The stock that was sold usually went up after. At 20-days, you were 1.5× more likely to miss a >5% rally than to avoid a >5% slide.
The stock that was bought instead did about the same. A drift-positive basket was replaced with another drift-positive basket. The churn bought nothing and you paid transaction costs on top.
Trailing stop losses might be psychological crutches. But with everything else in finance that is designed to ease the pain of ownership, you end up paying for it.
In the paper Momentum Returns and the Role of Liquidity Improvements (Jeppe Bro, March 28, 2026, SSRN) argues that the momentum anomaly arises from cross-sectional liquidity dynamics rather than representing an independent risk premium. Past winners systematically improve in liquidity prior to the portfolio formation period, whereas past losers severely deteriorate (summary).
What if we used this LIQIM to setup a long-only portfolio of 20 Indian stocks?
It doesn’t quite work that way. So, we looked at the return stats of LIQIM quintiles and figured that we could use that to enhance momentum by filtering out stocks that had degraded liquidity (backtest).
Turns out, there is about 50 bps of performance that you can squeeze out of a typical long-only momentum strategy using this liquidity filter.
Typically, momentum strategies create portfolios of 20-50 stocks. What if only a single stock was used?
Ammann, Manuel and Moellenbeck, Marcel and Schmid, Markus, Feasible Momentum Strategies in the US Stock Market (November 17, 2010, SSRN) explores precisely this idea (summary).
Each month, stocks are ranked by past return over a formation period J (3, 6, or 12 months).
The best-performing stock(s) are “winners” (bought).
Short the NIFTY.
Positions are held for a holding period K (3, 6, or 12 months), using the standard overlapping-portfolio construction (K staggered investment “strands” so only 1/K of the book turns over each month).
A one-month lag is inserted between the formation period and the holding period to avoid short-term reversal effects.
Portfolios of 1, 3, 5, or 10 stocks N per side are tested (buying more than just the single best).
Paper Best
Using the “best” config in the paper, we realized that considering just returns without volatility leads to failure.
Beta hedging + Omega
Momentum stocks tend to be high beta, so instead of shorting the same notional, we shorted the beta. Besides, Sharpe penalizes volatility symmetrically. Omega with NIFTY as MAR penalizes only underperformance relative to the index.
Also, the training set pointed us toward a longer holding period K (6 vs. the paper’s 3) and a larger number of stocks in the portfolio N (3 vs. the paper’s 1).
With these changes, we were able to squeeze out 5 more points in the hedged variant and almost match the buy & hold momentum index in the long-only variant.
The most important contribution of this paper is the parameterization of the holding period and the usage of strands (portfolio sleeves) to create over-lapping portfolios. Something that can be carried over to future research in this area.
Nathan, Daniel and Suominen, Matti and Tasa, Joni, The Intramonth Momentum Cycle (March 16, 2026, SSRN) discusses an end-of-month effect where the short-leg of a momentum strategy out-performs (summary).
The paper, however, conveniently leaves out borrow costs. Besides, in India, only stocks with futures listed can be shorted. When you include these constraints and add up transaction costs, the results are underwhelming.
In Barardehi, Yashar and Bogousslavsky, Vincent and Muravyev, Dmitriy, What Drives Momentum and Reversal? Evidence from Day and Night Signals (February 6, 2023, SSRN) the authors posit that momentum is entirely an intraday phenomenon (summary).
The authors split the standard past-return momentum signal into its intraday and overnight components and test which piece actually predicts future returns. They find that intraday-signal momentum works – stocks with high past intraday returns keep outperforming – while past overnight returns show no significant predictive power for future returns.
What we found with Indian stocks:
tl;dr: while overnight returns under-perform, intraday returns also under-perform a “total” (close-to-close) return ranking scheme.
