Gong, Rui and Lynch, John and Ogden, Richard, Skewness Managed Portfolios (May 31, 2026, SSRN) argue that returns to many prominent cross-sectional anomalies are driven by a small number of extreme, positively skewed stock returns. So explicitly selecting stocks with high expected skewness for the long side can substantially improve anomaly performance (summary).
If you look at the pre-COVID window, Omega variants out-performed the basic ones and Omega + Skew ruled them all. Also, the Skew variants out-performed the corresponding basic ones. However, zooming in on the post-COVID performance, we see that the effect of the Skew adjustment has attenuated a bit.
If you include the COVID crash, the big picture argues in favor of Omega + Skew.
You can go ahead and mark this as another useful tool in your toolbox.
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.
Sadly, no. If you held the top 5 ETFs in the Leading quadrant for a month, you solidly under-performed SPY.
What if you did a 5-year rolling window to pick a portfolio of 4 ETFs that had the highest Omega Ratio and held them for a year?
There’s enough juice even after assuming a 25bps transaction cost.
However, the out-performance is uneven. You could go years before you will see this strategy pay off.
There’s a caveat here for Indian investors. Gains for holding periods of less than two years is treated as regular income. So, depending on your tax slab, you could lose in taxes what you gained employing this strategy. A two-year holding period where half the portfolio is rebalanced every alternate year to meet the capital gains treatment had uninspiring returns.
We explored a bunch of other scenarios that did not clear the bar. You can read about it and have a look at the code on github.
The VIX is a poor proxy for volatility, and so is standard deviation and Sharpe Ratios that treat downside volatility the same as upside volatility. Long-only investors don’t mind a bit of volatility as long as the numbers go up. Its the drawdowns that are usually terrifying.
Does swapping symmetrical volatility measures with something that measures only downside volatility make sense?
Enter the Omega Ratio: bucket the ratio into quintiles, map each quintile to an exposure. Lower the ratio, lower the exposure (outline).
We get lower drawdowns (almost cut in half) and higher Sharpe Ratios but with lower returns for the most part. This strategy makes most sense for the NIFTY 50 and not so much for NIFTY500 MOMENTUM 50.
While NIFTY 50 drawdowns are low, is a 20% drop something that can be leveraged?
We also ran a sensitivity test on DRAG to see how transaction cost assumptions impact the strategy. We found that it actually impacts the lookback that is selected from the training set.
We expect transaction costs to range 25-50bps so the flipping around of lookbacks is worrisome.
The tl;dr is that reducing volatility comes at a cost and you pay it both in terms of reduced returns and transaction costs.
A changepoint is a point in time where the statistical properties of a sequence change. For financial return series this typically means the mean, variance, or distribution of returns shifts — a stock that was trending sideways suddenly becomes volatile, or a calm market enters a turbulent regime.
Can we use changepoint detection to time exposure to the market and beat simple buy-and-hold, on a risk-adjusted basis, on India’s NIFTY indices?
First, we calculated an aggregate score using different changepoint algorithms to classify the return-series. This was done with both a sliding window and an expanding window of data.
Then, we tried the simplest use of the signal: be fully invested when STABLE, fully out when UNSTABLE. This binary approach underperformed buy-and-hold badly, on both return and Sharpe ratio, on every index.
Throughout the process, we kept a 50-day SMA as the base case to beat. It is a simple totem to keep us honest about the complexity vs. efficacy trade-off.
The problem with this binary approach is that volatility clusters – the worst days and the best days tend to occur together. However, changepoint is backward looking so you will end up catching the worst days and missing out on the best days.
To fix this, we added a direction condition: only treat instability as a reason to exit if the market is also in a downtrend (price below its 50-day moving average). Otherwise, stay invested through volatile-but-rising periods. It removes the worst part of the filter – exiting during uptrends.
The next step was to remove the binary nature of the strategy: size the position by mapping it to model confidence [0,1] and sizing only when the market is in a downtrend (close < 50-day MA); in uptrends, stay fully invested regardless of volatility.
