction of the relationship between two markets is measured bythe correlation coefficient which reflects the simultaneous change in value of a pairof numeric series over time.Highly positively correlated markets can be expected to move in similar waysand highly negatively correlated markets are likely to move in opposite directions.Knowing which markets are positively or negatively correlated with a given marketis very important for gaining an understanding of the future directional movement ofthe market you propose to trade.Advancements in telecommunications have contributed to the integration of international markets. Sophisticated traders are starting to incorporate intermarket analysisin their trading decisions through a variety of means ranging from simple chart analysis to correlation analysis. Yet the intermarket relationships hidden in this data areoften quite complex and not readily apparent, while the scope of analysis is virtuallyunlimited.But what is intermarket analysis?The financial markets comprise of more than 500 000 securities, derivatives, currencies, bonds, and other financial instruments – the size of a small city. All interactwith each other to some extent and a seemingly unimportant event can cause a chainof reactions causing a landslide of large-scale changes to the financial markets.
4
Intermarket Trading Strategies
Consider the following example: Let’s suppose that the Bank of Japan decides tobuy dollars in order to push the yen down. As a result Japanese stock prices will goup as a weak yen will help boost profits for exporters. A sharp rise of the Nikkei willin turn have a positive effect on all other Asian markets. The next morning Europeanmarkets, in view of higher Asian markets and in the absence of other overnightnews, will open higher. This will in turn drive US index futures higher and boost USmarkets at open. In addition lower yen prices will encourage the “yen carry trade”,i.e. borrowing yen at lower or near zero interest rates and buying higher yieldingassets such as US bonds or even emerging market equities, which in turn will pushbonds and equities higher. On the other hand, a scenario for disaster will develop ifthe opposite happens and the yen rises sharply against the dollar. This will cause asharp unwinding of the “yen carry trade”, triggering an avalanche of sharp declinesin all financial markets.But what might cause the yen to rise? The following is a possible scenario: Aswe head into the economic slowdown, the carry trade money that has flowed intorisky cyclical assets is likely to fall in value. As a result, speculators in these assetswill cut their losses, bail out and repay their yen debts. This is a scenario for disasterbecause when the yen rebounds against the dollar, it often snaps back very fast andcarry trades can go from profit to loss with almost no warning.A popular chaos theory axiom (known as the “butterfly effect” because of thetitle of a paper given by the mathematician Edward Lorenz in 1972 to the AmericanAssociation for the Advancement of Science in Washington, D.C. entitled “Predictability: Does the Flap of a Butterfly’s Wings in Brazil Set Off a Tornado inTexas?”) stipulates that a small change in the initial condition of the system (theflapping of the wing) causes a chain of events leading to large-scale phenomena. Hadthe butterfly not flapped its wings, the trajectory of the system might have been vastlydifferent.A financial series would appear to be chaotic in nature, but its statistics are notbecause, as well as being orderly in the sense of being deterministic, chaotic systemsusually have well defined statistics.The rapid progress of global communications has contributed to the integration ofall international financial markets as the world has gotten smaller due to the ability tocommunicate almost instantaneously. Relationships that were dismissed as irrelevantin the past cannot be ignored any more as the globalization of the markets contributesto a convergence of formerly unrelated markets.Take a look at the comparison chart in Fig. 1.1. The S&P 500 is depicted with abold thick line. The second one however is not even a stock index. It is the Japaneseyen exchange rate (USD/JPY).The next composite chart in Fig. 1.2 is of three stock indices. The first two (depictedwith a bar chart and thick line) are of the S&P 500 and the Nasdaq Composite respectively. The third chart (thin line) is the Athens General Index which, surprisingly,correlates better with the S&P 500 than its compatriot, the Nasdaq Composite.
Intermarket Analysis
5
Figure 1.1 Comparison chart of the S&P 500 (in bold with the scale on the right Y-axis)and the yen (USD/JPY) (with the scale on the left axis) from June 2006 to January 2008.
The above examples are included to illustrate that the integration of global marketscan extend beyond the obvious relations.I often hear CNBC guests suggesting investing in international markets as a meansof diversifying one’s portfolio away from the US equity markets. Although someemerging markets may have relatively medium to low correlation with US markets,one important question to ask is whether diversification works when it is neededmost. Evidence from stock market history suggests that periods of negative shocksand poor market performance were associated with high, rather than low, correlations.The events of 21 January 2008 are still fresh in my mind, when a 2.9 % correction inthe S&P 500 was followed the next day by a devastating 7.2 % drop in the GermanDAX, wiping out nine months of profits in a day. Emerging markets sunk evenmore with the Jakarta Composite falling more than 12 % in two days while Brazil’sBovespa lost more than 8.5 %. Indeed, investors who have apparently relied upondiversification in the past to protect them against corrections of the market have beenfrequently disappointed.
