Oversold Large Caps: 2-Sigma Snapback
Time-series mean reversion. Buys Nifty 100 names trading more than two standard deviations below their own 60-day average price, sized by minimum-variance weights so the basket is not simply a bet on whichever name fell hardest. Weekly.
Why this is expected to work
Short-horizon reversal is the mirror image of medium-horizon momentum and just as well documented — De Bondt and Thaler (1985) showed extreme losers subsequently outperform extreme winners, and the effect is strongest over horizons of days to weeks. The mechanism is liquidity provision: when a large, well-covered stock drops sharply without news, someone is selling for a reason unrelated to value (a redemption, a margin call, an index deletion) and is paying a discount for immediacy. Buying that discount is being paid to supply liquidity. The universe is deliberately restricted to the Nifty 100, because the same screen on smallcaps cannot distinguish "temporarily dislocated" from "correctly repriced on news you have not read", and the reversal premium is exactly where the value trap lives.
How the pipeline works
NIFTY100 constituent
Keeps rows where nifty100_member == 1
nifty100_member == 1 Liquid: traded > Rs 10 cr/day
Keeps rows where turnover > 100000000
turnover > 100000000 Stretched 2 sigma below 60-day mean
Keeps names 2 sigma below their own 60-day average
15 most dislocated
Selects the bottom 15 by dislocation
What this template teaches
- RelativeValueRule — rolling z-score against a name's own history
- Why a rolling z-score cannot be written as one expression
- MinimumVarianceWeighting with covariance shrinkage
Mean Reversion: Getting Paid to Provide Liquidity
Buying large caps that have fallen two standard deviations below their own average is not contrarian instinct — it is being compensated for taking the other side of somebody's forced sale.
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