Entry
- Choose a universe (e.g. all top-100 coins on Hyperliquid, or S&P 500 stocks)
- Month-end: compute the 12-month return for each asset (often 12-1: 12M excluding the most recent month)
- Sort; long leg = top decile/quintile; short leg = bottom decile/quintile
- Equal-weight within each leg
Exit
- Re-rebalance at the next month-end — close on exit from the top/bottom zone
| Name | Typ. value | Description |
|---|---|---|
| lookback_months | 12 (12-1 Standard) | Formation period; the most recent month is typically excluded (short-term reversal) |
| holding_months | 1 | Rebalance frequency |
| portfolio_size | 10-20% je Leg | Deciles are common |
Pros
- The most robust academically documented anomaly
- Works across all asset classes
- Simple rule, low costs
- Rational aggregate of behavioral biases (underreaction, herding)
Cons
- Momentum crashes: after bear-market bottoms the 'loser' shorts often bounce back first → long-short loses heavily
- High turnover — fees eat up alpha at the retail level
- Needs a multi-asset universe
- Crowded since the 2000s — margins reduced
The original paper
Jegadeesh & Titman (1993) showed on US stocks 1965-1989: if you buy the top winners of the last 6 months and short the top losers, you achieve ~1% excess return per month — statistically highly significant, and not explainable by the asset-pricing models of the time.
Thirty years later: the effect holds in all asset classes studied — stocks, bonds, commodities, currencies, crypto. Fama & French called it 'the main embarrassment of the three-factor model' — a persistent phenomenon for which theory has no clean explanation.
The rule (12-1 standard)
- Month-end: compute each asset's return over the last 12 months, excluding the most recent month (because of short-term reversal effects).
- Rank: sort in descending order.
- Long leg: equal-weighted position in the top decile (or top 10%).
- Short leg (optional, for long-short): equal-weighted position in the bottom decile.
- Rebalance: monthly (weekly is also possible).
Why it works
Research suspects behavioral biases: - Initial underreaction: investors adapt too slowly to news → slow pricing-in of known information - Delayed overreaction: momentum traders and herding amplify the trend with a lag - Anchoring: investors cling to anchor-price-bound old valuations
The combination produces a 3-12-month persistence, followed by long-term reversals (3-5 years).
Momentum crashes
The biggest drawback: after bear-market bottoms (e.g. March 2009, March 2020) the depressed 'loser' shorts explode upward quickly → the long-short portfolio takes heavy losses. Daniel & Moskowitz (2016) documented these momentum crashes in detail.
Modern implementations use volatility scaling (Moskowitz/Pedersen) or trend adjustment (Barroso/Santa-Clara) to dampen crashes.
Crypto evidence
Research across 3,900+ coins 2014-2022: cross-sectional momentum exists with a magnitude similar to that in stocks, especially at the weekly level. The crypto-specific variant favors shorter lookbacks (2-4 weeks) than the equity standard (12M).
Relevance for Botty
Botty trades single-asset — cross-sectional is not directly applicable. If Botty were extended to multi-coin (e.g. the top 50 Hyperliquid perps):
- Weekly ranking by 14-day return
- Long the top 3-5, equal-weighted
- (no short leg initially, to avoid momentum crashes)
That would be a substantial extension, but on a robust evidentiary foundation.