Intermarket Analysis
7
the system’s performance. These can only be created by taking into considerationdirectional movements of correlated markets.The use of intermarket correlation analysis can help you improve on your tradingsystem by avoiding trades against the prevailing direction of correlated markets, butcan also be used on its own to develop a complete system based on divergencesbetween two or more highly correlated markets. Knowing the correlation of themarket you propose to trade with other markets is very important for predictingits future direction. In addition, short-term traders can take advantage of the timedifference between world markets and anticipate the next day’s movement. Asianmarkets are the first to start trading, followed by the European markets. For a UStrader the insight gained from all preceding markets is a valuable tool in predictingat least the opening in his local market.I have found that the most accurate economist is the market itself. It is far easier toforecast economic activity from the behavior of markets themselves than it is to forecast the capital markets from lagging economic statistics such as the unemploymentindex. The market is a discounting mechanism. It interprets the impact of economicnews some time in the future. Of course, this is only a guess and guesses are notalways right. But the truth is that the market is a much better guesser than any of usare, as it represents the average opinion of all the economists in the world.There appears to be no end to the conclusions that can be drawn if a little understanding, imagination, and pure common sense are applied. Major changes incommodity prices affect the bond markets of different countries in different ways,depending upon their economic structure.What sectors are affected first? Which asset class will provide the best potentialprofits? If opportunities dry up in one sector, where is the money heading to takeadvantage of the next cycle? This is what intermarket analysis can tell you if youlearn what to look for, which makes it a grand endeavor and a continuing challengebut always worth the effort.Intermarket analysis can also be useful in estimating the duration and state ofthe business cycle by watching the historic relationship between bonds, stocks andcommodities as economic slowing favors bonds over stocks and commodities.Near the end of an economic expansion bonds usually turn down before stocksand commodities and the reverse is true during an economic expansion. Bonds areusually the first to peak and the first to bottom and can therefore provide amplewarning of the start or the end of a recession. Bonds have an impressive record as aleading indicator for the stock market, although this information cannot be used inconstructing a trading system as the lead times can be quite long, ranging from oneto two years.You can see in Fig. 1.3 that bonds peaked in October 1998, 18 months beforestocks peaked in March 2000 and 29 months before the official start of the recessionin March 2001. The Commodity Research Bureau (CRB) index was the last to peak,making a complex triple top formation with the last peak coinciding with the start ofthe recession.
6
Intermarket Trading Strategies
Figure 1.2 Weekly comparison chart of the S&P 500 (thick line with the scale on the rightY-axis), the Nasdaq (in bold with the scale on the left axis) and the Greek Athens GeneralIndex (ATG) from 1999 to 2008.
The only effective method of diversifying one’s portfolio is by including assetclasses with low or negative correlation to stocks such as cash, foreign exchange orcommodities. Whatever the relationship is – leading, lagging, or divergent responsesto economic conditions – a strong negative correlation coefficient between two markets is a suggestion that these markets will move against each other sometime in thefuture. And, of course, the higher the absolute value of the coefficient of correlation,the higher the diversity of their performances.Although intermarket analysis has been classified as a branch of technical analysis,it has not been embraced fully by analysts. The majority of traders continue to focuson only one market at a time and they tend to miss the forest for the trees. No marketexists in a vacuum, and traders who focus on the bigger picture portrayed throughall international markets tend to be the ones that deliver better performance.Traditional technical analysis indicators such as moving averages are laggingindicators calculated from past data and are limited in assessing the current trend.Regardless of the hours spent in back-testing, there is a limit beyond which a systembased on a lagging indicator can be improved further. Thus the addition of leadingindicators that anticipate reversals in trend direction is essential and beneficial to
Intermarket Analysis
1.1
9
DETERMINING INTERMARKET RELATIONS
The simplest and easiest method of intermarket analysis is a visual inspection of acomparison chart of one security superimposed on the chart of another. A customindicator can also be calculated from the ratio of prices, to help assess their pastrelation and anticipate future direction. Both of the above methods, however, arelimited to two markets and the use of the correlation coefficient is essential for ananalysis of multiple markets. For predictive purposes, we wish to detect correlationsthat are significantly different from zero. Such relationships can then be used topredict the future course of events in trading systems or forecasting models.In addition, linear regression can be used to predict the future price trend of amarket based on its correlation with multiple related markets.When assessing intermarket relations you should always keep in mind that theseare neither fixed nor static in time. Instead they fluctuate continuously in strength andtime. It is usually very difficult to determine which market is leading or lagging. Alead can shift over time and become a lag, with the markets switching positions asfollower and leader. In addition, a weak positive correlation can sometimes becomenegative and vice versa. For this reason it is always prudent to look at the prevailingrate of change of the correlation between two related markets before reaching anyimportant conclusions or trading decisions.The variability of the correlation over time is more evident in Fig. 1.4, where yearlycorrelations between the S&P 500 and four major international indices are plottedagainst time from 1992 up to the end of 2007. You can see that correlations before1996 were inconsistent and unpredictable but started to converge during the last tenyear period. The most incongruous relationship is that between the S&P 500 andJapan’s Nikkei (in white) as it fluctuated from negative to positive values over time.The recent integration of global markets has also been accelerated by a flurryof mergers, acquisitions and alliances between international exchanges, the mostimportant being the merger between the New York Stock Exchange and Euronext,Europe’s leading cross-border exchange, which includes French, Belgian, Dutch andPortuguese national markets. A few months later the Nasdaq, after a failed bid forthe London Stock Exchange, announced a takeover of the OMX, which owns andoperates stock exchanges in Stockholm, Helsinki, Copenhagen, Reykjavik (Iceland)and the Baltic states.
1.2 USING INTERMARKET CORRELATIONSFOR PORTFOLIO DIVERSIFICATIONThe benefits of diversification are well known: most investment managers diversifyby including international equities, bonds and cash in their US stock portfolio. Lesscommon, however, is the diversification into other asset classes such as commoditiesor foreign currencies (forex).
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Intermarket Trading Strategies
Figure 1.4 Yearly correlation variation between the S&P 500 and leading internationalindices from 1992–2007. The correlation with the DJ-30 is depicted by a black line, withGermany’s DAX by a dashed line, with the FTSE by a grey line, and with the Nikkei by awhite line. Notice the correlation volatility, especially before 1996. The Nikkei had the weakestand most volatile correlation with the S&P 500.
There is a widely held belief that, because commodities and currencies are tradedon very thin margins, they are just too risky and can lead to financial ruin. Visions ofwheat being delivered to the trader’s front yard, margin calls or stories of consecutive“limit down days” add fuel to the fire. Claims that “over 90 % of all futures traderslose money over time”