Invest Without Emotions
Investing insights to make you a better investor.
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.
Code and charts on github.
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.
Code and charts on github.
We had looked at using VIX for driving equity index positioning about eight years ago and had abandoned the idea having found no relationship between VIX levels and future returns (Part I, Part II). We found some modest success in using realized volatility to position towards a target volatility (Volatility Targeting). However, the idea of using VIX as a predictor of future equity returns refuses to die.
The correct time to take more equity risk is when VIX has been high for six months but has been trending down. The correct time to take less equity risk is when VIX has been low for six months but has been trending up. The target equity weight is then proportional to the target equity risk divided by VIX. Therefore, at most times, low VIX corresponds to high equity weight and high VIX to low equity weight.
Thanks to AI, we can now test this hypothesis out without wasting too much time. We got Claude to give us the long-only and long-short outlines and backtest plans (lo, ls). And then we got hermes + deepseek to execute this in R. The tl;dr is that inverse-volatility long-only is “good enough.”
The approach sounded good on the face of it but doesn’t bear scrutiny once you run the numbers through it.
Code and charts on github.
Keller, Wouter J., Relative and Absolute Momentum in Times of Rising/Low Yields: Bold Asset Allocation (BAA) (July 18, 2022, SSRN) builds on PAA, VAA and DAA following the same philosophy. The differences between them and some of the weaknesses of BAA is summarized here.
The author swapped out SPY (the S&P 500 ETF) with QQQ (the NASDAQ 100 ETF) to juice returns. We added the original back into our backtest for completeness. Also, we ran a parallel backtest with SPHB (High-Beta ETF) instead of EEM in the canary assets like before.
The results are just as disappointing as they were before.
As much as you might hate 60/40, at least there is no model risk.
Not to belabor the point but, practically speaking, investors are better off managing drawdowns through asset allocation (hence giving up the upside during booms) or through using simple trend-following to avoid steep drawdowns (hence incurring higher transaction costs due to whipsaws) than trying to construct a Rube Goldberg machine.
Code and charts on github.
Keller, Wouter J. and Keuning, Jan Willem, Protective Asset Allocation (PAA): A Simple Momentum-Based Alternative for Term Deposits (April 5, 2016, SSRN) successfully protects your portfolio from returns.
This paper, superseded their VAA and DAA papers. The key differences between them can be found here. The paper used the Emerging Market ETF – EEM – to be one of the canary assets. While EM equities are indeed risky, a risk-off in them doesn’t really mean a risk-off in DM equities. So, we ran a parallel backtest with SPHB (High-Beta ETF) instead of EEM.
Here too, the out-of-sample performance leaves much to be desired.
Once again, investors are better off managing drawdowns through asset allocation (hence giving up the upside during booms) or through using simple trend-following to avoid steep drawdowns (hence incurring higher transaction costs due to whipsaws) than trying to construct a Rube Goldberg machine.
Code and charts are on github.