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Equal-Weight Consensus

Updated dailyData needs: lowlong onlyshort onlylong short

2009

#42 meta_equal_weight — DeMiguel-Garlappi-Uppal 2009 1/N over alpha sleeves.

In plain terms

Instead of trying to optimize weights across sub-signals (which overfits), just equal-weight K canonical price signals. Hard to beat out-of-sample.

How it works

DeMiguel-Garlappi-Uppal (2009, RFS) "Optimal vs Naive Diversification" shows 1/N portfolio weighting is shockingly hard to beat out-of-sample once you account for estimation error in fancy optimization. We apply this at the alpha-family level: several canonical sub-signals (SMA50/200 cross, RSI(14) mean-reversion, 20d-high breakout, 1m-vs-12m TSMOM-lite, 5d return reversion), each returns {-1, 0, +1}, the meta position is the mean clipped to [-1, +1] — buy when ≥3/5 agree long, short when ≥3/5 agree short.

Live results

23 times picked on its own · 52 times inside a blend (47 beat the stock) · updated 2026-06-06

This strategy is a frequent ingredient in blends that combine a few strategies on one stock. It has contributed to 52 such blended picks (47 of which beat simply holding the stock). Picking it on its own is only one of the ways it shows up.

How its picks scored vs. buy & hold

Each pick is graded on a recent year it was never tuned on, against simply owning the same stock

Where its edge concentrates

Share of picks in each company-size group that beat buy & hold

How often it trades

Active vs. patient. Bars on the left mean it waits for rare setups; bars on the right mean it trades often

Return vs. buy & hold

How much each pick beat or trailed simply owning the stock over the test year (extreme microcap moves trimmed)

Loading substrate evidence…

Data dependencies

Daily prices
Adjusted-close OHLCV for every US-listed ticker; primary price feed.

Expected edge

See the source research for the original effect size; a modern replication on new data may be weaker.

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