The majority of literature that discusses asset allocation linking multiple markets has a heavy dose of macro and microeconomics. Typically, macro-micro relationships require applying econometric models to comprehend the structural linkages between the two intertwined fields of economics. John Murphy removes the hard statistical methods while retaining the economic logic with chart-based reasoning.
John Murphy was the technical analyst for CNBC-TV for seven years and a professional analyst for over 25 years. His career includes time at Merrill Lynch as a Director of Commodity Technical Analysis. principles of microeconomics 8th edition mankiw John has his own consulting firm, JJM Technical Advisors. He is also president of MurphyMorris, Inc., which was created to produce educational software products and online services for investors.
There are adequate reader reviews on Amazon and Google Book Search, to help you decide if you will get the book. For those who have just started or are about to read the book, I’ve summarized the core concepts in the larger and essential chapters to help you get through them quicker.
The number on the right of the title of the chapter is the number of pages contained within that chapter. It is not the page number. The percentages represent how much each chapter makes up of the 246 pages in total, excluding appendices.
1. A Review of the 1980s. 16, 6.50%.
2. 1990 and the First Persian Gulf War. 16, 6.50%.
3. The Stealth Bear Market of 1994. 18, 7.32%.
4. The 1997 Asian Currency Crisis and Deflation. 14, 5.69%.
5. 1999 Intermarket Trends Leading to Market Top. 16, 6.50%.
6. Review of Intermarket Principles. 16, 6.50%.
7. The NASDAQ Bubble Bursts in 2000. 18, 7.32%.
8. Intermarket Picture in Spring 2003. 16, 6.50%.
9. Falling Dollar During 2002 Boosts Commodities. 14, 5.69%.
10. Shifting from Paper to Hard Assets. 14, 5.69%.
11. Futures Markets and Asset Allocation. 20, 8.13%.
12. Intermarket Analysis and the Business Cycle. 20, 8.13%.
13. The Impact of the Business Cycle on Market Sectors. 18, 7.32%.
14. Diversifying with Real Estate. 18, 7.32%.
15. Thinking Globally. 12, 4.88%.
Focus on chapters 3, 7 and 11-14, which makes up about 46% of the book. Especially chapters 11-14 are relevant for practical trading purposes. Unlike my prior book reviews, where I’ve summarized the key points for each focus chapter, I will summarize the key points across chapters 3, 7 and 11-14. This is to recognize the connectivity of intermarket relationships across the 4 main asset classes of Stocks (Equities), Bonds, Currencies and Commodities. The context of the summary is to be viewed from a retail option trader’s perspective.
Here are the Key Directional Intermarket Relationships in brief.
The U.S. Dollar (USD)
- USD turns up as Bonds rise under normal conditions but Bonds fall during deflationary periods. USD turns down as Bonds fall but Bonds rise during deflationary periods.
- USD turns up as Commodities fall. USD turns down as Commodities rise.
- USD turns up as Stocks rise but Stocks fall during deflationary periods. USD turns down as Stocks fall but Stocks rise during deflationary periods.
The USD remains the most liquid of all major traded currencies and maintains its position as the primary global reserve currency, despite growing sentiment for an alternative basket of currencies to replace it.
- Bonds turn up as the USD falls but the USD rises during deflationary periods. Bonds turn down as the USD rises but the USD falls during deflationary periods.
- Bonds turn up as Commodities fall. Bonds turn down as Commodities rise.
- Bonds turn up as Stocks rise. Bonds lead Stocks and Stocks lag behind Bonds. Bonds turn down as Stocks fall. Again, Bonds lead Stocks and Stocks lag behind Bonds.
- Commodities turn up as the USD falls. Commodities turn down as the USD rises.
- Commodities turn up as Bonds fall. Commodities turn down as Bonds rise.
- Commodities turn up as Stocks fall. Commodities turn down as Stocks rise.
- Stocks turn up as the USD rises. Stocks turn down as the USD falls.
- Stocks turn up as Bonds rise. Stocks turn down as Bonds fall. Again, Bonds lead Stocks and Stocks lag behind Bonds.
- Stocks turn up as Commodities fall. Stocks turn down as Commodities rise.
Specific to Equities, as you trade the options on Sector Indexes of the S&P 500, please be aware of the correlation versus non-correlation with other equity and non-equity traded products. I am stating in brief, the more commonly known relationships that are repeatedly elaborated on in the book:
- Changes in Energy (XLE) especially Oil (OIH, OSX) impacts Semiconductors (SMH, SOX).
- Utilities (XLU, UTH, UTY) are negatively correlated with Semiconductors (SMH, SOX).
- With broad-based Equity Indexes, the highest correlation is between Dow Jones and S&P 500.
- Canada benefits from rallies in oil being the ninth largest producer of crude oil globally. While Japan, a major net oil importer suffers. The tickers for this inter-play would be FXC/XDC (Canadian Dollar), FXY/XDN (Japanese Yen) and OIH/OSX (Oil).
- Gold (XAU, GLD) behaves like the Australian Dollar (FXA, XDA). Australia is the third largest producer of gold globally.
- Top three currencies that have the tightest correlations with commodities are the Australian Dollar, the Canadian Dollar and the New Zealand Dollar.
- Gold/Silver (XAU, GLD) has very little correlation with other Indices.
In conclusion, from a retail option trader’s viewpoint, always remember that it is volatility that you are trading. To trade the volatilities across multiple asset classes, use an optionable Index representing that particular asset class. Remember, Implied Volatility can be added to or reduced from your portfolio, as not all Asset Classes or Sectors or Individual Companies or Countries move up/down in value ALL at the same time; and/or, ALL at the same rate.
This is not a criticism of the book but a personal observation. It does not address the use of Relative Strength as a mechanism to cycle in or cycle out of an asset class, as one asset class weakens or strengthens against another asset class. I have written about Relative Strength in another article, entitled “Stock Option Trading – Fundamental Flaw in Fundamental Analysis and Stock Picking”. Please read it as a supplement to this article.
Thanks for reading my article, Clinton Lee.
Founder, Home Options Trading: a uniquely retail-focused option-centric trading firm.
Please see Consistent Results ([http://www.homeoptionstrading.com/consistent_results/]), displaying the Model Portfolio’s Performance YTD, updated each month-end. The portfolio models a typical self-directed retail option trader’s account up to USD $50,000. Here’s the stats in summary:
Return: Profit/Start of Year Cash Balance = up +75.62%.
Win/Loss Probability = 90.48%. 9 Wins per 1 Loss. Average Win/Average Loss = $3.09 Won per $1 Loss. Performance Ratio = (Win/Loss Probability) x (Average Win/Average Loss) = 90.48% x $3.09 = 2.80. Positive Expectancy = $1,051 per trade.
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Clinton’s career spans 16 years of treasury, finance and banking across Hewlett Packard, JP Morgan Chase, Citibank; and, is currently a Corporate Director for Regional Business Development with ABN Amro (acquired by RBS) in Asia. Despite the years in the finance/banking industry, it did not help him directly grasp online options trading from home.