The moving-average rule that beat the Dow — and the cost that kills it

Brock, Lakonishok & LeBaron tested moving-average and trading-range-breakout rules on 90 years of Dow data and found buy signals reliably preceded higher returns than sell signals. This rebuilds that exact statistic on synthetic data you control — including the two things that later erased most of the result: transaction costs, and how many variants you had to try before one looked good.

The rule

Short average1 bar

BLL's headline pairs were 1–50, 1–150, 5–150, 1–200 and 2–200.

Long average200 bars

Band filter1.00%

Signals only count when the averages are this far apart — BLL's 1% band, meant to strip out whipsaw crossings.

Signal handling

Variable = stay positioned while the condition holds (BLL's VMA). Fixed = hold 10 bars after each crossing (their FMA).

Position when bearish

Costs & market

One-way cost10 bps

Bessembinder & Chan (1998) measured real costs above the break-even level these rules needed.

Trend persistence0.35

0 = a pure random walk, where no rule can have real predictive power. Anything above 0 adds a slow-moving unobserved drift the rule can genuinely detect.

Sample#1

Same generator, new random draw. Watch how much the result moves with luck alone.

BLL's statistic: buy-day return minus sell-day return, annualised

gross of costs, as the 1992 paper reported it

Mean return, buy days

Mean return, sell days

Rule vs buy & hold, net of cost

CAGR difference, hypothetical

Break-even one-way cost

Rule variants you have tested in this session1

Synthetic price · both averages · signals

Price Short average Long average Buy signal Sell signal In a buy position

Cumulative index, net of cost (100 = start of sample)

Rule, net of cost Buy & hold

What you are looking at: a hypothetical index of a simulated rule on synthetic data, not an account and not a return anyone earned. Set trend persistence to 0 and the rule's edge disappears into noise, because there is nothing to detect — any remaining spread is the luck of one draw. Push the one-way cost above the break-even figure and the rule loses to buying and holding even when the raw signal still looks predictive.

What the research actually found

Support level: mixed (split). The literature does not agree, and IndicatorEdge does not pretend it does.

“On 1897–1986 data, MA rules produced buy signals that reliably preceded higher returns than sell signals (Brock et al. 1992) — the single most-cited pro-TA result in finance.”

“The edge largely dies out of sample. Correcting the same rule universe for data snooping erases most significance (Sullivan, Timmermann & White 1999); measured trading costs exceed the break-even costs of the BLL rules (Bessembinder & Chan 1998); and rules that predicted small-cap and NASDAQ indexes stop working after ETFs made those indexes cheap to arbitrage (Hsu, Hsu & Kuan 2010).”

The oldest rigorous test in this family, Fama & Blume (1966) on filter rules, found nothing after costs. So the honest reading is: the raw statistic BLL measured is real and was measured carefully; the tradable edge is what fails.

Fama, Eugene F., & Blume, Marshall E. (1966). “Filter Rules and Stock-Market Trading.” Journal of Business, 39(1), 226–241.

Brock, William, Lakonishok, Josef, & LeBaron, Blake (1992). “Simple Technical Trading Rules and the Stochastic Properties of Stock Returns.” Journal of Finance, 47(5), 1731–1764.

Bessembinder, Hendrik, & Chan, Kalok (1998). “Market Efficiency and the Returns to Technical Analysis.” Financial Management, 27(2), 5–17.

Sullivan, Ryan, Timmermann, Allan, & White, Halbert (1999). “Data-Snooping, Technical Trading Rule Performance, and the Bootstrap.” Journal of Finance, 54(5), 1647–1691.

Hsu, Po-Hsuan, Hsu, Yu-Chin, & Kuan, Chung-Ming (2010). “Testing the predictive ability of technical analysis using a new stepwise test without data snooping bias.” Journal of Empirical Finance, 17(3), 471–484.