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Technical Analysis of the Financial Markets — John J. Murphy
Macro Overview & Strategic Value
Chapter 17 expands the analytical frame from single-market chart reading to a cross-asset macro framework, arguing that currencies, commodities, bonds, and stocks form a single interconnected chain — the dollar influences commodities, commodities influence bonds, and bonds influence stocks — such that no single market can be fully understood in isolation. Murphy’s core thesis is that this intermarket “ripple effect” gives technicians an edge unavailable to pure fundamentalists: instead of needing to build a fundamental model for every asset class, a trader can simply overlay the same chart-reading toolkit from earlier chapters onto these related markets and read the sequence directly off price.
This matters to a practitioner because it operationalizes sector/industry rotation and asset allocation as a chartable, rules-based process rather than a discretionary macro call — relative strength ratio charts convert “which sector should I rotate into” into the same trendline/moving-average toolkit already mastered for individual securities. It also gives a trader an early-warning system: bond and commodity trend changes are explicitly framed as leading indicators for equity turns, meaning intermarket monitoring can flag a stock market shift before it’s visible on the S&P 500 chart itself.
Structurally, this chapter is the book’s clearest bridge from technical analysis to macro/economic forecasting — Murphy explicitly cites Geoffrey Moore’s work using intermarket sequencing to track the business cycle, elevating chart-based methods into a genuine cross-asset forecasting discipline, and previews neural-network-based intermarket software as the natural computational extension of the manual ratio-chart techniques described here.
Core Concepts & Mechanics
- The four-market chain (dollar → commodities → bonds → stocks) — A rising dollar depresses commodities, falling commodities support bond prices (via lower inflation expectations), and rising bond prices are typically bullish for stocks; the practical implication is that a trader should scan all four asset classes before drawing conclusions about any single one.
- Program trading as the stocks/futures arbitrage link — When the S&P 500 futures premium over its cash index deviates from computed “fair value,” automatic arbitrage triggers program buying (bullish, futures overpriced) or program selling (bearish, futures underpriced), directly transmitting sudden futures-market moves (often from bonds) into cash equity prices.
- Bonds as a leading indicator for stocks — Rising bond prices (falling yields) are generally supportive for equities, and bond trend turns often precede corresponding turns in the S&P 500; monitoring Treasury Bond futures gives a trader an early warning system for equity market direction shifts.
- Commodities as a leading indicator for bonds — Commodity prices act as a proxy for inflation expectations and typically move inversely to bond prices; a sharp commodity upturn warns of coming bond weakness, while commodity weakness supports bond strength.
- Gold and the dollar as the commodity-complex trigger — Gold and the dollar typically trend inversely, and gold itself functions as a leading indicator for the broader commodity complex — meaning dollar analysis is a prerequisite for reading gold, and gold analysis is a prerequisite for reading commodities generally.
- Sector rotation via bond/commodity balance — When bonds are strong and commodities weak, interest-rate-sensitive sectors (utilities, financials, consumer staples) outperform; when commodities are strong relative to bonds, inflation-sensitive sectors (gold, energy, cyclicals) outperform — converting the intermarket read directly into an equity sector allocation decision.
- Relative strength (ratio) analysis — Dividing one market’s price by another’s (e.g., CRB Index ÷ T-Bond futures, or an individual stock ÷ its sector index) produces a single line whose rise/fall shows which side is outperforming; standard trendline and moving-average tools applied to this ratio line itself generate rotation buy/sell signals.
- Top-down market approach — Establish the overall market’s trend first, then select outperforming sectors/industry groups via relative strength, then select outperforming individual stocks within those groups — a direct structural echo of the macro-to-micro sequencing established in Chapter 8, now applied across market breadth rather than just time frame.
- Deflation-driven regime shift and correlation stability — The standard 1970s-1990s intermarket relationships assume a disinflationary/inflationary backdrop; under a genuine deflation scenario, bonds and stocks can decouple (bonds rally while equities fall) even as the bond/commodity inverse relationship persists — meaning intermarket rules should be applied within their historical regime, not treated as permanently fixed.
Technical Terminology & Reference Table
| Term | Operational Definition |
|---|---|
| Intermarket analysis | Study of interrelationships between currencies, commodities, bonds, and stocks |
| Program trading | Automated arbitrage between S&P 500 futures and the cash index when price deviates from fair value |
| Fair value | Theoretical futures-to-cash premium based on interest rates, index yield, and days to expiration |
| Program buying / selling | Arbitrage-driven buying/selling of stock baskets triggered by futures mispricing vs. fair value |
| Relative strength (ratio) analysis | Charting the ratio of one market/security’s price to another’s to identify relative outperformance |
| Sector rotation | Shifting capital among stock market sectors/industry groups based on intermarket conditions |
| Positive/negative correlation | Two markets trending in the same direction (positive) or opposite directions (negative) |
| Top-down approach | Sequencing analysis from overall market → sector → individual stock |
| Decoupling | Breakdown of a normally stable intermarket relationship (e.g., bonds and stocks moving independently) |
| Neural network (intermarket) | AI-based pattern-detection software used to model complex, simultaneous intermarket relationships |
The Author’s Market Philosophy
Murphy’s model here treats financial markets as a single interconnected system rather than a collection of independent instruments — his central assumption is that price relationships between asset classes are structurally persistent (though not immutable) and can be read directly off charts using the same tools built throughout the book, without requiring separate fundamental models for each market. Edge generation, in this framework, comes from being one of relatively few traders who systematically monitors the full cross-asset chain rather than a single market in isolation — the dollar/commodity/bond/stock sequence gives an informational lead time that a single-market technician wouldn’t have. He explicitly qualifies this model’s boundary conditions: the standard relationships hold under an inflation/disinflation regime but can shift meaningfully under deflation, showing an assumption that intermarket structure is real but regime-dependent rather than a universal law.
