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Chapter 2.1 — Timing Is the Key

Hurst price-motion model: what it claims

Hurst's price-motion model: five proposed causes of price movement, the 75/23/2 estimates, the cyclic component, and the limits of the historical hypothesis.

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In one sentence — In 1970 J. M. Hurst proposed that price arises from five kinds of influence; only one, “X motivation,” would be cyclic and partly timeable.

What is Hurst's price-motion model?

The model appears in Chapter 2 of The Profit Magic of Stock Transaction Timing. Its first five elements, numbered I–V, assign roles to random decisions, historical events, foreseeable fundamentals, unforeseeable fundamental shocks, and X motivation. Elements VI–X describe how the cyclic part would work.

The 75%, 23%, and 2% shares are Hurst's stated estimates, not modern measurements accepted for every market. That distinction matters: this entry describes a historical model faithfully without turning it into a universal law.

Hurst’s price-motion model Five causal roles; three numerical shares proposed in the 1970 book. The percentages are Hurst’s historical estimates, not universal market measurements. HURST 1970 · CH. 2 Hurst’s price-motion model Five causal roles; three numerical shares proposed in the 1970 book CYCLEPEDIA DIAGRAM — EMICICLO WHAT HURST ASSIGNS — HISTORICAL ESTIMATES 75% foreseeable fundamentals 23% X motivation 2% · random II · HISTORICAL EVENTS not quantified treated as negligible in the model IV · FUNDAMENTAL SHOCKS not quantified rare in the model, but potentially sharp HOW THEY ADD UP — SYNTHETIC ILLUSTRATION synthetic price slow trend trend + cycles STATUS 1970 model STATED SHARES 75 / 23 / 2% CHART synthetic The percentages are Hurst’s historical estimates, not universal market measurements.
Top: the three numerical shares proposed by Hurst and the two unquantified elements. Bottom: a synthetic example of trend, cyclic components, and small random noise. It is not fitted to market data.
Select the highlighted points to explore the detail

Elements I–V: five roles, not five percentages

I. Random decisions

Hurst estimates that about 2% of motion comes from decisions with no systematic relation to price. He is not saying that the remaining 98% is predictable: much of the model describes a slow trend or shocks that a trader cannot time.

II. Historical events

Wars, crises, and major news are treated as negligible influences on long-run systematic motion. Hurst uses the Dow's behaviour after President Kennedy's assassination as an example. It is an illustration selected by the author, not general proof that news does not matter.

III. Foreseeable fundamentals

Hurst assigns about 75% of motion to knowable fundamental factors, represented as a trend that changes slowly and smoothly. It is the model's underlying current, not a percentage calculated from the chart shown here.

IV. Unforeseeable fundamentals

Unexpected company- or market-specific developments receive no numerical share. In the model they are rare but can produce large, sudden moves. Hurst's response is capital protection, not forecasting the event.

V. X motivation

Hurst assigns about 23% to X motivation: an oscillatory, “semi-predictable” part that would justify cyclic timing. Semi-predictable does not mean knowing the next low precisely; it means working with estimates exposed to variation and error.

Elements VI–X complete the model

The cyclic component is not defined only by the 23% share. Hurst adds five rules: summation, commonality, variation, nominality, and proportionality. The entry on the five principles of the cyclic model explains them in order and separates the book's formulation from later extensions.

How to read the figure

The upper bar reproduces only the book's three numerical shares. Historical events and unforeseeable shocks remain separate because Hurst does not assign them a percentage; that is why there are not five slices adding to one hundred.

The lower chart is an illustrative laboratory. A slow trend is added to three waves and a small random component to produce a synthetic price. It does not prove that a real price contains those same waves, nor estimate shares for a particular instrument.

Where the evidence ends

Chapter 2 states the model; later chapters and appendices in the same book provide Hurst's selected demonstrations. They are evidence within his argument, not independent validation of the 75/23/2 shares.

The author's later expectation of about 10% wrong signals is likewise a forecast for his method, not a guaranteed success rate. The model is most useful as a set of hypotheses to test against data, alongside invalidation and risk-management rules.

Sources