Category: Investing Insight

Investing insights to make you a better investor.

Stop-losses or Stop-profits?

While we use trailing stop losses in our Rapid Fire themes, they are by no means the brahmastra they are made out to be. We audited two of the strategies in that theme: Momo (Velocity) v1.0 and Momo (Relative) v1.1. We would have made more money by doing nothing.

Some findings (details):

  1. 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.
  2. 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.

Analysis, charts and code are on github.

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VIX and Trend-following

We are not big fans of using VIX for timing equities. Strategies that seem to work on US market data fall apart when applied to Indian equity indices. When Alpha Architect published their follow-on VIX and Trend Following results, we decided to test it on our own NIFTY large-, mid- and small-cap indices.

India VIX decides how much recent history to use:

  • When volatility is calm, rank the assets by their 10-month performance. This changes slowly and avoids reacting to short-term noise.
  • When volatility is elevated, use 3-month performance so the strategy can respond faster.
  • When volatility is very high, use only the latest month’s performance.

tl;dr: it kind of works – beats the NIFTY 50 after 25bps of costs post-COVID.

However, with an on-going 30% drawdown, it is not exactly self-recommending. Having said that, its pre-COVID results might be a better indicator of how the strategy manages to side-step large shocks.

It does a semi-decent job of switching between different market-cap indices.

The full-period metrics (2009 – 2026) are promising. Seems to hold its own against buy & hold – lower drawdowns with similar(ish) returns.

While 25bps of costs should cover transaction costs, the problem is surviving capital gains taxes require a much higher returns.

Details of the approach, descriptions of the strategies and code are on github.

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Skewness Enhanced Momentum

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).

We ran a backtest that “enhanced” basic momentum with skew predictions and liquidity improvements.

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.

Code and charts are on github.

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Liquidity Improvements and Momentum

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.

Code and charts are on github.

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Building Winning Portfolios with SPDR Sector ETFs

A few years ago, we had a look at using momentum to rotate through SPDR Sector ETFs. We concluded that just buying & holding the S&P 500 ETF was a winning idea. However, after looking at industry rotation in Indian stocks, we checked if using the same technique on SPDR Sector ETFs made sense.

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.

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