Systemic & Portfolio Integration
The top-down, relative-strength-driven sector rotation framework is a direct portfolio-construction extension of trend-following principles — instead of applying trend tools only to a single instrument, the same toolkit rotates capital toward whichever asset class or sector currently exhibits the strongest relative trend. Correlation measurement between markets also functions as a systematic risk-management input, since Murphy explicitly ties allocation weighting to intermarket relationship strength (heavier reliance on high-correlation pairs, lighter on near-zero ones), directly extending the diversification/concentration trade-off discussed in Chapter 16’s money management framework.
Important Formulas, Data, or Initial Examples
- Ratio construction: relative strength line = Price(Market A) ÷ Price(Market B); rising line = A outperforming B, falling line = B outperforming A.
- CRB Index/T-Bond ratio example: 1994 favored commodities (rising ratio); 1995 favored bonds (falling ratio); the ratio fell sharply in mid-1997 during the Asian crisis on deflation fears.
- Historical bond-stock linkage example: bond price bottoms in 1981, 1984, 1988, 1991, and 1995 preceded major stock market upturns; bond peaks in 1987, 1990, and 1994 warned of weak stock market years.
- Dollar/gold example: the 1980 dollar bottom coincided with a major commodity peak; the 1995 dollar bottom contributed to a sharp commodity decline a year later.
- Sector-specific linkage examples: utility stocks closely track (and often lead) Treasury Bond prices; gold mining shares closely track gold prices; rising oil prices help energy stocks but hurt airline stocks.
- Dollar/cap-size example: a strong dollar tends to favor small-cap, domestically-oriented stocks (e.g., Russell 2000) over large-cap multinationals (e.g., Dow Industrials), and vice versa for a weak dollar.
- 1997-98 deflation case study: the Asian currency/stock crisis (spreading to Russia and Latin America by mid-1998) crushed commodity exporters (Australia, Canada, Mexico, Russia) while pushing Treasury Bonds to record highs, causing bonds and stocks to decouple.
Active Recall Evaluation
- Explain the full causal chain Murphy describes running from the U.S. dollar through to the stock market, and identify at which link gold sits within that chain.
- Why does Murphy describe Treasury Bond futures as a “leading indicator” for the stock market rather than simply a correlated one?
- Walk through the mechanics of program buying: what specific misalignment triggers it, and why is its net effect bullish for the cash index?
- How does a deflationary environment specifically alter the standard intermarket relationships described earlier in the chapter, particularly between bonds and stocks?
- Explain how relative strength (ratio) analysis converts a sector-rotation decision into a standard technical-analysis problem, using the CRB Index/T-Bond ratio as a working example.
Answer Key (spoiler)
- The chain runs: the U.S. dollar influences commodity prices (a rising dollar depresses commodities), commodity prices influence bond prices (rising commodities/inflation expectations pressure bonds lower), and bond prices influence stock prices (rising bonds typically support stocks); gold sits within the commodities link specifically — it is described as being especially sensitive to the dollar and, in turn, as a leading indicator for the broader commodity complex, making it effectively a sub-link between the dollar and general commodity prices.
- Historical chart comparisons show that turns in Treasury Bond prices (both short-term shifts and longer-range trend changes) have tended to occur before corresponding turns in the S&P 500 cash index — bond bottoms in 1981, 1984, 1988, 1991, and 1995 preceded stock upturns, and bond peaks in 1987, 1990, and 1994 preceded weak stock years — this consistent lead time, rather than just simultaneous co-movement, is what qualifies bonds as a leading rather than merely correlated indicator.
- Program buying is triggered when the S&P 500 futures contract trades above its computed “fair value” premium over the cash index by a predetermined threshold; arbitrageurs respond by selling the overpriced futures contract and buying the underlying basket of S&P 500 stocks to bring the two back into alignment, and that basket-buying activity directly pushes the cash index higher, making the net effect of the arbitrage bullish for the stock market even though the trade itself is a relative-value, not directional, position.
- Under normal disinflation/inflation regimes, bonds and stocks are typically positively correlated (both benefiting from falling or moderating inflation); under a genuine deflation scenario, the bond/commodity inverse relationship persists (commodities fall, bonds rise) but the bond/stock relationship can break down entirely, since falling prices broadly can hurt corporate earnings and equity valuations even while driving safe-haven demand into bonds — causing the normally-linked bond and stock markets to decouple and move in opposite directions.
- Instead of relying on subjective judgment about which asset class or sector is “stronger,” relative strength analysis converts the comparison into a single plotted line (Price A ÷ Price B) that can be read with the exact same trendline and moving-average tools used throughout the book; in the CRB/T-Bond example, a rising ratio line objectively signals commodities are outperforming bonds (favoring inflation-sensitive stock sectors), while a falling ratio line signals the opposite (favoring rate-sensitive sectors) — turning a qualitative macro judgment into a standard, chartable technical-analysis decision.